Machine_Learning/Heart-Disease-detection-using-ML/end-to-end-heart-diease-project.ipynb

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   "source": [
    "# Predicting Heart Disease using Machine Learning\n",
    "\n",
    "This notebook looks into using various python based Machine Learning and Data Science libraries in an attempt to build a ML model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27d3b185-d443-477b-bd7b-9e10969cb435",
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   "source": [
    "## Approach\n",
    "\n",
    "1. Problem definition\n",
    "2. Data\n",
    "3. Evaluation\n",
    "4. Features\n",
    "5. Modelling\n",
    "6. Experimentation"
   ]
  },
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   "id": "c0badb9a-86a0-4ac5-a03f-02764acf61f2",
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   "source": [
    "## 1. Problem definition\n",
    "\n",
    "Given deta parameter about a patient can we predict weather or not they have heart disease\n",
    "\n",
    "## 2. Data\n",
    "\n",
    "organized data come from claveland UCI ML Repository\n",
    "\n",
    "## 3. Evaluation\n",
    "\n",
    "if we can reach 95% accuracy at predicting weather or not a patient has heart disease or not during the proof of concept\n",
    "\n",
    "## 4. Features\n",
    "\n",
    "This is where you'll get different information about each of the features in your data. You can do this via doing your own research (such as looking at the links above) or by talking to a subject matter expert (someone who knows about the dataset).\n",
    "\n",
    "**Create data dictionary**\n",
    "\n",
    "1. age - age in years\n",
    "2. sex - (1 = male; 0 = female)\n",
    "3. cp - chest pain type\n",
    "    0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "    1: Atypical angina: chest pain not related to heart\n",
    "    2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "    3: Asymptomatic: chest pain not showing signs of disease\n",
    "4. trestbps - resting blood pressure (in mm Hg on admission to the hospital) anything above 130-140 is typically cause for concern\n",
    "5. chol - serum cholestoral in mg/dl\n",
    "    serum = LDL + HDL + .2 * triglycerides\n",
    "    above 200 is cause for concern\n",
    "6. fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n",
    "    '>126' mg/dL signals diabetes\n",
    "7. restecg - resting electrocardiographic results\n",
    "    0: Nothing to note\n",
    "    1: ST-T Wave abnormality\n",
    "        can range from mild symptoms to severe problems\n",
    "        signals non-normal heart beat\n",
    "    2: Possible or definite left ventricular hypertrophy\n",
    "        Enlarged heart's main pumping chamber\n",
    "8. thalach - maximum heart rate achieved\n",
    "9. exang - exercise induced angina (1 = yes; 0 = no)\n",
    "10. oldpeak - ST depression induced by exercise relative to rest looks at stress of heart during excercise unhealthy heart will     stress more\n",
    "11. slope - the slope of the peak exercise ST segment\n",
    "    0: Upsloping: better heart rate with excercise (uncommon)\n",
    "    1: Flatsloping: minimal change (typical healthy heart)\n",
    "    2: Downslopins: signs of unhealthy heart\n",
    "12. ca - number of major vessels (0-3) colored by flourosopy\n",
    "    colored vessel means the doctor can see the blood passing through\n",
    "    the more blood movement the better (no clots)\n",
    "13. thal - thalium stress result\n",
    "    1,3: normal\n",
    "    6: fixed defect: used to be defect but ok now\n",
    "    7: reversable defect: no proper blood movement when excercising\n",
    "14. target - have disease or not (1=yes, 0=no) (= the predicted attribute)"
   ]
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   "metadata": {},
   "source": [
    "## Preparing the tools\n",
    "\n",
    "we're going to use pandas,matplotlib,numpy for data analytics"
   ]
  },
  {
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    "#import all the tools we use\n",
    "\n",
    "#regular EDA and plotting library\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "#models from scikit learn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "#model evaluation\n",
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV,GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "from sklearn.metrics import precision_score,recall_score,f1_score\n",
    "from sklearn.metrics import RocCurveDisplay"
   ]
  },
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   "source": [
    "## Load data"
   ]
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       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "0        0   0     1       1  \n",
       "1        0   0     2       1  \n",
       "2        2   0     2       1  \n",
       "3        2   0     2       1  \n",
       "4        2   0     2       1  \n",
       "..     ...  ..   ...     ...  \n",
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   "source": [
    "df=pd.read_csv(\"C:/Users/nanda/Downloads/heart-disease (1).csv\")\n",
    "df"
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       "(303, 14)"
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    "df.shape"
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    "## data explorarion\n",
    "\n",
    "The goal is to find out more about data and become subject matter expert on data set you ipport\n",
    "\n",
    "1. What question(s) are you trying to solve?\n",
    "2. What kind of data do we have and how do we treat different types?\n",
    "3. What's missing from the data and how do you deal with it?\n",
    "4. Where are the outliers and why should you care about them?\n",
    "5. How can you add, change or remove features to get more out of your data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "03264f44-653c-4c05-a864-e1d5b3e9f105",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3e358730-87e0-4fb2-91dc-fb5428680aec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "1    165\n",
       "0    138\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets find out hoew many of each class are there\n",
    "df[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "25f8a1bc-f717-443a-a3e6-a8e481112936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"target\"].value_counts().plot(kind=\"bar\",color=[\"salmon\",\"lightblue\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "576e49ea-0227-4642-84aa-f5c02cb7b377",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d461a86b-9b9c-4acd-bc21-e60b80b0757e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# are there any missing data\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "96179b1a-824d-4044-b853-209a11e32326",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d48d24c2-19b1-4863-b3b0-c934620e8171",
   "metadata": {},
   "source": [
    "## Heart Disease frequency according to sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2bdb6c9b-fdf0-4d84-bb5a-c38ec556a75f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "1    207\n",
       "0     96\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dedf5ac9-bb2e-4d45-acc7-f9eed00114bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex      0    1\n",
       "target         \n",
       "0       24  114\n",
       "1       72   93"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#compare target column with sex column\n",
    "pd.crosstab(df.target,df.sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "348f55fa-4cf2-4892-8cda-a256fd951faa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a plot\n",
    "pd.crosstab(df.target,df.sex).plot(kind=\"bar\",figsize=(10,6),color=[\"salmon\",\"lightblue\"])\n",
    "plt.title(\"Heart Disease frequency for sex\")\n",
    "plt.xlabel(\"0=No disease,1=disease\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"Female\",\"Male\"])\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d699f266-3c7f-4e31-9bb5-fa7f4963dd8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "thalach\n",
       "162    11\n",
       "160     9\n",
       "163     9\n",
       "152     8\n",
       "173     8\n",
       "       ..\n",
       "202     1\n",
       "184     1\n",
       "121     1\n",
       "192     1\n",
       "90      1\n",
       "Name: count, Length: 91, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"thalach\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98d7aa48-fbc5-461f-9bb5-7306dbe7e3d6",
   "metadata": {},
   "source": [
    "## Age vs Max heart rate from heart disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2706b385-c7d1-4c3b-9cfb-63c3311f80b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kye7H33rrLcXvKYe/2xgAKisrcfnll6NDhw74+uuvce+99+Lee+9F37590adPH03aM2LECLzzzjtwOBw+31PLc1SL8uVytqO3z/R33YnIWNiRIqKw0LdvX9x00024/vrrsW7dOvTv3x/x8fEoKSnBqlWr0L17d9x6663o2rUrTjvtNNx7770QQiAlJQVLlizBsmXLZH1e9+7dAQCzZ8/GRRddBLPZ7LVD0apVK0ybNg0zZszAjTfeiMsvvxz79u3Dww8/3GLMq7y8HIMGDcL48ePRtWtXJCYmYu3atVi6dKm7Y5GQkIC5c+diwoQJOHr0KMaOHYv09HQcOnQImzdvxqFDhzB//nwA9V/oFixYgK5duyI/Px/r16/Hv/71L2RnZ3t87siRI5GXl4fevXsjLS0Ne/fuxdNPP4327dsjNzcXAPDMM8+gX79++NOf/oRbb70VHTp0QEVFBXbu3IklS5Y0qa7W0JAhQxATE4OrrroKd999N6qrqzF//nwcO3bMY7mUlBTceeedmDVrFlq1aoVLL70UxcXFeOSRR5CZmQmT6Y+hvlOmTMH//vc/9O/fH3fccQfy8/PhdDpRVFSEL774AlOnTvX6JVar46KhoUOHon///rj77rtRWVmJ3r1747vvvsPrr7+u+D3l8HcbA8Att9yCoqIi/Pjjj4iPj8cTTzyBNWvW4Morr8TGjRs97lYqdeWVV+LNN9/ExRdfjNtvvx3nnnsuoqOjUVxcjK+//hqjR4/GpZdeioKCArRq1Qq33HILpk+fjujoaLz55pvYvHmzrM/r3r07Fi9ejPnz5+Pss8+GyWRqNibcEjnb0dtn+rvuRGQwela6ICLyxlW1r3EFNhdv1bFeeeUV0adPHxEfHy+sVqs47bTTxLXXXivWrVvnXmbbtm1iyJAhIjExUbRq1UpcfvnloqioqEklNVd1tkOHDjX5nJqaGnHjjTeKtLQ0IUmSACB2797tdX2cTqeYNWuWyMnJETExMSI/P18sWbJEDBgwwGfVvurqanHLLbeI/Px8YbPZhNVqFV26dBHTp08XlZWVHp+xcuVKcckll4iUlBQRHR0t2rZtKy655BLx3//+173MsWPHxMSJE0V6erqIi4sT/fr1E99++22TdjzxxBOioKBAtG7dWsTExIh27dqJiRMnij179nh85u7du8UNN9wg2rZtK6Kjo0VaWpooKCgQM2bM8LotXJYsWSJ69OghLBaLaNu2rbjrrrvcle6+/vprj203Y8YMkZ2d7d52H3/8sejRo0eTSojHjx8XDz74oOjSpYuIiYkRSUlJonv37uKOO+4QBw8e9NketceF65hteByUlZWJG264QSQnJ4u4uDgxZMgQ8fPPP8uq2tdw/zX8nMbnRnPt8mcbv/TSS00qRQohxM6dO4XNZhNjxozx2c4JEyaI+Pj4Jo+72tPQyZMnxb///W93mxISEkTXrl3FzTffLHbs2OFebvXq1eL8888XcXFxIi0tTdx4441iw4YNTdrp7bOFqK/4OHbsWJGcnOw+R31p3769uOSSS5p9zt9j1ddn+rvuRGQckhDNTMZCREQUwnbv3o2uXbti+vTpuP/++/VuDhERRSB2pIiIKKRt3rwZb7/9NgoKCmCz2fDLL79gzpw5sNvt2Lp1q9dKjURERIHEMVJERBTS4uPjsW7dOrz88ssoKytDUlISBg4ciMcee4ydKCIi0g3vSBEREREREclkankRIiIiIiIiaogdKSIiIiIiIpnYkSIiIiIiIpKJxSYAOJ1OHDhwAImJie4ZzImIiIiIKPIIIVBRUYGsrCyPid8bY0cKwIEDB5CTk6N3M4iIiIiIKETs27cP2dnZXp9nRwpAYmIigPqNZbPZdG4NERERERHpxW63Iycnx91H8IYdKcAd57PZbOxIERERERFRi0N+WGyCiIiIiIhIJnakiIiIiIiIZGJHioiIiIiISCaOkSIiIiKiiCWEQF1dHRwOh95NoSAxm82IiopSPe0RO1JEREREFJFqa2tRUlKCEydO6N0UCrK4uDhkZmYiJiZG8XuwI0VEREREEcfpdGL37t0wm83IyspCTEyM6jsUFPqEEKitrcWhQ4ewe/du5Obm+px01xd2pIiIiIgo4tTW1sLpdCInJwdxcXF6N4eCyGq1Ijo6Gnv37kVtbS0sFoui92GxCSIiIiKKWErvRpCxabHfeeQQERERERHJxI4UERERERGRTOxIERERERGFIUmS8MEHH+jdjLDFjhQRERERkYFcd911kCQJkiQhOjoabdq0wZAhQ/DKK6/A6XS6lyspKcFFF12kY0vDGztSREREREQqCKcTzj074dyyAc49OyEadGYCZfjw4SgpKcGePXvw2WefYdCgQbj99tsxYsQI1NXVAQAyMjIQGxsb8LZEKnakQogeJyERERERKefcXoi6Z2bAsXA+HIvfhGPhfNQ9MwPO7YUB/dzY2FhkZGSgbdu26NWrF+6//358+OGH+Oyzz7BgwQIAntG+2tpa3HbbbcjMzITFYkGHDh0wa9Ys9/uVl5fjpptuQnp6Omw2Gy644AJs3rzZ/fxvv/2G0aNHo02bNkhISMA555yDL7/80qNN8+bNQ25uLiwWC9q0aYOxY8e6nxNCYM6cOejUqROsVit69OiB9957L3AbKAg4j1SIcG4vhGPpB4C9/I8HbUkwDx8DU7d83dpFRERERM1zbi+EY9HCpk/Yy+sfHzchqN/jLrjgAvTo0QOLFy/GjTfe6PHcs88+i48++giLFi1Cu3btsG/fPuzbtw9AfSfnkksuQUpKCj799FMkJSXhhRdewIUXXohff/0VKSkpOH78OC6++GLMmDEDFosFCxcuxMiRI/HLL7+gXbt2WLduHSZPnozXX38dBQUFOHr0KL799lv35z/44INYvHgx5s+fj9zcXHzzzTe4+uqrkZaWhgEDBgRtG2mJHakQEGonIRERERH5JpzO+h/BfXAs/RBSlzxIQZyrqmvXrigsbHo3rKioCLm5uejXrx8kSUL79u3dz3399dfYsmULSktL3VHAf//73/jggw/w3nvv4aabbkKPHj3Qo0cP92tmzJiB999/Hx999BFuu+02FBUVIT4+HiNGjEBiYiLat2+Pnj17AgAqKyvx5JNPYvny5Tj//PMBAJ06dcKqVavwwgsvsCNFyoTqSUhERERE3omiXZ5JoubYyyCKdkHq0Dk4jUL93SVJkpo8ft1112HIkCHo0qULhg8fjhEjRmDo0KEAgPXr1+P48eNITU31eE1VVRV+++03APWdoUceeQQff/wxDhw4gLq6OlRVVaGoqAgAMGTIELRv3x6dOnXC8OHDMXz4cFx66aWIi4vDtm3bUF1djSFDhni8f21trbuzZUTsSOksVE9CIiIiIvKhwq7tchrZvn07Onbs2OTxXr16Yffu3fjss8/w5ZdfYty4cRg8eDDee+89OJ1OZGZmYsWKFU1el5ycDAC466678Pnnn+Pf//43OnfuDKvVirFjx6K2thYAkJiYiA0bNmDFihX44osv8NBDD+Hhhx/G2rVr3ZUEP/nkE7Rt29bj/Y1cDIMdKb2F6ElIRERERD4k2rRdTgPLly/Hli1bcMcddzT7vM1mwxVXXIErrrgCY8eOxfDhw3H06FH06tULBw8eRFRUFDp06NDsa7/99ltcd911uPTSSwEAx48fx549ezyWiYqKwuDBgzF48GBMnz4dycnJWL58OYYMGYLY2FgUFRUZNsbXHHak9BaCJyERERER+Sa16wTYknwni2zJ9csFQE1NDQ4ePAiHw4Hff/8dS5cuxaxZszBixAhce+21TZZ/6qmnkJmZibPOOgsmkwn//e9/kZGRgeTkZAwePBjnn38+xowZg9mzZ6NLly44cOAAPv30U4wZMwa9e/dG586dsXjxYowcORKSJOEf//iHx5xVH3/8MXbt2oX+/fujVatW+PTTT+F0OtGlSxckJiZi2rRpuOOOO+B0OtGvXz/Y7XasXr0aCQkJmDBhQkC2UaDpOuhm1qxZOOecc5CYmIj09HSMGTMGv/zyi8cyQgg8/PDDyMrKgtVqxcCBA/HTTz95LFNTU4O///3vaN26NeLj4zFq1CgUFxcHc1UUc5+EvgTwJCQiIiIi+SSTCebhY3wuYx4+OmBj3JcuXYrMzEx06NABw4cPx9dff41nn30WH374Icxmc5PlExISMHv2bPTu3RvnnHMO9uzZg08//RQmkwmSJOHTTz9F//79ccMNN+D000/HlVdeiT179qBNmzYA6jtirVq1QkFBAUaOHIlhw4ahV69e7vdPTk7G4sWLccEFF6Bbt254/vnn8fbbb+PMM88EADz66KN46KGHMGvWLHTr1g3Dhg3DkiVLmo0hGoUkhBB6ffjw4cNx5ZVX4pxzzkFdXR0eeOABbNmyBdu2bUN8fDwAYPbs2XjsscewYMECnH766ZgxYwa++eYb/PLLL0hMTAQA3HrrrViyZAkWLFiA1NRUTJ06FUePHsX69eubPZAas9vtSEpKQnl5OWy24N/58Vq17xQzq/YRERERaaq6uhq7d+9Gx44dYbFYFL9P81PYJMM8fDS/v4UwX/vf376Brh2pxg4dOoT09HSsXLkS/fv3hxACWVlZmDJlCu655x4A9Xef2rRpg9mzZ+Pmm29GeXk50tLS8Prrr+OKK64AABw4cAA5OTn49NNPMWzYsBY/V++OFMCTkIiIiCiYtOpIAfVVmEXRrvox7Yk2SO06sdpyiNOiIxVSY6TKy+s7ESkpKQCA3bt34+DBg+7SjEB9ZY8BAwZg9erVuPnmm7F+/XqcPHnSY5msrCzk5eVh9erVzXakampqUFNT4/7bbte/kIOpWz6kLnk8CYmIiIgMRjKZWF05AoXMt3QhBO68807069cPeXl5AICDBw8CgDub6dKmTRv3cwcPHkRMTAxatWrldZnGZs2ahaSkJPd/OTk5Wq+OIpLJBFOHzjB17wVTh87sRBERERERhaiQ+aZ+2223obCwEG+//XaT5xpPKuZtojF/l7nvvvtQXl7u/m/fvn3KG05ERERERBEnJDpSf//73/HRRx/h66+/RnZ2tvvxjIwMAGhyZ6m0tNR9lyojIwO1tbU4duyY12Uai42Nhc1m8/iPiIiIiIjIX7p2pIQQuO2227B48WIsX768SfnDjh07IiMjA8uWLXM/Vltbi5UrV6KgoAAAcPbZZyM6OtpjmZKSEmzdutW9DBERERERkZZ0LTbxt7/9DW+99RY+/PBDJCYmuu88JSUlwWq1QpIkTJkyBTNnzkRubi5yc3Mxc+ZMxMXFYfz48e5lJ06ciKlTpyI1NRUpKSmYNm0aunfvjsGDB+u5ekREREREFKZ07UjNnz8fADBw4ECPx1999VVcd911AIC7774bVVVVmDRpEo4dO4Y+ffrgiy++cM8hBdRPEBYVFYVx48ahqqoKF154IRYsWODXHFJERERERERyhdQ8UnoJhXmkiIiIiCh4tJxHioxHi3mkQqLYBBERERERGcvAgQMxZcoUvZuhG3akiIiIiIgM5LrrroMkSXj88cc9Hv/ggw9anCKoJQsWLIAkSZAkCWazGa1atUKfPn3wz3/+E+Xl5R7LLl68GI8++qiqzzMydqSIiIiIiFQQQuDQiRrss1fh0IkaBGPkjMViwezZs5tMAaQFm82GkpISFBcXY/Xq1bjpppvw2muv4ayzzsKBAwfcy6WkpHjULYg07EgRERERESm0v6IKS3eV4tt9R7G2pAzf7juKpbtKsb+iKqCfO3jwYGRkZGDWrFk+l/vf//6HM888E7GxsejQoQOeeOKJFt9bkiRkZGQgMzMT3bp1w8SJE7F69WocP34cd999t3u5xtG+efPmITc3FxaLBW3atMHYsWPdzwkhMGfOHHTq1AlWqxU9evTAe++9537e4XBg4sSJ6NixI6xWK7p06YJnnnnGo10rVqzAueeei/j4eCQnJ6Nv377Yu3ev+/klS5bg7LPPhsViQadOnfDII4+grq6uxfVVSteqfUREREYnhMDhqlpU1zlhiTKhtTVGdbSGiIxhf0UVfjhQ1uTxqjonfjhQhj5ZQNtEa0A+22w2Y+bMmRg/fjwmT56M7OzsJsusX78e48aNw8MPP4wrrrgCq1evxqRJk5CamuqukO2v9PR0/OUvf8Err7wCh8PRpDr2unXrMHnyZLz++usoKCjA0aNH8e2337qff/DBB7F48WLMnz8fubm5+Oabb3D11VcjLS0NAwYMgNPpRHZ2NhYtWoTWrVu774RlZmZi3LhxqKurw5gxY/DXv/4Vb7/9Nmpra/Hjjz+6r7eff/45rr76ajz77LP405/+hN9++w033XQTAGD69Okyt65/2JEiIiJSaH9FFQpL7aiqc7ofs0aZkJ9uC9iXJyIKDUIIFJbafS5TWGpHVoIlYD+uXHrppTjrrLMwffp0vPzyy02ef/LJJ3HhhRfiH//4BwDg9NNPx7Zt2/Cvf/1LdkcKALp27YqKigocOXIE6enpHs8VFRUhPj4eI0aMQGJiItq3b4+ePXsCACorK/Hkk09i+fLlOP/88wEAnTp1wqpVq/DCCy9gwIABiI6OxiOPPOJ+v44dO2L16tVYtGgRxo0bB7vdjvLycowYMQKnnXYaAKBbt27u5R977DHce++9mDBhgvv9H330Udx9990B60gx2kdERKSA65fohp0o4I9fogMd6yEifR2uqm1y/jdWVefE4aragLZj9uzZWLhwIbZt29bkue3bt6Nv374ej/Xt2xc7duyAw+GQ/VmusV/NdQyHDBmC9u3bo1OnTrjmmmvw5ptv4sSJEwCAbdu2obq6GkOGDEFCQoL7v9deew2//fab+z2ef/559O7dG2lpaUhISMBLL72EoqIiAPXjsa677joMGzYMI0eOxDPPPIOSkhL3a9evX49//vOfHu//17/+FSUlJe52aI0dKSLSnXA64dyzE84tG+DcsxPC6fsfJiK9+ftLNKdqJApf1S10ouQup1T//v0xbNgw3H///U2eE0I06fSouS5t374dNpsNqampTZ5LTEzEhg0b8PbbbyMzMxMPPfQQevTogbKyMjhP/bv+ySefYNOmTe7/tm3b5h4ntWjRItxxxx244YYb8MUXX2DTpk24/vrrUVv7R0f01VdfxZo1a1BQUIB3330Xp59+Or7//nsAgNPpxCOPPOLx/lu2bMGOHTsCNk8Yo31EpCvn9kI4ln4A2BuUVLUlwTx8DEzd8nVrF5Evcn6JTouLDVKriCiYLFH+3Y/wdzk1Hn/8cZx11lk4/fTTPR4/44wzsGrVKo/HVq9ejdNPP73JGKeWlJaW4q233sKYMWNgMjW/TlFRURg8eDAGDx6M6dOnIzk5GcuXL8eQIUMQGxuLoqIiDBgwoNnXfvvttygoKMCkSZPcjzW8W+XSs2dP9OzZE/fddx/OP/98vPXWWzjvvPPQq1cv/PLLL+jcubOs9VKDHSki0o1zeyEcixY2fcJeXv/4uAnsTFFICpVfoolIP62tMbBGmXz+qGI9VYAm0Lp3746//OUvmDt3rsfjU6dOxTnnnINHH30UV1xxBdasWYPnnnsO8+bN8/l+QggcPHgQQgiUlZVhzZo1mDlzJpKSkprMXeXy8ccfY9euXejfvz9atWqFTz/9FE6nE126dEFiYiKmTZuGO+64A06nE/369YPdbsfq1auRkJCACRMmoHPnznjttdfw+eefo2PHjnj99dexdu1adOzYEQCwe/duvPjiixg1ahSysrLwyy+/4Ndff8W1114LAHjooYcwYsQI5OTk4PLLL4fJZEJhYSG2bNmCGTNmaLCVm2JHioh0IZzO+jtRPjiWfgipSx4kL798EekllH6JJiJ9SJKE/HRbs1X7XPLTbUGr4vnoo49i0aJFHo/16tULixYtwkMPPYRHH30UmZmZ+Oc//9lioQm73Y7MzExIkgSbzYYuXbpgwoQJuP3222Gz2Zp9TXJyMhYvXoyHH34Y1dXVyM3Nxdtvv40zzzzT3b709HTMmjULu3btQnJyMnr16uWOJN5yyy3YtGkTrrjiCkiShKuuugqTJk3CZ599BgCIi4vDzz//jIULF+LIkSPIzMzEbbfdhptvvhkAMGzYMHz88cf45z//iTlz5iA6Ohpdu3bFjTfeqGaz+iQJBrhht9uRlJSE8vJyrwcHEWnLuWcnHAvnt7icecKtMHUI3m16In8IIbB0V2mLv0QP75TOUuhEIaq6uhq7d+9Gx44dVY2hYfVOY/K1//3tG/COFBHpo8L3QH3ZyxEFUaj9Ek1E+mmbaEVWgoXzyUUgdqSISB+Jft799Xc5oiBrm2hFnyzwl2gigiRJLCwTgdiRIsMRTidE0a76OxWJNkjtOnEMjQFJ7ToBtiTPan2N2ZLrlyMKUfwlmogocrEjRYbCUtnhQzKZYB4+pvmqfaeYh49mJ5lCHn+JJiKKTPyGQobhLpXd+A7GqVLZzu2F+jSMFDN1y4d53IT6O1MN2ZJhZulzIiIKAtZdi0xa7HfekSJDYKns8GXqlg+pSx7jmkREFFTR0dEAgBMnTsBq5ZjGSHPixAkAfxwHSrAjRYYginb5HksDAPYyiKJdkFgq23Akk4n7jYiIgspsNiM5ORmlpaUA6ucp4vjG8CeEwIkTJ1BaWork5GSYzWbF78WOFBkDS2UTERGRxjIyMgDA3ZmiyJGcnOze/0qxI0XGwFLZROQHVvUkIjkkSUJmZibS09Nx8uRJvZtDQRIdHa3qTpQLO1JkCCyVTUQtYVVPIlLKbDZr8sWaIgt/piNDcJXK9oWlsokiF6t6EhFRsPFbJxkGS2UTUXP8reopnM7gNIiIiCICo31kKCyVTUSNsaonERHpgR0pMhyWyiYiD6zqSUREOuDP+EREZGys6klERDrgHSkiikgskx0+WNWTiIj0wI4UEUUclskOL66qno5FC70uw6qeRESkNf6rQkQRhWWywxOrehIRUbDxjhRRgDFCFjr8LZMtdcnjPjIgVvUkIqJgYkeKKIAYIQstLJMd/ljVk4iIgoU/0xEFCCNkIYhlsomIiEgjvCNFFACMkIWoCC2TbbR4qdHaS0REkYkdKaIAYIQsNEVimWyjxUuN1l4iIopc/ImPKBAYIQtJrjLZvoRTmWyjxUuN1l4iIops4fFtgSjURGiEzAi0KJMtnE449+yEc8sGOPfshHA6A9Ra5fyNl4ZK243WXiIiIkb7iAIgEiNkRqKmTLZRomdGi5carb1ERES8I0UUAJEWITMiyWSCqUNnmLr3gqlDZ/87UUaJnhktXmq09hIRUcTjtziiANEiQkahw3DRM6PFSzVqrxFil0REFB4Y7SMKIDURMgotRoueGS1eqkV7jRK7JCKi8MBvc0QBpiRCRiHIYNEzo8VL1bbXULFLIiIKC6HxLygRUagzWlQOxouXKm2v4WKXREQUFhjtIyLyg9Gici5Gi5cqaa/RYpdERBQe2JEiIvKDK3rmWLTQ6zKhFJVrSDKZDNWBkN1eg8UuiYgoPITev/hERCHKaFG5iGHA2CURERkf70hRxBBOp2HiTQAghMDhqlpU1zlhiTKhtTUGkiT591od1tVo21cpo0XlIoFRY5eRJFKuD0QUWdiRoohgtLLI+yuqUFhqR1XdH4PjrVEm5Kfb0DbR6vO1eqyr0bavWkaLyoU7I8cuI0GkXR+IKHLwXxUKe0Yri7y/ogo/HCjz6EQBQFWdEz8cKMP+iiqvr9VjXY22fSk8MXYZmnh9IKJwxjtSFNb8LYssdckLiV+rhRAoLPU9IL6w1I6sBEuTmJ9W6yonUhgK25eRIXJh7DK0hML1gYgokNiRorBmtLLIh6tqm9yJaqyqzonDVbVIi4v1eFyLdZUbKdR7+zIyRI0xdhk69L4+EBEFGn8CovBmsLLI1S10onwup3JdFUUKddy+jAwRhTiDXX+JiORiR4rCm8HKIlui/Dslm11Oxbr6GykUQmj2mWr4GxkSTu8dU+F0wrlnJ5xbNsC5Z6fPZY0uktaVQojBrr9ERHLp2pH65ptvMHLkSGRlZUGSJHzwwQcezx8/fhy33XYbsrOzYbVa0a1bN8yfP99jmZqaGvz9739H69atER8fj1GjRqG4uDiIa0GhzF0W2ZcQKovc2hoDawudKeupcUuNqVlXOZFCrT5TDTmRoeY4txei7pkZcCycD8fiN+FYOB91z8wIy7tYkbSuFFqMdv0lIpJL145UZWUlevTogeeee67Z5++44w4sXboUb7zxBrZv34477rgDf//73/Hhhx+6l5kyZQref/99vPPOO1i1ahWOHz+OESNGwOFwBGs1KIS5yiL7EkplkSVJQn66719n89NtzRZ/ULOuSiOFum1fFZGhSIoERtK6Uugx2vWXiEguXa9eF110EWbMmIHLLrus2efXrFmDCRMmYODAgejQoQNuuukm9OjRA+vWrQMAlJeX4+WXX8YTTzyBwYMHo2fPnnjjjTewZcsWfPnll8FcFQphRiuL3DbRij5ZyU3uTFmjTOiTlexzHin3ujaOyiQm+VxXNZHCPz4ziNtXYWRIi0igXuTG84y8rhQ+jHb91QKjtESRI6Sr9vXr1w8fffQRbrjhBmRlZWHFihX49ddf8cwzzwAA1q9fj5MnT2Lo0KHu12RlZSEvLw+rV6/GsGHDmn3fmpoa1NTUuP+22znQNdwZrSxy20QrshIsfpchb8rf5eq5IoW+4n3eIoV/aDR+SgTuy4M7MuQr3tdMZMioVcSUVCc06rpS+DHa9VcNVhIliiwhfRV79tlnccYZZyA7OxsxMTEYPnw45s2bh379+gEADh48iJiYGLRq1crjdW3atMHBgwe9vu+sWbOQlJTk/i8nJyeg60GhQTKZYOrQGabuvWDq0Dnk/xGXJAlpcbHIsVmRFhfrVyfKHeWqaPQFusJ3lEtNpPCPz2z0g0SFPWDxMcWRIQNWEVMczzPgulL4Mtr1VwlGaYkiT0hfyZ599ll8//33+Oijj7B+/Xo88cQTmDRpUouxPSGEzy+d9913H8rLy93/7du3T+umEwWd2iiXkkihnvExJZEhEZ/o13v7u1ygqdq+rJhGFDSM0hJFppCN9lVVVeH+++/H+++/j0suuQQAkJ+fj02bNuHf//43Bg8ejIyMDNTW1uLYsWMed6VKS0tRUFDg9b1jY2MRGxvr9XkiI9IiytU20YrMuBgc2rsXNVXViLVakNa+PUxmc8A+EwCcdXUQ676DOHoEUkoqpN59YYpq+fIkPzIkvDyudLnAUrN9lcYfjU44nRERIYuU9TQKRmmJIlPIdqROnjyJkydPwtToHwaz2QznqV90zj77bERHR2PZsmUYN24cAKCkpARbt27FnDlzgt5mIl1pEOVy5ftTGnwhcNiSAG/5fg0+07FsCZxrVgKn5qgSAPDFEojzB8A8ZGSLby2ZTH5/MZEqj2u6XMCp2L6u+KNj0UKvLwu3immRMj4lUtbTUBilJYpIuv4Levz4cWzatAmbNm0CAOzevRubNm1CUVERbDYbBgwYgLvuugsrVqzA7t27sWDBArz22mu49NJLAQBJSUmYOHEipk6diq+++gobN27E1Vdfje7du2Pw4ME6rhmRDlRGuRTl+1V+pmPZEjhXr3B3otyEgHP1CjiWLfHv/f1ltLibyvZGUsW0SBmfEinraThGu7YQkSZ0vSO1bt06DBo0yP33nXfeCQCYMGECFixYgHfeeQf33Xcf/vKXv+Do0aNo3749HnvsMdxyyy3u1zz11FOIiorCuHHjUFVVhQsvvBALFiyA2UsUiShcqYly+Zvvl7rkedzBUPOZzrq6+jtRPjjXrIQ06CK/Yn7+aNheIUk4kpaNGks8YqsrkXqoGJIQIRV30yKeFwkV05Qev0YTKetpRJEapSWKdLp2pAYOHAjR+JfoBjIyMvDqq6/6fA+LxYK5c+di7ty5WjePyFDURLmU5vtVfea675reiWqykKhf7rwBvpfzk6u9xatXY2uvC1Ad90dRCcuJCuRtWI7sgoKQ+RKqVTxPTvzRiCJlfEqkrKcRRWKUlohCvGofEcmjOMqlIt+v9DPF0SN+faS/y/mrJDsX6/qOQrU1wePxamsC1vUdhZLsXE0/T61IiucpFinjUyJlPQ2K5ypR5AnZYhNEpIyiKJcGY3HkfqaUkupXbTwpJdW/tvlBCIHCUjvQ3PQIpx4rLLUjK8EiY/LjwIuEeJ4qkTI+JVLW08B4rhJFFnakiMKQ3CiXFvl+2Z/Zuy/wxRLf8T5Jql9OI4eralFV53sel6o6Jw5X1SItLrSmSAj3eJ4akTI+JVLW0+h4rhJFDv5EQkTufL8vWuf7TVFRMJ3ve+yT6fwBmhWaAIDqFjpRcpej0KDH8auHSFlPIiKj4NWWiADok+83DxkJU8HAplE7SYKpYKBf80jJYYny75LX0nLC6YRzz044t2yAc89OCGdod7yM1l4lImV8SqSsJxGREUjCV9m8CGG325GUlITy8nLYbMyWU2QTTmfQ8/3OujqIdd9BHD0CKSUVUu++mt6JchFCYOmuUlSddDQ/TkoIWKPNGN4p3esYKaNNhmq09qqlx/Grh0hZTyIiPfjbN2BHCuxIEUWS4u0/40fp1HnesLN06lJ4rrAju1vXZl/rngzVi1C7I2C09hIREYUCf/sG/PmKDEdpTCkS4k3km3A60WbpIvT+7iNYqo57PGc5UYHe332ENkv/2+yx4e9kqKFyXBmtvRQ8TocDv+/ahaKftuH3XbvgdDj0bhJpQAiBQydqsM9ehUMnanzO00lE2mDVPjIUpTGlSIs3UfNcE5pm2suRsX8njqRlo8YSj9jqSqQeKoZ06otHcxOaGm0yVKO1l4KjePvPKKwx18+jFmUFTgKWLbuQH+vweieWQt/+iioUlto9qpJao0zIT7ehbaJVx5YRhTfekSLDcMeUGn85tJfDsWghnNsLNX0dhaEGE5VKQqB16T60LfoZrUv3uTtRjZfz+VgLn6Ero7WXAs4Va622xHs8Xm2Jx4+SDcXbf9apZaTG/ooq/HCgrMnUDlV1TvxwoAz7K6p0ahlR+GNHigxBaUyJ8SbyoGZCU6NNhmq09lJAOR0OFNaY6/9opkomABTWmBnzMxj3JOM+FJbaGfMjChB2pMgQ5MSUtHhdKHDW1cHx/UrUfboYju9XwllXp3eTAiZY49fcE5r64mVCUzWv1YPR2tuQHmM9wn0M5aG9e+vjfF6qUUKSUG1NwKG9e32+D8fhhBY5k4wTkfY4RoqMQWlMyaDxJseyJXCuWemuJCcA4IslEOcP0HxuJb0Fc/yaa0JTn5XsvExoqua1ejBae130GOsRCWMoa6qq68dE+bOcFxyHE3o4yTiRvkLrX1Aib5TGlAwYb3IsWwLn6hXuTpSbEHCuXgHHsiW6tCsQjDZ+zWiToRqtvXqM9TDaMahUrNWiajmOwwlNWk0yTkTK8I4UGYI7puQrptdMTEnp6/TirKurvxPla5k1KyENuiggE9aqIXdSXX/Hr0ld8jS7a6LFZ5q65UPqkqd4MtRgT6Sqtr3B4u9Yj6wEi9fJkgF521ePY1AvrXNyYNm623u8TwhYTlSgdfem10Kt9k2kCOY53toaA2uUyWe8zxplQmtrTEA+nxNDU6QLrW9iRF4ojSkZLd4k1n3X9E5Uk4VE/XLnDQhOo/ygJIqoR3lurT5TMpkUtUmvCJnS9gaTnLEeaXGxzT4vd/tGUol4af9e5G1YjnV9R9Wfp81MRp238WtIKWag0bpqsW8iRbDPcUmSkJ9uww8Hyrwuk59uC0gHNxIisUQtCY1vj0R+UBpTMlK8SRw9oulywaA4iqjH+DUdx8xFSoRMKbVjPRRtX4OOoVSkwo7M4h0+J6POLN7R7LpyHI5/9DrH2yZa0ScrGdZG8T1rlAl9spIDMn6N1zOierwjRYaiNKZklHiTlJIKf2pgSSmpAW+LP1RFETUavyYrWqLTmDkjR8iCFd1RM9ZD8fY14BhKxU6tQ2bxDp+TUTe3rrEm/+5m+LucXEaIj+l9jrdNtCIrwYLDVbWornPCcirOF4g7UXqvK1EoYUeKDEdpTMkI8Sapd1/giyW+432SVL9cCFATRdRi/JrcaIleY+aMGiELZnRHzVgPpdvXaGMo1Wi4rq7JqJvwsq6ph4phOeFscXxV6qFKIEHb49co8bFQOMclSQpKtDIU1pUoVPCnAqIQYoqKgul832OfTOcPCJlCE2qiiK7xa774Gr+mJFqi9jMVM2CELNjRHddYD1+8jvVQuH11Ox50oGZdpeN2tN27vf6PZiK8ANC26GdIx7U9fg0VHzPgOa5YJK0rUQuM/68DUYiTO9GnechImAoGNv3lV5JgKhgYUvNI+Rsx9Lac0vFr/kZLmtvW7s9sUio/ye8xc7InbzVYhEzN9lVD8VgPFdtXizGURpnMV/H5lmDD/vbd6v9o5roEAPvbdYVI0O74bXgMCknC4fQc7G/XFYfTcyBOfWYgjkHFDHaOqxJJ60rUgtD4WZsoTCmNpZiHjIQ06CJZ5cT1oEUUUcn4NW2iJcrGDijZp0aLkOkZ3VEy1kPt9lUzhtIo0TMXJet6JC0b1bXHvL+pJKE63oYjaa2QrlE7XcdgSXYutva6ANVxie7nLCcqkLdhOTKLd4RMfMxo57gakbSuRC3hHSmiAFEbSzFFRcF83gBEXXwZzOeFTpyvIa2iiJLJBFOHzjB17wVTh84tf4FVES1x75eKRvulouX9onSfGi5CpnN0xzXWI8dmRVpcbIsD5rXYvrKPQRgsetaA3HWtcfpTAsf/5fxSYUdJdi7W9R1VPzargWprAtb1HYWS7NyQiY8Z7hxXIZLWlaglPMqJAkCvaJQedIkiKoyWqNkvavepkcrwGzG6E+ztG0nnuJqKikqJBBu29rqg/g8vccKtPQdpGidUy1DnuEqRtK5EvoTeT9xEYSDSqhoFO4qoNFqiZr9osU8NU4bfoNEdU7d8iNO6Qny5BOLIYUiprSENHglTTNNKf2rpfY4HsyS4moqKSmkVJxRCBKUkuIupWz5w+pk4tHcvaqqqEWu1IK19e5jM5oB9pl6Mcj0jCiR2pIgCIQKrGpmiopqUOA8UV7TEsWih12WajZao2S8a7VNDlOFXun115li2pH5es1Nj9sSuXyHWrQHOH6D9nVG9J3cO4rgsV0XFHw6UeV3Ga0VFhbSIE+6vqEJhqd2jA2iNMiE/3RaQSWo9P9MKRFmBk4B1z+GAfqaejHA9Iwqk0PpXkChcGDAaZTSKoiVq9kuE7VOjRXccy5bAuXpFs+W5natXwLFsibYfqNPxoNe4LMUVFRVSGyfcX1GFHw6UNbmLVlXnxA8HyrC/okp1G0PhM4lIX7wjRRQARo1GqeGsqwt6lUHZ0ZLsDvXjK1qoMojsDk0fNvA+VRpvMkp0x1lXV38nCvWlso+kZaPGEo/Y6kqkHiqGJASca1ZCGnSRZsekHseDv+OypC55vqteKowFKqmoqJSqCZqFQGGp7zuBhaV2ZCVYNGu7Hp9JRPpjR4ooAIwajVKqSaQKAL5YAhGISFUjsqIlxXt8d6KA+ueL9wCN3tOo+1RtvMkI0R2x7jtAiJZLZa/7TrP4qR7HgxbjstTGAl0VFQNNTZzwcFWtzw4YUH+X6HBVrWbrosdnEpH+QutffKIwYrRolFJBj1SpoXJci9H2aaREjcTRI36VyhZHj2j6uUE/HlQev0Yr1640TljdQodG7nJavpeWn0lE+uMdKaIAMko0SqmGkSqvy2gcqVJFg3EtRtmnERU1SknF1sy8+v9vrlS2ENjacxCyag9q/tGmbvlA7hnBibWqOH61igUGm5I4oR7l2vX4TCLSXwh8syEKb0aIRinlilT5XkhoGqlSQ6txLUbYp5EUNTpy5jmoPuBjn7pKZefmoI3Gn904KicAYM1KIAAV9NQcv3qXa1dDbpxQj3LtenwmEemPP40QkWL+RqW0jlQp5RrX4ksojnNSIpKiRrXCvztq/i7nr2BH5VQdvxE0JYNrfJUvWpdr1+MziUh/xv+2QES6kVJSNV0uGIw2zkkpI0eNhNMJ556dcG7ZAOeenRBO3509PdbV36hcS22Xu66Kj98IK98f7HLten0mEemL0T4iUkzq3Rf4YkmL5cSl3n2D1yg/GGWckxpGjRopqSqnx7rqWUFPyfFr5PL9SgWzXLuen0lE+gmfbw1EFHSmqCiYzvc99sl0/oDQKDTRiGQywdShM0zde8HUoXNYdaKA+qhRdqLF5zLZiaFVaEJpVE6XWJXOFfTkHr+RFGttyDW+KsdmRVpcbFCOdz0+k4j0EV5XTCIKOvOQkTAVDGy2WpqpYGDA55GSG42KFEIIFFdU+1ymuKIaoqViIUGiNioX9FhVECroaX0saxFrdToc+H3XLhT9tA2/79oFp8OhaRsp+IQQOHSiBvvsVTh0oiZkrglERhB6PxMTkeGYh4yENOii4JSAbkDt5KLhzGhV+7SIygUzVmXUCnpqYq3F239GYY25fq6uKCtwErBs2YX8WAeyu3XVtJ0UHGon7CaKdOxIEZEmTFFRQS1x7o5GNXYqGoUwKhyhhOGq9mlUVU5uqWylJJMJprye9ZNRe2HKOyskK+gpKd9fvP1n/CjZgEZp0WpLPH4EgO0/szNlMK4JuxtzTdjdJwvsTBG1gNE+IjIcvaJRRmK4qn0GqyonnE44t270uYxz66bmj0GDravT4UBhjbn+j+YmPAZQWGNmzM9A/J2wmzE/It9C5F9QIiL/yYlGRSpXJTtfQqlqnzsq50sIVZVTcwzqva5yx8Qc2ru3Ps7nLSIpSai2JuDQ3r3eP5NjGUOKnOgvEXnHaB8RGU8ETS6qlKuSXXPRHZdQmiDUVVWu2bjmKSFVVU7FMajnuioZE1NTVV0/JqoFNVXNFzfhWMbQY7joL1GICpF/kYiIZDBYNEovRpsg1FCTJas8BvVYV9eYmMZ3IlxjYvZXVDX7ulir7zL6vpZTW+adAsNw0V+iEMU7UkSkCeF0Bm2C20icXFQptZXslO5Xp8OBQ3v3oqaqGrFWC9Lat4fJbG7xdUaZLFmLY1DNusrdL/6OiclKaDq3WFr79rBs2YVqS3zz8T4hYKmuRFpnz3X1dyyj1CUv5PYvENxrWrBpNYm1EIKTD1NEY0eKiFQLdnTHcDEwnSmtZKd0v6otk62kqlywaXUMKllXJftFTTl8k9mM/FhHfXU+ITw7U6fGV+XHOpp0lPUs865WuMcRtYj+snQ6EaN9RKSSXtEdQ8XADEjpfnWVya62xHs8Xm2Jx4+SDcXbfw5UkyOC0v2idkxMdreuOFfYYamu9HjcUl2Jc4W9+Q6yQccyRkocUU30V2lMlCjc8I4UESmmVXRHaYTG1C0fyD0j6BMBhzul+9VdJtuC5stkC4HCGjOyHE3vXmglWHEsPWJraj4z1uRf3MrXctnduiLz5Ekc3rwRVSeqYI2zonWPnjBHRzf/ggbjw5wmE3Z3PgsnEpIRd7wMHXdugslVuS+ExjIaPY4ol5Lor5qYqJGFc9QzFBg1JspvG0SkmBbRHTURmsavFQCwZiUQJvEbvSjdr+4y2d40KJPdppP249eCGcfSI7am5jNTDxXDcsLpvYy5ELCcqEDqoUogwfu56lz6AVIatMH57VJIXravaxzZto498FuX3kCDL53bzhqI035ZhzN2F4bUWEYjxxGVkhv9VRMTNapwj3rqzcgxUXaliUg5ldEdNRGaSInf6ELhfvVW/roxf5eTI+jHgx6xNTUl14/bkbdhef0fjeeNOvV33savIR3X7lyVTCZsHz4ev3U9p9k7lL91PQfbh18VWr/qGzSOGEyRVjqd/9YEltFjoiF09SIiw1FRAtrfCE1zE3eqeS21TMQnKlouNta/X5/9Xc5fDY8HIUk4nJ6D/e264nB6DsSpL/CaHw8aleCXM1Gt0v3iakdm8Q70/u4jWKqOezxlOVGB3t99hMziHZqeq06nEztNp8bKNRf1BLDTFA9nAM9TuZMPc2qFlkVS6XT+WxNY/sZEWzxvdcRoHxEppqYEtJoITSTGb4LL33+0PJdLlZywnKhoOT6WpO0XLNfxUJKdi629LkB13B8dCcuJCuRtWI7M4h2aHg9alD+XHxdStl8atjezeAcy9u/EkbRs1FjiEVtdidRDxZCE0Pxc/a3sxKkP9zLO4dTjv5WdQG6Kj0ioQkriQpxaoWValU43Av5bE1jhEBM1/s8FRKQbVwloX7yWgFYToWH8JqCkyuMtL9TMcqZTnRYAPuNjphMVqtvoocKOkuxcrOs7qskYrWprAtb1HYWS7FxNjwdVxz4URuUU7pfG7ZWEQOvSfWhb9DNal+6r70T5aq/C863ypMOvl/m7nBxK40Jq92skcJVO96Wl0umGwX9rAiocYqKReyUgIk0oLkOuJhYY79+v1/4uF0xyoly6UbpvVMTH1BAJNmztdUH9H14iZFt7DoJI8P65SvaL+9hPlHfsK44LqYydaXGueotONve5cX5Gu1pazulw4Pddu1D00zb8vmsXnA7fHS+1cSHXdhK2ZM91tbXi1AqnuEqnN47v+VM63VAY9QyocIiJ6hrt++abb/Cvf/0L69evR0lJCd5//32MGTPGY5nt27fjnnvuwcqVK+F0OnHmmWdi0aJFaNeuHQCgpqYG06ZNw9tvv42qqipceOGFmDdvHrKzs3VYI6LIZOqWD6lLnqzSsOoiNP7+0hlav4gapfKT0n2jJj6mxpG0bFTXHvO+gCShOt6GI2mtkN7M0+r3S+O7b747YUrjQlrEztScqyW2dO/RSfuhJp/bqfx3bHVa6zuzXqKeEAKdyn8HUpsf/6Vkcmct4kIl2bnYPOomj1/CLVEm9Ei3oa3Pd44cmcU7kLb0QxyxJP5xnldXIGr4aCCErmdqMOoZWOEQE9W1i1dZWYkePXrgueeea/b53377Df369UPXrl2xYsUKbN68Gf/4xz9gsVjcy0yZMgXvv/8+3nnnHaxatQrHjx/HiBEj4GjhFysi0pZkMsHUoTNM3XvB1KFzi9EXNREaqdK/aJi/ywWDkSo/Kd03quJjKtQ4/Rs71NxymlSObBzrqbD7fq3CuJBWsTMl5+rvw8f5jE7+PvzyJu9jqrTjtF/W1f/hJep52i/rYKpsfnsondxZbVzIFQts/Hy1QaqIBYPr2JfsZZ7nub0s5K5najDqGVjhEBPVdc9fdNFFmDFjBi677LJmn3/ggQdw8cUXY86cOejZsyc6deqESy65BOnp9b8plpeX4+WXX8YTTzyBwYMHo2fPnnjjjTewZcsWfPnll8FcFSJSQI9YoB6MWPlJ6b5RvE9ViDX7Odlso+V0qxyp4vjVY/sKIbAlplX9H16ik1tiWjWNyiXacMbmb3Daz2ub7Uid9vNanLH5m2bX0z25s4/PLKwxNxvzUxMXCocqYoFmxOuZGnqcc5HEFRO1GjQmGrJV+5xOJz755BPcfffdGDZsGDZu3IiOHTvivvvuc8f/1q9fj5MnT2Lo0KHu12VlZSEvLw+rV6/GsGHDmn3vmpoa1NTUuP+22zlIkEgvwY8FBp9RKz8p2Teu1yH3DIh130EcPQIpJRVS774wRYXWPzl6VY5Ue/wq3S/utjudsl7rjsr5qL7XXFTOtZ5nbP4GXbZ+h596DEBlYivEVxzDmZtXwuxweF1PNZM7q4kLhUMVsUAz6vVMDbXnHPnWNtGKrAQLDlfVorrOCcup8zOU70S5hNa/ag2Ulpbi+PHjePzxxzFjxgzMnj0bS5cuxWWXXYavv/4aAwYMwMGDBxETE4NWrVp5vLZNmzY4ePCg1/eeNWsWHnnkkUCvAhH5STKZZP2D64pbOBYt9LpMSMUtDFz5Se6+AZqOORIAsGYlEKCxYDUOP6N9jZfTqXKkFsevkv0CKBsPpjQq51rP4tWrPcZWHc7siN+zc5G3YTmyCwqaXc+aqur6MVEtaG5yZ1dc6IcDZV5f5y0uFA5VxALOwNczNZSec+QfSZIM+eNEiHzLaMo1Qd/o0aNxxx134KyzzsK9996LESNG4Pnnn/f5WiGEz17sfffdh/Lycvd/+/bt07TtRBR4hopbGCyKqIYeY8EUR7nU7Be9KuipoHTfqInK+VWWvhmxVkuzj/u7nNK4UDhUEQu4CLqeEbUkZO9ItW7dGlFRUTjjjDM8Hu/WrRtWrVoFAMjIyEBtbS2OHTvmcVeqtLQUBQUFXt87NjYWsbHG6/UShTIhRNBvyxslbmG0KKJS/o6dkLrkabqPlEa5VO2X7A71UTdfY2UkqX45L4J5/KrZN0q3r3u8UXPXAdc4p1I7shIsTa4Vae3bw7JlV32hCW+TO1dXIq2z93NGSVxI7ypiaq6jwboGR8r1jMgfofVto4GYmBicc845+OWXXzwe//XXX9G+fXsAwNlnn43o6GgsW7bM/XxJSQm2bt3qsyNFRNraX1GFpbtK8e2+o1hbUoZv9x3F0l2lQaluJbcCmR4ipfKTnLETWnJXfjpVTtuzUfWPNRflUrVfivf47kS5Prt4j++2B+n4VbNvlFbWkjPeqDGT2Yz82FOFJLxU/MuPdcBkNvt8f1dcKMdmRVpcbIsdCz2riKm5jgbzGhwp1zMif+h6lB8/fhybNm3Cpk2bAAC7d+/Gpk2bUFRUBAC466678O677+Kll17Czp078dxzz2HJkiWYNGkSACApKQkTJ07E1KlT8dVXX2Hjxo24+uqr0b17dwwePFiv1SKKKK5SwY2/MFWxVLAHQ0URldJx7IRfEwE3Q/F+Mdo4EZXtVRKVUzveKLtbV5wr7LBUV3o8bqmuxLnC7nUeKbX0qCKm5jqqxzU4Iq5nRH7QNdq3bt06DBo0yP33nXfeCQCYMGECFixYgEsvvRTPP/88Zs2ahcmTJ6NLly743//+h379+rlf89RTTyEqKgrjxo1zT8i7YMECmFv4lYqI1PO3VHBz0Z1IZJQoolIivvlJVeUuJ7eqnCu2lmkv9zoRsGPpIa+RQiX7Rat1DRoNxrXIjcppMd4ou1tXZNbV4bc9xaisPYn4mGicltcR5gBXgGybaEVmXAwO7d2LmqpqxFotSGvfvsU7YEqouY7qeQ0O9+sZkT907UgNHDiwxbkYbrjhBtxwww1en7dYLJg7dy7mzp2rdfOIqAUsFSxfeFd+8nduHe/LKakq1zC25poIuIkWyjHL3y/q1zWYtBrXIqeylnu80UmH13FO1mizz/FG+yuqUFhqR5UjFjDHAg7gt71HkJ9uC+j8Mq7jMKXB9nLYkgJSeVLNdVTva3B4X8+IWsafDYhIMZYKpoakyuMtL+RjOcUV/3SI2ald12DTY1yLJEnoXnus/g8v45y61x7zeqdEr9hwsCtPqrmO8hpMpC92pChiCCFw6EQN9tmrcOhETUTPTK8VlgqWTzidcO7ZCeeWDXDu2Qnh9O8LjtLXBZWK+Ji/VeWaXe8G7yckCYfTc7C/XVccTs+BaPglXctyzAYsAa3FuBY511HhdKLN0kU+x661WfrfZvepv5E1ra/jqo5DhdRcR2NN/sX1/F2OiOQJ2fLnRFpyx0Ma/CpnjTIFPB4S7vQuFWw0SmJral4XbGriY3KqyjWOErk+t8SW7jHxK1D/hT1vw3Jk2g9pWo7ZqCWg1YxrkXsdde1TX2PXXMs13qd6RdbUHIdKqbmOph4qhuWEs36eLm9l4k9UIPVQJZDACB6R1vgzMYU9VpULHD1LBRuN0riQHhPcKqUqPqYinieZTPh9+DifE7/+PvxybWNrJhNMeT19LmPKOyskB94rKbmu6DraYF+5xq61LfoZrUv3uTtRjZdz0S2ypkdMVMV1VDpuR96G5fV/eIlP5m38GtLxEKkeSRRmQu8KT6QhveIhkUSPUsFGozQupEfMSC3F8TE1sUAhsCXm1KTsjb9snvp7S0wr3zE0mdFf4XTCuXWjz2WcWze1uG+MENlUfB1VsU91iw3rFNl0XUcbr0+L19FEm3+l/0MoYkoUThjto7Cmd0WjSCG3LHKkURoX0iNmpAUl8TE1UTn3ee7teJMkn+e5kuivFvvGKJFNpddRNftUr9iwnpHNzOIdSFv6IY5YEv+IQFZXIGr4aMDL8eBqb2bxDu/xyRCMmBKFC96RorDGikbB4yqLnGOzIi0ulp2ohpTGhYw26WsDcuNjamKBas5zxdFflfvGSJFNpdtXzT6VJAnZiRafr81O1H5uJD2qGwJ/HA+SvcwzAmkv83k8NGyvt/hkINpLRPV4ZlFYY1U5CglK40IGrAynhtJYoNLzXFX0V68KhTpQcx1Vuk+FECiuqPb5ecUV1QGJZWtR3VAOtcdDsNtLRH9gtI/CGqvKUShQGhcyamU4NUzd8oHcMyDWfQdx9AiklFRIvfvCFOX9nyul57ma6K9eFQr1oPY6qiTqqXcs29QtHzj9TBzauxc1VdWItVqQ1r49TGZzi68VQsiKOWtxPKipxkhEyrEjRWHNVQ3phwNlXpdhVTkKNFf8xrFooddlmovfKH2dkTUeNyQAYM1KwMe4IaXnuZpIoKp9Y7DIpnv77j/meuCPJ0/dEWrpOiqZTLI6hXrHsv8YN2cFoqzAScC653CLU2YommpDo+NB7jYmIvXC519fIi9YVY5CgdL4TSTFdtSMG1JynquN/upRoVAvflWG05CesWyl4+YUj7cz4PFARPV4R4oiAqvKBZ5wOg0VK5Ebv9GC0vhNJMR2/B0nInXJ87recs9zLaK/wa5Q6BLM8821b3xNrOtYesjnvpFLr1i2v+PmshI8C10ofR0AILtD/V0+X+O9JKl+OSIKKexIUcRwVZUj7RmljLOLoviNRpTGb8I9tqPVuCE557lW0V+5+0ZtZDPY51vDfeOqDNeExmO69IplKx2bpWpMV/Ee350ooP754j1AGF8DiIwofH7OJCJdGKmMM6AifkOBpdO4Ib2iv0pjgbqcbxG0b5SOzVI1pstgY+aI6A+8I0UUhoIV+9EijhVMquI3Dd9HxfZV+lo9oohBpeM4kbaJVmTGxSiq0AYo36dyY4G6nW8NtrmQpOYnfW20nFaCHctWOjZL1ZguncdIGS2WTRRK2JEiCjPBjP0YrYyzFiWV1Wxfpa/VM4oYLHqWenftl5QGn+2wJfmsFNj4tUrPNzmxQL3ON9e+KbGlY2uvC1Adl+h+znKiAnkbliPTfihgZfiDGctWOjZLzZiuUDj2jRLLJgo1/MmBKIwEPfZjsEiK2pLKarav0tdGShTRNW7Il0CUetdjnyqm0/kmmUz4ffg4rOs7CtXWBI/nqq0JWNd3FH4ffnlY3MVwjc3ypbmxWUpfBxjz2Ceiesa/6hERAP9jP8Kp4bwrBivbqyZ+o2b7Kn2tv1FE0dJAdYMIdql3PfapKjqdb0IIbIlpVf9H447Aqb+3xLQKm+NQ6dgsNWO6tDj2hdMJ556dcG7ZAOeenT6PPV2O32ba4G97tXgdUSAw2kcUJvSI/egZSVFCTfxGzfZV+lotoohGE8xS73rsUzX0Ot/cx6G3cUmSFHbHodKxWWrGdKk59uVG9PSOZSuNFDKKSKFG0b9MdXV1+PLLL/HCCy+goqICAHDgwAEcP368hVcSUcDoEPvRK5KilJr4jartq/C1aqOIRiWZTDB16AxT914wdegcuONHh32qhl7nW8Qeh6fGZuXYrEiLi/W7wIXS1wHKjn1FET0dY9lKI4WMIlIokn1Hau/evRg+fDiKiopQU1ODIUOGIDExEXPmzEF1dTWef/75QLSTiFqiU+zH1C0fGDehmV8Jk2EePtrvSEqwqkbVx28gv3iDmu2r8LWqKoE1EPYV/5TSYZ+q5Trf6pZ+iCOWxD+q51UfR9TwUf6dbzKPB62Ow0ihx2TJvjRXyVHEJ/p4RYP3b2E5ucdSw/Z6qwDZbHsNViGWIofsjtTtt9+O3r17Y/PmzUhNTXU/fumll+LGG2/UtHFE5D89Y3bBjKRoQUn8Rs32VfpaNVFEl0io+KeUHvtUCyXZudg86iaPO0CWKBN6pNvQtoXXKjketDgOI4WekyV71WxEz9/xbN6XU3Isudpbkp3rvQJk8Y4m7dU7ikjkjexu+6pVq/Dggw8iJsbzgtm+fXvs379fs4YRkTx6x+yCFknRiNz4jZrtq/S1qqKIiJyKf0rpsU/Vcu3T5iaEbWmfKj0e1B6HkcJIkyVLlf4NxfC2nOJrS4UdJdm5PitAlmTnNl0vg1WIpcgh+wrvdDrhcDiaPF5cXIzERP9uFRNRYAS76pkaoVA1Si4121fpa5VWAou0in9K6bFPlVKzT9UeD2oq0kUC3a5nSiOmKqKpqo7DBBu29rqg/g8vFSC39hwEkaBde4kCSXa0b8iQIXj66afx4osvAqj/per48eOYPn06Lr74Ys0bSETyBLPqmRp6RzWUjmNQs32VvlZJFNHIFf+COcYE0GefKqFmn2pxPKipSBfu9J4sWW7EVE00Vc2xdCQtG9W1x7y/UJJQHW/DkbRWSNeovUSBJLsj9dRTT2HQoEE444wzUF1djfHjx2PHjh1o3bo13n777UC0kYhkkkym0M+J6101SsU4BjXbV+lrXVFEfxm10ppe5Y312KdyqdmnWh0Pco/DiKHjZMnm4WPqI4VeNBsbVvg6QN2xVOP07w544+XUtJcokGQfcVlZWdi0aRPuuusu3HzzzejZsycef/xxbNy4Eenp6S2/ARERoFtUI1JK6Bqx0lqk7Bul1OxTIx4PhqJj9ExpxFTp6/Q6Do0UXafIIfuO1DfffIOCggJcf/31uP76692P19XV4ZtvvkH//v01bSARhSc9ohpaldANdvRMCaNVWmN545ap2adGOx6MRu/omalbPpB7BsS67yCOHoGUkgqpd1+Yonx/zVMSTdXzODRKdJ0ih+yO1KBBg1BSUtLk7lN5eTkGDRrUbCEKIqLG9IhqaDGOQa/omVyuSms/HCjzukwoVVrTe8ycEajZp+7X7j/meuCPJ08VBQil48Fo9I6eNb4uCQBYsxLw47okN5qqyXGo4rpkiOg6RQzZZ7QQotkD/MiRI4iPj9ekUUQUGYIe1VA5jsFo0TNDVVpjeWO/qNmnmcU70Pu7j2Cp8ixpbTlRgd7ffYTM4h0BaXOk0Ct6psd1Sc1xaKjrElEL/L4jddlllwGo/zXhuuuuQ2zsH4NNHQ4HCgsLUVBQoH0LiSisBTWqoabkb4PomZAkHEnLRo0lHrHVlUg9VAxJCP9igUIEteqZYSqtaTTGRI/YZbA/U8k+dR2/mfZyZOzf6eX4PRTR0UktmLrlA6efiUN796KmqhqxVgvS2reHyWwOyOfpGYlVc20xzHWJqAV+d6SSkup/YRFCIDExEVbrH78YxMTE4LzzzsNf//pX7VtIRGEvWFENNeMYXNGzkuxcbO11Aarj/pg3z3KiAnkbliOzeIfP6Nn+iioUlto9xgdYo0zIT7cF9FdYI1Ra02KMiR6xS92qDMrcpw2jk5IQaF26r+lCER6d1MIf57gViLICJwHrnsMBO8f1jsSqubYY4bpE1BK/O1KvvvoqAKBDhw6YNm0aY3xEZDiqxjFU2FGSnYt1fUc1earamoB1fUeh93cfIdtL9Gx/RVWz4wKq6pz44UAZ+mQhoiMtaseYuONNjZ2KNyEA0So9PlMxRicDTpdznPuVSFey7/NOnz6dnSgiMiyl4xhEgg1be11Q/0fj+Mmpv7f2HASR0EwsUAgUlvr+IlNYaocQ/s2xEq4U7xs/403Cqd2cWXp8pipaRSeFwKETNdhnr8KhEzUhf8yqaa9wOuHcsxPOLRvg3LPT577U7RzXsey6Xox2DFJ4k121DwDee+89LFq0CEVFRaitrfV4bsOGDZo0jIgoUJSMyzqSlo3q2mPe31SSUB1vw5G0Vmg8o97hqlqf5X6B+l+tD1fVRnzURcm+0SPepHekSi4topN6RVOVUtNeuZFNvc5xvcuuB5vRjkEKf7LvSD377LO4/vrrkZ6ejo0bN+Lcc89Famoqdu3ahYsuuigQbSQi0pxkMsHUoTNM3XvB1KFziwOxa5z+/erZ3HLVLXzBkrtcuJO7b3SJNxksUuWKTvriKzrpiq017iy4Ymv7K6q0aqom1LRXSRU8vc5xtfvVSIx2DFJkkH1mzZs3Dy+++CKee+45xMTE4O6778ayZcswefJklJe38OscEZFBWaL8u1w2t5ya15If9Ig3GTAqpzg6qUFsTU5MTi017W1cnfNweg72t+uKw+k5EKcivM1FNvU8x/Uqux5MjEdTqJId7SsqKnKXObdaraioqAAAXHPNNTjvvPPw3HPPadtCIqIQ0NoaA2uUyWd8x3qqhK+Wr6WW6RFvMmpUTkl0Um1sLdiVDdW0V2l1Tr3P8aBOI6EDxqMpVMk+wzIyMnDkyBEAQPv27fH9998DAHbv3s1fAogobEmShPx033cX8tNtzc6Doua11DI94k1GjsrJjU6qia3pMVmsqphdg+qc1dYEz+VPVecsyc5tEtkMhXNcdiTWQBiPplAl+yy74IILsGTJEgDAxIkTcccdd2DIkCG44oorcOmll2reQCKiUNE20Yo+WcmwNornWKNM6JOV7PMugprXUsv0iDfpGZULJqWxNb0qG8aa/eusNLecmuqcPMcDh/FoClWyo30vvvginKcuerfccgtSUlKwatUqjBw5ErfccovmDSQiCgQhBA5X1aK6zgnLqciNP78Wt020IivBovi1mfGx+K3sBCpPOhAfbcZpyXEwhdEvx3rSI96kR1QOUH78KqE0tqZVZcNgrqua6pyA+nNcOJ1hG89TQ+/oJJE3sjpSdXV1eOyxx3DDDTcgJycHADBu3DiMGzcuII0jIgoEtWNTJElSlMNv7nN3Hqtk6V4NSSZT0MuNy/1MtTGlYI+tcsXWfth/zPXAH0+eumvWbGxNg8qGSta1xuFnhc1mllNTnRNQd44HeyyZkbiPwWYmPHZhPJr0IOtnjqioKPzrX/+Cw+EIVHuIiAJKr7EpLN1LLmpiSnodR5nFO9D7u49gqTru2cYTFej93UfILN7R9EUqKxsqXVe9KmwGu+R6pGF0kkKR7Gjf4MGDsWLFClx33XUBaA4RkTxyYj/+jk3JSrBo+sumFp8bSZEfPdY1mJ+pOCqn0fErNyrnGuuUaS9Hxv6dOJKWjRpLPGKrK5F6qBiSEHAsPQSpS57HNmtY2VBIUrOv81bZUM266lFhU017/R1L1nj7RiI10Wogsq6jFByyO1IXXXQR7rvvPmzduhVnn3024uPjPZ4fNWqUZo0jIvJFbuxHrxK6RisfrSc91jXYn6k0pqTF8askKtdwrJMkBFqX7mu6UDNjnVyVDYtXr/ZaSjy7oKDZL7Jq1lVNDEyPfaPVWLJIoTRaHUnXUQoe2R2pW2+9FQDw5JNPNnlOkiTG/ogoKFwxmsZcMZo+WWjyxVCvErqalI9u7FTkB2Ey4Sagz7rqtX3rY0qQ1anRYmyV3HMGgKqxTvWlxFs3beOpUuLmtq3Qtpm3UruuSravmteqLbnuF3+XoyYi6TpKwSW7I+UM4GzkRET+UBqj0auEbqDLR4dD5KfhunqLgWm9rnpvX7kxJTXHr6pYoMKxTu7PbG59Tj3m7TO1OFfVVtgM1r5RO5ZMT0aIyul9nlN4k92RIiLSm9IYjV4ldPUuH20ErnUtyc71GgPLLN6h6bqGwvaVE1NSc/yqiso1GOvkVTNjndR8plbnqtIYmNzXqmmv0u2rN6NE5ULhPKfwxa43ERmO0hiNa/yDL4Eooav4cyMp8lNhPxUDG4Vqa4LHU64YWEl2rrbrarDtK0kSshMtPpfJTmy+0ISa6JlrrJMv5uGjm/yar+ozVayrHtRcW5RuXz0Zqsqgwc5zMpbQOSuJiPykJkajVwldRZ9r4MiPXM64RGztdUH9H42/bJ76e2vPQXA2uFOlmsG2rxACxRXVPpcprqiGEE3nOFIblTN1y4d53IT6OycN2ZJh9jK+JNbsXyenueXUrKte1FxblGxfvfgblRMtDAURQuDQiRrss1fh0ImawO1Lg53nZCyM9hGR4aiN/agtoauU3M81auRHiSPChOq4OO8LSBKq4204Ik6gjUafabTtq3dUztQtH1KXvKCMidGrwqZaaq4twdy+amgRlQvmpNJGO8/JWELr7CQi8oMWET3X+IccmxVpcbFBiwjJ+VwjRn6Uqqmp0XQ5fxht+4ZCVE4ymWDq0Bmm7r1g6tDZ57apcfh3h6G55fSqsKkFNdcWOdtXNyqjcsGeVNpo5zkZi+yjxmw2o7S0tMnjR44cgdls1qRRREQtiZRZ7o0U+VEj1ur7S77c5fxlpO2rtmpfsKNyatqrJhZIAaYiKudv9UitY35GOs/JWGRH+7wd3DU1NYiJ0bbSFREpI4QIemxND3pF9ILNKJEfNdLat4dlyy5UW+KbL5ctBCzVlUjrrH38xijbV6+qfQ3JubboVSWzITXXQiOU9m4oWO1VE5XTM7JplPOcjMXvjtSzzz4LoP6W9X/+8x8kJPxRVcnhcOCbb75B165dZX34N998g3/9619Yv349SkpK8P7772PMmDHNLnvzzTfjxRdfxFNPPYUpU6a4H6+pqcG0adPw9ttvo6qqChdeeCHmzZuH7OxsWW0hChfBzJ6HAjXljY1EMpnCujSvyWxGfqwDPwKAEJ6dqVM/4OXHOmAKUPLBCNvXFWltblJdF2+RVi2icnKvLWraqyYWqLS9DRmltLdLMNvriso1O8HtKd6icnpHNo1wnpOx+N2ReuqppwDU/7rz/PPPe8T4YmJi0KFDBzz//POyPryyshI9evTA9ddfjz//+c9el/vggw/www8/ICsrq8lzU6ZMwZIlS/DOO+8gNTUVU6dOxYgRI7B+/XpGDSniuLLnjbmy532yEJadKQoP2d26Att/RmGN2aMEuqW6EvmxjvrnI1x9pBWyOwhqq/YF+9qiZ3vdpb0bO1XaGyEWBdOjvaZu+cC4Cc103pJhHj7a6+fpNSk6UaD43ZHavXs3AGDQoEF4//33kZycrPrDL7roIlx00UU+l9m/fz9uu+02fP7557jkkks8nisvL8fLL7+M119/HYMHDwYAvPHGG8jJycGXX36JYcOGqW4jkVH4mz3PSgiduVcoONREfoIdE83u1hVZDgcO7d2LmqpqxFotSOvcKWB3ooxISaRVTcxO6bVFzTVJj/YC/pf2lrrkhUQkTM/2KonKhULck0hLssZInTx5Env37sWBAwc06Ui1xOl04pprrsFdd92FM888s8nz69evx8mTJzF06FD3Y1lZWcjLy8Pq1au9dqRqamo8Kj/Z7ZyEjYzPqOWCKbDURH70iomazGa06cRSxL7IjbSqidkpvbaouSbp0V5Am9LewaR3e+VG5dTsV6JQJOvniejoaNTU1ATtAJ89ezaioqIwefLkZp8/ePAgYmJi0KpVK4/H27Rpg4MHD3p931mzZiEpKcn9X05OjqbtJtKD3tlzCj3uyE/jL1qnIj/O7YVeXxvsEsUUeEorXSq9tqi9JgW7vQBUl/YOOqO1F5FTcZUig+yqfX//+98xe/Zs/Oc//0FUVODm812/fj2eeeYZbNiwQXbHTQjh8zX33Xcf7rzzTvffdrudnSkyPGbPg8cI1bzURH4YEw1fSmKBSq8tWlyTgtleAKpKe+tCg/bqcT2LlIqrFP5k94R++OEHfPXVV/jiiy/QvXt3xMfHezy/ePFiTRr27bfforS0FO3atXM/5nA4MHXqVDz99NPYs2cPMjIyUFtbi2PHjnnclSotLUVBQYHX946NjUVsLKNNFF6YPQ8Oo1TzUhP5YUw0vMmNBSq9tmh1TQpWewF1pb31oLa9el7PIqXiKoU32T85JCcn489//jOGDRuGrKwsj4hcUlJSy2/gp2uuuQaFhYXYtGmT+7+srCzcdddd+PzzzwEAZ599NqKjo7Fs2TL360pKSrB161afHSmicOTKnvvC7Lk6aqJyQaci8sOYKDWk9Nqi1zVJkiRkJ/qeuDk7sfm7qa7S3r54K+2tBzXtNdT1jChEyb4j9eqrr2r24cePH8fOnTvdf+/evRubNm1CSkoK2rVrh9TUVI/lo6OjkZGRgS5dugAAkpKSMHHiREydOhWpqalISUnBtGnT0L17d3cVP6JIorQ0MrXMaNW81ER+GBOlxpReW/S4JgkhUFxR7XOZ4opq5KU134lTWtpbL0raa7jrGVGICtwgJz+sW7cOgwYNcv/tGrc0YcIELFiwwK/3eOqppxAVFYVx48a5J+RdsGAB55CiiMXseWDoXR1LLjWRH8ZEqTlKry3BviZpEU1VUtpbT3Lba7TrGVGoUtSReu+997Bo0SIUFRWhtrbW47kNGzb4/T4DBw6EEP7NXg4Ae/bsafKYxWLB3LlzMXfuXL/fhyjcMXseAAarjuWK/DQ7Uecp3iI/LFFM3ii9tgTzmqRVNFVuaW+9yWqvwa5nRKFK9k8rzz77LK6//nqkp6dj48aNOPfcc5Gamopdu3a1OLkuEZHWhBA4dKIG++xVOHSiRtaPM7IYrZoX6n+lNo+bUH9nqiFbMszjJviMKLFEMRlVrMm/Dr6/y8kVtGuSGga8nhGFItl3pObNm4cXX3wRV111FRYuXIi7774bnTp1wkMPPYSjR48Goo1ERM0K5oSxRqvm5aImosSYKBlR6qFiWE44UW1NAJo7VoWA5UQFUg9VAgna3nHSaxJruYx6PSMKNbLvSBUVFbkr4lmtVlRUVACor7L39ttva9s6IiIvgj1hrNGqeTUkmUwwdegMU/deMHXoLKuNrkhWjs2KtLhYdqIo5EnH7cjbsLz+j8Z3g079nbfxa0jHtY2tGWkSayNfz4hCiewzJCMjA0eOHAEAtG/fHt9//z2A+op7IXn7mojCjr8Txmp9TVITlSOiIEm0IbN4B3p/9xEsVcc9nrKcqEDv7z5CZvEOTWNrel2T1OD1jEg92dG+Cy64AEuWLEGvXr0wceJE3HHHHXjvvfewbt06XHbZZYFoIxGRBz0njDVaNS+iSOOKrWUW70DG/p04kpaNGks8YqsrkXqoGJIQmsfWjDqJNa9nROrI7ki9+OKLcDrrLxa33HILUlJSsGrVKowcORK33HKL5g0kImpM7wljjVbNiyiSNKxYKQmB1qX7miyjdWxN72uSGryeESknuyNlMplganDxGTduHMaNG6dpo4iIfOGEsUTkS7An1eU1iSgyKZpH6ttvv8ULL7yA3377De+99x7atm2L119/HR07dkS/fv20biMRkQdOGEtkLEKIoFd/DGZsjdckosgk+2ryv//9D8OGDYPVasXGjRtRU1MDAKioqMDMmTM1byARUWOuCWN94YSxRKFhf0UVlu4qxbf7jmJtSRm+3XcUS3eVBqWKnZqKlbI+h9ckoogk+4oyY8YMPP/883jppZcQHR3tfrygoAAbNmzQtHFERN5wwlii0GekkuBq8ZpEFHlkR/t++eUX9O/fv8njNpsNZWVlWrSJiMgvnDCWIpUeUTm5/C0JnpVgCbm2K6X2miScTlbQIzIQ2R2pzMxM7Ny5Ex06dPB4fNWqVejUiTNgE1FwuSaMJYoU+yuqUFhq97jLY40yIT/dFlJ3PYxaElwtpdck5/bCZopjJME8fAzndCIKUbJ/5rj55ptx++2344cffoAkSThw4ADefPNNTJs2DZMmTQpEG4mIiAjGisoZuSR4sDm3F8KxaKFnJwoA7OVwLFoI5/ZCfRpGRD7JviN19913o7y8HIMGDUJ1dTX69++P2NhYTJs2Dbfddlsg2khEBhBJkRQjxKrIGOQcS0aLymlVEtxo55vc9gqns/5OFAAhSc1OIOxY+iGkLnkBuaYabfsShRJF5c8fe+wxPPDAA9i2bRucTifOOOMMJCQkaN02IjKISIqkGCVWRaFP7rFktKicFiXBjXa+KWmvKNoF2MtRkp2Lrb0uQHVcovs5y4kK5G1YjsziHRBFuzSfONdo25co1Cj+aSMuLg69e/fGueeey04UUQSLpEiKkWJVFNqUHEtGi8qpLQlutPNNcXsr7CjJzsW6vqNQbfX8PlVtTcC6vqNQkp1bf7c/FNpLRG6y70hVVlbi8ccfx1dffYXS0lI4nZ4n4K5duzRrHBGFtoaRFG8CGUkJJqPFqih0KT2WtIrKqeF0OHBo717UVFUj1mpBWvv2MJnNXpevLwkO+XdpDHa+qWmvSLBha68L6v9ovC6SBAiBrT0HoW2C9+0czPbqLZJi5BT6ZHekbrzxRqxcuRLXXHMNMjMzQ+4EI6LgcUVSfLKXBSSSEmxGi1VR6FJ6LGkRlVOjePvPKKwx1981ibICJwHLll3Ij3Ugu1tXr69TUhLcaOebmvYeSctGde0x7y+UJFTH23AkrRXStWgsjLd9XSIpRk7GILsj9dlnn+GTTz5B3759A9EeIjISf6MmGkdS9GC0WBWFLqXHkisq98OBMq+v8RWVU6N4+8/4UbIBlkZttMTjRwDY/rPPzpTckuBGO9/UtLfGKfx6rb/LKW2HmuWCwR0jb+xUjBzjJrAzRUEn+15oq1atkJKSEoi2EJHRJPoe/yB7uRAWCrEqCg9qjqX6qFwyrI2es0aZ0CcrOSAFApwOBwprTsXKmoueASisMcPpcGj2mUY739S0N9bsX8fX3+WUtkPNcoHmb4xcOEOn40eRQfYdqUcffRQPPfQQFi5ciLi4uEC0iSggWOJVe1K7ToAtyXe8z5Zcv5zB6R2rovCh9lhSEpVT49DevU2KIHiQJFRbE3Bo71606aTNuW60843tDaxQiJFzbBY1x6+OVM+ePT0u0Dt37kSbNm3QoUMHREdHeyy7YcMGbVtIpAGWeA0MyWSCefiY5uMWp5iHjw6Lf2z0jFVReNHiWJIblVOjpqq6fkyUP8tpxGjnm5r21jj8jPb5uZw/jLZ99Y6Rc2wWeeNXR2rMmDEBbgZR4LhKvDbmKvHaJwvsTKlg6pYPjJvQzD8yyTAPHx1W/8gorUBGZGSxVgtw0s/lNGS0801pe/WK2Rlq++oYI+fYLPLFr47U9OnTA90OooAwcolXIzF1y4fUJS8iYg/BjlVR+DHadal1u3bAjt/rx0M11x4hACHql9OY0c43Je3VM2andvsGKzKvV4w8kqb4IGVkj5EiMhKjlng1IslkMnyJc38FM1ZF4cdo16WjtQ7A15fEUx2so7UOpEVp/7XCaOeb3PbqHbNTun2DGZnXK0YeCmOzKLSx+0xhzYglXokovBntumS09hqRHtUY1XBF5hv/IOCKzO+vqNL8M03d8mEeN6H+zlRDtmSYAxWvi6ApPkgZ3pGisGa0Eq9E4YJVMr0z2nXJaO01KqPEGPWMpgY9Rh5BU3yQMuxIUVgzWolXonDAKpm+Ge26ZLT2GpkRYox6R1ODGSOPpCk+SBnZXfiqKu+3a0tKSlQ1hkhrruy5LyFV4pXI4PSI/BiN0a5LRmsvBVYkRT1dY7N8CZcpPkgZ2Xu+Z8+ezc4V9d577yE/n+UfKfQYLXtOZFT+Rn6E0G4+HKMy2nXJaO2NREIIHDpRg332Khw6UROw8yzSop66jM0iw5Ad7RsyZAgKCgrw8MMP45577kFlZSVuu+02/Pe//8Xjjz8eiDYSqWaU7DmRkekd+TEao12XjNbeSBLMOG0kRj0jaYoPkkd2R2ru3Lm45JJLcP311+OTTz7BgQMHYLPZsHbtWpxxxhmBaCORJoyQPScyskiK/GjFaNclo7U3EgR70nm9y7XrJZKm+CD/KSo2MXToUFx22WWYP38+oqKisGTJEnaiiIjCkJzqe5EW+XExWoVC4XTyl/UwoVcFvfqoJ1hUhiKe7I7Ub7/9hvHjx+PgwYP4/PPPsXLlSowePRqTJ0/GY489hujo6EC0k4iIgkxuXCgSIz9Gq1Do3F4Ix9IPPKuQ2ZJgHj6GYz0MSM84LaOeRAqKTZx11lno2LEjNm/ejCFDhmDGjBlYvnw5Fi9ejHPPPTcQbSQioiBTUn1PkiRkJ1p8vm92ovZzy+jFaBUKndsL4Vi0sGkpZ3s5HIsWwrm9UJ+GkWJ6x2ldUc8cmxVpcbFhc24T+Ut2R2revHl45513kJyc7H6soKAAGzduRK9evbRsGxER6UBp9T0hBIorqn2+rriiOiyq9hmtQqFwOuvvRPngWPohhJPj14wkUuO0RKFCdrTvmmuuafbxxMREvPzyy6obRESRx2hjTMKd0riQVjEjI4zhMVqFQlG0y/ekogBgL4Mo2hWQAfV6nePhfm3RO05rtO2rpr1GuC5R8CkqNgEA27ZtQ1FREWpra92PSZKEkSNHatIwIooMRhtjEgmUxoW0iBkZZQyP3pEq2Sp83z2TvZwMep3jkXBt0bOCntG2r5r2GuW6RMEnuyO1a9cuXHrppdiyZQskSXLHFlwnqcPh0LaFRBS2gl22l/yjNC6kNmbkHsPT2KkxPAihyS8NF6lKtGm7nJ/0Oscj6dqiRwU9o21fNe010nWJgk/2Ff72229Hx44d8fvvvyMuLg4//fQTvvnmG/Tu3RsrVqwIQBOJKBwZbYxJJHHFhXxpLi6k9HWA8cbwqFlXPUjtOgG2JACAkCQcTs/B/nZdcTg9B8J1t8KWXL+cRvQ6xyPx2tI20YrhndLxp5wUnJOZjD/lpGB4p/SAdGaMtn3VtNdo1yUKPtl3pNasWYPly5cjLS0NJpMJJpMJ/fr1w6xZszB58mRs3LgxEO0kojBjtDEmkURpXEhNzEjvMTxyGW1SUslkgnn4GBSvXo2tvS5AdVyi+znLiQrkbViO7IICTcd86HWOR+q1JViTJRtt+6ppr9GuSxR8sq+YDocDCQkJAIDWrVvjwIEDAID27dvjl19+0bZ1RBS2DDfGJMLUx4WSm9x1sUaZ0Ccr2esv3Upfp+cYHqUUr6tOSrJzsa7vKFRbEzwer7YmYF3fUSjJztX08/Q6x3ltCSyjbV9V7TXgdYmCS/Ydqby8PBQWFqJTp07o06cP5syZg5iYGLz44ovo1Em7SAARhTfDjTGJQEon3FT0Op3G8KhllElJ3fGm5tp16rHCUjuyErSb50uvc5zXlsAy2vZV1V6DXpcoeGR3pB588EFUVlYCAGbMmIERI0bgT3/6E1JTU/Huu+9q3kAiCk96l+0l/yiNC8l9nXsMj68YjcZjeLQSrEiVGnrEsfQ6x3ltCSyjbV817TXydYmCQ/bPBcOGDcNll10GAOjUqRO2bduGw4cPo7S0FBdccIHmDSSi8OQaY+JLKI0xocCSTCaY8nr6XMaUdxbnbVFIjziWXuc4ry2BZbTtq6a9rrGFvpiHj+Z1KYJpsudTUlJC5oQhIuNwjTFpHKkI1TEmFDjC6YRza32xIm9V5ZxbN7E6lkKxZv/+jW5pOeF0wrlnJ5xbNsC5Z2eL+0OvcWRGG7/mInf76sVo21dNe03d8mEeN8Fd9dLNlgwzS59HPL+jfTfccINfy73yyiuKG0NEkSezeAfSln6II5ZE1FjiEVtdidTqCkQNHw3wH6iI4aqOVZKd67WqXGbxDlbH0pHSSUn1GkdmlPFrLkab9NVo21dNe03d8iF1yau/TlXYgUQbpHadeCeKIAk/C/2bTCa0b98ePXv29Dk3wPvvv69Z44LFbrcjKSkJ5eXlsNk4YJAoWLxOdHgKf+2LHM4tG1D8449Y13dU/QMNv9yc+jen93cfIfvcc2Hq3kuHFhrbPnsV1paUtbjcOZnJyLE1/XWe52pgcfsShRZ/+wZ+35G65ZZb8M4772DXrl244YYbcPXVVyMlJUWTxhJR5PF3okOpSx5/9YsAIsGGrb1OjbNt/AuxJAFCYGvPQWibYA5+48KAmsplPFcDi9uXAkE4nbyDFgR+b9F58+ahpKQE99xzD5YsWYKcnByMGzcOn3/+ecjMXk1ExiFnokMKf0fSsuvjfN5iNpKE6ngbjqRlB7dhYcJVucwXb5XLeK4GFrcvac25vRB1z8yAY+F8OBa/CcfC+ah7Zgac2wv1blrYkdU1jY2NxVVXXYVly5Zh27ZtOPPMMzFp0iS0b98ex48fD1QbiSgccaJDaqDG6d8Pcv4uR55UVVrjuRpY3L6kIXdMtHHn3F4Ox6KF7ExpTPE9PkmSIEkShBBwhmhVGSIKYZzokBow2iSfRqS4chnP1cDi9iWN+BsTDdVqkEYka0LempoaLF68GK+88gpWrVqFESNG4LnnnsPw4cNhYu6SiGTQaqJDIYRhqkYBxmtvsBhtkk+jUlK5jJOSBpbe25fXpPAhJybK6qfa8Lv3M2nSJGRmZmL27NkYMWIEiouL8d///hcXX3yx4k7UN998g5EjRyIrKwuSJOGDDz5wP3fy5Encc8896N69O+Lj45GVlYVrr70WBw4c8HiPmpoa/P3vf0fr1q0RHx+PUaNGobi4WFF7iCh4tJjocH9FFZbuKsW3+45ibUkZvt13FEt3lWJ/RZXGrdWG0dobTEab5NPIJElCWlwscmxWpMXFtrhNOSlpYOm5fXlNCjOMiQadrPLn7dq1Q8+ePX1edBcvXuz3h3/22Wf47rvv0KtXL/z5z3/G+++/jzFjxgAAysvLMXbsWPz1r39Fjx49cOzYMUyZMgV1dXVYt26d+z1uvfVWLFmyBAsWLEBqaiqmTp2Ko0ePYv369TCb/avuxPLnRPppfu6UZJiHj/ZZ7nd/RRV+OFDm9flQmxTSaO3Vy/6KKhSW2j3uTFmjTMhPt3H76EzpuUr+Cfb25TUp/Dj37IRj4fwWlzNPuBUm3pHyyd++gd8dqeuuu86vXwJfffVV/1vZsCGS5NGRas7atWtx7rnnYu/evWjXrh3Ky8uRlpaG119/HVdccQUA4MCBA8jJycGnn36KYcOG+fXZ7EgR6UtumVYhBJbuKm0xBja8U3pI3MEwWnv1xqhR6GJJ5cAK1vblNSk8CacTdc/MaDEmGnX7AzxvW6D5PFILFizQol2qlJeXQ5IkJCcnAwDWr1+PkydPYujQoe5lsrKykJeXh9WrV3vtSNXU1KCmpsb9t93OW5xEepJMJll57cNVtT6/AABAVZ0Th6tqkRYXq7Z5qhmtvXpzRc8o9Mg9V0meYG1fXpPCkysm6nNyZ8ZwNWWYLVldXY17770X48ePd/cMDx48iJiYGLRq1cpj2TZt2uDgwYNe32vWrFlISkpy/5eTkxPQthORtqpb+AIgd7lAM1p7iSi88ZoUvkzd8mEeN6G+gElDtmSYx01gDFdjsqr26eXkyZO48sor4XQ6MW/evBaXF0L4vBV933334c4773T/bbfb2ZkiMhCjlcrWor2MuxGRVox2DSV5TN3yIXXJYww3CEK+I3Xy5EmMGzcOu3fvxvLlyz1yihkZGaitrcWxY8c87kqVlpaioKDA63vGxsYiNpa3qomMymilstW2lwUYiEhLRruGknyM4QZHSHdNXZ2oHTt24Msvv0RqaqrH82effTaio6OxbNky92MlJSXYunWrz44UERmb0Uplq2mvq7JW4y88VXVO/HCgjGWKiUg2o11DiUKVrnekjh8/jp07d7r/3r17NzZt2oSUlBRkZWVh7Nix2LBhAz7++GM4HA73uKeUlBTExMQgKSkJEydOxNSpU5GamoqUlBRMmzYN3bt3x+DBg/VaLSIKgraJVvTJgmHu1ChprxAChaW+i+EUltqRlWDhFx6DYmST9GK0ayhRKPK7/HkgrFixAoMGDWry+IQJE/Dwww+jY8eOzb7u66+/xsCBAwHUF6G466678NZbb6GqqgoXXngh5s2bJ2vME8ufExmX0b6IymnvoRM1+Hbf0Rbf8085KaysZUCMbFIoMNo1lCgYNJ9HKpyxI0VEoWifvQprS8paXO6czGTk2PjF20g4GSoRUejSfB4pIiIKLlbWMga5v+gzsklE4SgSJ+xmR4qIKESxslboUxLP42SoRBRunNsL4Vj6AWAv/+NBWxLMw8eE9dxV4d1NJCIyMFbWCm1KKypyMlQiCifO7YVwLFro2YkCAHs5HIsWwrm9UJ+GBQE7UkREIay+slYyrI3ie9YoE8fR6MjfeF5zw5AZ2SSicCGczvo7UT44ln4I4QzPH4YY7SMiCnFtE63ISrCwslYAyc32q4nn6R3ZZJU2CgWROJ4mHImiXU3vRDVmL4Mo2hWWEwSzI0VEZACSJHG8TIAoyfariee5Ipu+qvYFKrLJkusUCiJ1PE1YqvB9Z172cgbDrj8REUUspdl+tfE8PSKbSsd0EWkpksfThKVEP6cN8nc5g+EdKSLywNgPRQp/s/1Sl7wmkSMt4nnBjGyy5Hr4U3rtDmbETs05R6FJatcJsCX5jvfZkuuXC0PsSBGRG2M/FEnUZPu1iucFK7LJkuvhTem1O9gRu0gfTxOOJJMJ5uFj6u8yemEePjpsO8bhuVZEJBtjPxRxVGb7jVRRkSXXw5fSa7cuEbsIH08Trkzd8mEeN6H+zlRDtmSYx00I63FvvCNFRIz9UGTSINvfNtGKzPhY/FZ2ApUnHYiPNuO05DiYQuzXV5ZcD09Kr926RewifDxNODN1y4fUJS/iKjGyI0VEjP1QRNIi299cpGrnscqQi8PqXXKdAkPptVuviF2kj6cJd5LJFHGRzPDuJhKRXxj7oUjkyvb74ivbb6Q4rGtMly+BKrlOgaP42q1TxE7tOUcUanikEhFjPxSxlGb7/Y1UCSG0aqpqrjFdjc/jUBzTRf5RfO3WMWKn53gaIQQOnajBPnsVDp2oCanzk4yJ0T4iYuyHIpqSbL+h47CNvjzyy6RxKb126x2x02M8DavSUiDw52UiYuyHIp5kMsHUoTNM3XvB1KFzi1/ojBiHdUURqx2eHadqhwi5KCL5R+m1OxQidnLPOTWMFMMlY2FHiogAGKuUM5HWhNMJ556dcG7ZAOeenRBO3x0greKwwYoaaRVFZDQq9Ci9dkdKyWojxnDJOBjtIyK3tolWZCVYcLiqFtV1TlhORUJ4J4rCmZJJSbWIwwYzaqRFFJHRqNCl9NodCSWrDR3DpZAXPmcKEWlCkiSkxcUix2ZFWlwsO1EU1pROSqo2DhvsqJHaKCKjUaFP6bU7mBE7PRgxhkvGEV5nCxERkZ/8nZTUW8xPaaRKj6iRmigio1FkZKxKS4HEaB8REUUkLSYlVRKp0iNqpCaKyGgUGRmr0lIgsftNRESRSaNJSeVGqvSIGqmJIjIaRUbGqrQUSOxIERFRZNJpUlK9okZKo4iMRpHRsSotBQqjfUREFJG0mpRUCCEr2qdn1EhJFJHRKAoHbROtyIyPxW9lJ1B50oH4aDNOS46DKcyKa1BwsSNFREQRyTUpqWPRQq/LtDQpqZKS4K6o0Q8Hyry+byCjRq4oopzl9WwvkRaaO1d3Hqtk+X5Shd1wIiKKWGomJVVTEtxoUSOjtZeoIZbvp0DhHSkiIopoSiYl9bckeFaCxeudGqNNgG209lLgyY216kGLc5XIG3akiIgo4kkmk9cS583RqiS43Jid3ozWXgocJbFWPbB8PwUSo31EREQysSQ4RTIjReV4rlIgsSNFREQkU6zZvwiQv8sRGYW/UTkhRJBa5BvL91Mg8aghIiIiIr/IicqFAlf5fl9Yvp+UYkeKiIhIphqHf7+2+7sckVEYLSrnKt/vC8v3k1LsSBEREcnEuBBFKiMe+yzfT4HCqn1EREQyueJCviJOjAsZmxFKe+vBqMc+y/dTILAjRUREJJMrLvTDgTKvyzAuZFxGKe2tByMf+yzfT1oLnfuuREREBsK4UHgyUmlvvfDYJ6rHO1JEREQK6RUX0it2Fu5xN39Le2clWAKy3sLphCjaBVTYgUQbpHadIJlC8zdvRuWI2JEiIiJSJdhxIb1iZ5EQd5NT2lvrfe7cXgjH0g8Ae/kfD9qSYB4+BqZu+Zp+llYYlaNIF5o/cxAREVETesXOIiXupldpb+f2QjgWLfTsRAGAvRyORQvh3F6o6ecRkTbYkSIiIjIAf2NnQmg7d5Ven6sHPUp7C6ez/k6UD46lH0I4Q2NeJiL6AztSREREBiAndhYOn6sHV2lvX7Qu7S2KdjW9E9WYvax+OSIKKexIERERGYBesTO9PlcPrtLevmhe2rvC990+2csRUdCwI0VERGQAesTO9PxcvbhKezden4CV9k703XGTvRwRBU14XPWIiIjCnB6xMz0/V3eNxnwFagyY1K4TYEvyvZAtuX45Igop7EgREREZgCRJyE60+FwmO1H7+Y10ibvpyFWhsNrh2XGqdoiAVCiUTCaYh4/xuYx5+OiQnU+KKJLxrCQiIjIAIQSKK6p9LlNcUR2QOyeuuFvjO1MBi7vpRK8KhaZu+TCPm9D0zpQtGeZxE0J2HimiSMcJeYmIiAxAz8ligfrOVFaCBYeralFd54TlVJwvXO5EAfpuY1O3fEhd8uqr81XYgUQbpHadeCeKKISxI0VERGQAoVA9T5KkgHTSQoXe21gymSB16ByQ9yYi7fFnDiIiIgOItOp5euA2JiI5eCUgIiIygIitnhdE3MZEJAc7UkRERAYQadXz9MBtTERysCNFRERkEJFSPU9P3MZE5C8WmyAiIjKQSKiepzduYyLyh653pL755huMHDkSWVlZkCQJH3zwgcfzQgg8/PDDyMrKgtVqxcCBA/HTTz95LFNTU4O///3vaN26NeLj4zFq1CgUFxcHcS2IiIiCy1U9L8dmRVpcLL/gBwC3MRG1RNeOVGVlJXr06IHnnnuu2efnzJmDJ598Es899xzWrl2LjIwMDBkyBBUVFe5lpkyZgvfffx/vvPMOVq1ahePHj2PEiBFwOBzBWg0iIiIiIoowkgjEFOgKSJKE999/H2PGjAFQfzcqKysLU6ZMwT333AOg/u5TmzZtMHv2bNx8880oLy9HWloaXn/9dVxxxRUAgAMHDiAnJweffvophg0b5tdn2+12JCUloby8HDab70GmREREFP6EEIz2EUUof/sGIVtsYvfu3Th48CCGDh3qfiw2NhYDBgzA6tWrAQDr16/HyZMnPZbJyspCXl6ee5nm1NTUwG63e/xHREREBAD7K6qwdFcpvt13FGtLyvDtvqNYuqsU+yuq9G4aEYWQkO1IHTx4EADQpk0bj8fbtGnjfu7gwYOIiYlBq1atvC7TnFmzZiEpKcn9X05OjsatJyIiIiPaX1GFHw6UoarO6fF4VZ0TPxwoY2eKiNxCtiPl0vg2uhCixVvrLS1z3333oby83P3fvn37NGkrERERGZcQAoWlvlMqhaV2hMioCCLSWch2pDIyMgCgyZ2l0tJS912qjIwM1NbW4tixY16XaU5sbCxsNpvHf0RERBTZDlfVNrkT1VhVnROHq2qD1CIiCmUh25Hq2LEjMjIysGzZMvdjtbW1WLlyJQoKCgAAZ599NqKjoz2WKSkpwdatW93LEBEREfmjuoVOlNzliCi86Toh7/Hjx7Fz507337t378amTZuQkpKCdu3aYcqUKZg5cyZyc3ORm5uLmTNnIi4uDuPHjwcAJCUlYeLEiZg6dSpSU1ORkpKCadOmoXv37hg8eLBeq0VEREQGZIny7/dlf5cjovCma0dq3bp1GDRokPvvO++8EwAwYcIELFiwAHfffTeqqqowadIkHDt2DH369MEXX3yBxMRE92ueeuopREVFYdy4caiqqsKFF16IBQsWwGw2B319iIiIyLhaW2NgjTL5jPdZT5VCJyIKmXmk9MR5pIiIiAgAtpSWY8exE16fz20Vh+7pSUFsEREFm+HnkSIiIiIKJiEEiiuqfS5TXFHNqn1EBEDnaB8RERGRL8LphCjaBVTYgUQbpHadIJkC8zuwnKp9aXGxmn++EAKHq2pRXeeE5VSEsKUpX4hIP+xIERERUUhybi+EY+kHgL38jwdtSTAPHwNTt3zNP0/Pqn37K6pQWGr36MhZo0zIT7ehbaJV888jIvUY7SMiIqKQ49xeCMeihZ6dKACwl8OxaCGc2ws1/0y9qvbtr6jCDwfKmtwNq6pz4ocDZdhfUaXp5xGRNtiRIiKiiCeEwKETNdhnr8KhEzUcA6Mz4XTW34nywbH0QwintneGXFX7fNG6ap8QAoWldp/LFJbaeUwShSBG+4iIKKIxUhV6RNGupneiGrOXQRTtgtShs2afK0kS8tNt+OFAmddl8tNtmo5b0ntcFhEpxztSREQUsRipClEVvu/QyF5OhraJVvTJSm5yZ8oaZUKfrGTNO9d6jssiInV4R4qIiCKSv5GqrAQLK6cFW6Kfczr6u5xMbROtyEqwBKWCnl7jsohIPXakiIgoIjFSFbqkdp0AW5LveJ8tuX65QLVBkoKy313jsnwdi1qPyyIibfDnDSIiikiMVIUuyWSCefgYn8uYh48O2HxSweQal+WL1uOyiEgbxr8CERERKcBIVWgzdcuHedyE+jtTDdmSYR43ISDzSOkl2OOyiEgbjPYREVFEYqQq9Jm65UPqkldfxa/CDiTaILXrFBZ3ohoL5rgsItIGO1JERBSR9Ch1TfJJJpOmJc5DWbDGZRGRNsLvJx0iIiI/MVJFRERK8Y4UERFFNEaqiIhICXakiIgo4jFSRUREcjHaR0REREREJBM7UkRERERERDKxI0VERERERCQTO1JEREREREQysdgEERERBZQQglURiSjssCNFREREAbO/ogqFpXZU1Tndj1mjTMhPt3GeLiIyNEb7iIiIKCD2V1ThhwNlHp0oAKiqc+KHA2XYX1GlU8uIiNRjR4qIiIg0J4RAYand5zKFpXYIIYLUIiIibbEjRURERJo7XFXb5E5UY1V1Thyuqg1Si4iItMWOFBEREWmuuoVOlNzliIhCDYtNEBERRZBgVdCzRPn3W62/yxERhRp2pIiIiCJEMCvotbbGwBpl8hnvs57qyBERGRF/BiIiIooAwa6gJ0kS8tNtPpfJT7dxPikiMix2pIiIiMKcXhX02iZa0ScrGdZG8T1rlAl9spI5jxQRGRqjfURERGFOTgW9tLhYTT+7baIVWQmWoIzLIiIKJnakiIiIwpzeFfQkSdK8g0ZEpDdG+4iIiMIcK+gREWmPV0wiIqIw56qg5wsr6BERycOOFBERUZhjBT0iIu2xI0VERBQBWEGPiEhbLDZBREQUIVhBj4hIO+xIERERRRBW0CMi0gajfURERERERDKxI0VERERERCQTO1JEREREREQysSNFREREREQkEztSREREREREMrEjRUREREREJBM7UkRERERERDKxI0VERERERCQTO1JEREREREQysSNFREREREQkEztSREREREREMrEjRUREREREJBM7UkRERERERDKxI0VERERERCQTO1JEREREREQyhXRHqq6uDg8++CA6duwIq9WKTp064Z///CecTqd7GSEEHn74YWRlZcFqtWLgwIH46aefdGw1ERERERGFu5DuSM2ePRvPP/88nnvuOWzfvh1z5szBv/71L8ydO9e9zJw5c/Dkk0/iueeew9q1a5GRkYEhQ4agoqJCx5YTEREREVE4C+mO1Jo1azB69Ghccskl6NChA8aOHYuhQ4di3bp1AOrvRj399NN44IEHcNlllyEvLw8LFy7EiRMn8NZbb+nceiIiIiIiClch3ZHq168fvvrqK/z6668AgM2bN2PVqlW4+OKLAQC7d+/GwYMHMXToUPdrYmNjMWDAAKxevdrr+9bU1MBut3v8R0RERERE5K8ovRvgyz333IPy8nJ07doVZrMZDocDjz32GK666ioAwMGDBwEAbdq08XhdmzZtsHfvXq/vO2vWLDzyyCOBazgREREREYW1kL4j9e677+KNN97AW2+9hQ0bNmDhwoX497//jYULF3osJ0mSx99CiCaPNXTfffehvLzc/d++ffsC0n4iIiIiIgpPIX1H6q677sK9996LK6+8EgDQvXt37N27F7NmzcKECROQkZEBoP7OVGZmpvt1paWlTe5SNRQbG4vY2NjANp6IiIiIiMJWSN+ROnHiBEwmzyaazWZ3+fOOHTsiIyMDy5Ytcz9fW1uLlStXoqCgIKhtJSIiIiKiyBHSd6RGjhyJxx57DO3atcOZZ56JjRs34sknn8QNN9wAoD7SN2XKFMycORO5ubnIzc3FzJkzERcXh/Hjx+vceiIiIiIiClch3ZGaO3cu/vGPf2DSpEkoLS1FVlYWbr75Zjz00EPuZe6++25UVVVh0qRJOHbsGPr06YMvvvgCiYmJOraciIiIiIjCmSSEEHo3Qm92ux1JSUkoLy+HzWbTuzlERERERKQTf/sGIT1GioiIiIiIKBSxI0VERERERCQTO1JEREREREQysSNFREREREQkEztSREREREREMrEjRUREREREJBM7UkRERERERDKxI0VERERERCQTO1JEREREREQysSNFREREREQkEztSREREREREMrEjRUREREREJBM7UkRERERERDKxI0VERERERCQTO1JEREREREQysSNFREREREQkEztSREREREREMkXp3QAiIiIiMh4hBA5X1aK6zglLlAmtrTGQJEnvZhEFDTtSRERERCTL/ooqFJbaUVXndD9mjTIhP92GtolWHVtGFDyM9hERERGR3/ZXVOGHA2UenSgAqKpz4ocDZdhfUaVTy4iCix0pIiIiIvKLEAKFpXafyxSW2iGECFKLiPTDjhQRERER+eVwVW2TO1GNVdU5cbiqNkgtItIPO1JERERE5JfqFjpRcpcjMjJ2pIiIiIjIL5Yo/746+rsckZHxKCciIiIiv7S2xsDaQifJeqoUOlG4Y0eKiIiIiPwiSRLy020+l8lPt3E+KYoI7EgRERERkd/aJlrRJyu5yZ0pa5QJfbKSOY8URQxOyEtEREREsrRNtCIrwYLDVbWornPCcirOxztRFEnYkSIiIiIi2SRJQlpcrN7NININo31EREREREQysSNFREREREQkEztSREREREREMrEjRUREREREJBM7UkRERERERDKxI0VERERERCQTO1JEREREREQysSNFREREREQkEztSREREREREMrEjRUREREREJBM7UkRERERERDKxI0VERERERCQTO1JEREREREQyRendgFAghAAA2O12nVtCRERERER6cvUJXH0Eb9iRAlBRUQEAyMnJ0bklREREREQUCioqKpCUlOT1eUm01NWKAE6nEwcOHEBiYiIkSdK7OYrZ7Xbk5ORg3759sNlsejeHDIzHEmmBxxFphccSaYXHEvlDCIGKigpkZWXBZPI+Eop3pACYTCZkZ2fr3QzN2Gw2XhxIEzyWSAs8jkgrPJZIKzyWqCW+7kS5sNgEERERERGRTOxIERERERERycSOVBiJjY3F9OnTERsbq3dTyOB4LJEWeByRVngskVZ4LJGWWGyCiIiIiIhIJt6RIiIiIiIikokdKSIiIiIiIpnYkSIiIiIiIpKJHSkiIiIiIiKZ2JEymPnz5yM/P989kdz555+Pzz77zP28EAIPP/wwsrKyYLVaMXDgQPz00086tpiMYNasWZAkCVOmTHE/xmOJ/PXwww9DkiSP/zIyMtzP81gif+3fvx9XX301UlNTERcXh7POOgvr1693P89jifzRoUOHJtckSZLwt7/9DQCPI9IOO1IGk52djccffxzr1q3DunXrcMEFF2D06NHuC8CcOXPw5JNP4rnnnsPatWuRkZGBIUOGoKKiQueWU6hau3YtXnzxReTn53s8zmOJ5DjzzDNRUlLi/m/Lli3u53gskT+OHTuGvn37Ijo6Gp999hm2bduGJ554AsnJye5leCyRP9auXetxPVq2bBkA4PLLLwfA44g0JMjwWrVqJf7zn/8Ip9MpMjIyxOOPP+5+rrq6WiQlJYnnn39exxZSqKqoqBC5ubli2bJlYsCAAeL2228XQggeSyTL9OnTRY8ePZp9jscS+euee+4R/fr18/o8jyVS6vbbbxennXaacDqdPI5IU7wjZWAOhwPvvPMOKisrcf7552P37t04ePAghg4d6l4mNjYWAwYMwOrVq3VsKYWqv/3tb7jkkkswePBgj8d5LJFcO3bsQFZWFjp27Igrr7wSu3btAsBjifz30UcfoXfv3rj88suRnp6Onj174qWXXnI/z2OJlKitrcUbb7yBG264AZIk8TgiTbEjZUBbtmxBQkICYmNjccstt+D999/HGWecgYMHDwIA2rRp47F8mzZt3M8RubzzzjvYsGEDZs2a1eQ5HkskR58+ffDaa6/h888/x0svvYSDBw+ioKAAR44c4bFEftu1axfmz5+P3NxcfP7557jlllswefJkvPbaawB4XSJlPvjgA5SVleG6664DwOOItBWldwNIvi5dumDTpk0oKyvD//73P0yYMAErV650Py9JksfyQogmj1Fk27dvH26//XZ88cUXsFgsXpfjsUT+uOiii9z/3717d5x//vk47bTTsHDhQpx33nkAeCxRy5xOJ3r37o2ZM2cCAHr27ImffvoJ8+fPx7XXXutejscSyfHyyy/joosuQlZWlsfjPI5IC7wjZUAxMTHo3LkzevfujVmzZqFHjx545pln3FWyGv+iUlpa2uSXF4ps69evR2lpKc4++2xERUUhKioKK1euxLPPPouoqCj38cJjiZSIj49H9+7dsWPHDl6XyG+ZmZk444wzPB7r1q0bioqKAIDHEsm2d+9efPnll7jxxhvdj/E4Ii2xIxUGhBCoqalBx44dkZGR4a5OA9Rng1euXImCggIdW0ih5sILL8SWLVuwadMm93+9e/fGX/7yF2zatAmdOnXisUSK1dTUYPv27cjMzOR1ifzWt29f/PLLLx6P/frrr2jfvj0A8Fgi2V599VWkp6fjkksucT/G44i0xGifwdx///246KKLkJOTg4qKCrzzzjtYsWIFli5d6p4HaObMmcjNzUVubi5mzpyJuLg4jB8/Xu+mUwhJTExEXl6ex2Px8fFITU11P85jifw1bdo0jBw5Eu3atUNpaSlmzJgBu92OCRMm8LpEfrvjjjtQUFCAmTNnYty4cfjxxx/x4osv4sUXXwQAHkski9PpxKuvvooJEyYgKuqPr7s8jkhL7EgZzO+//45rrrkGJSUlSEpKQn5+PpYuXYohQ4YAAO6++25UVVVh0qRJOHbsGPr06YMvvvgCiYmJOrecjIbHEvmruLgYV111FQ4fPoy0tDScd955+P777913EngskT/OOeccvP/++7jvvvvwz3/+Ex07dsTTTz+Nv/zlL+5leCyRv7788ksUFRXhhhtuaPIcjyPSiiSEEHo3goiIiIiIyEg4RoqIiIiIiEgmdqSIiIiIiIhkYkeKiIiIiIhIJnakiIiIiIiIZGJHioiIiIiISCZ2pIiIiIiIiGRiR4qIiIiIiEgmdqSIiIiIiIhkYkeKiIiIiIhIJnakiIgo7K1evRpmsxnDhw/XuylERBQmJCGE0LsRREREgXTjjTciISEB//nPf7Bt2za0a9dO7yYREZHB8Y4UERGFtcrKSixatAi33norRowYgQULFng8/9FHHyE3NxdWqxWDBg3CwoULIUkSysrK3MusXr0a/fv3h9VqRU5ODiZPnozKysrgrggREYUUdqSIiCisvfvuu+jSpQu6dOmCq6++Gq+++ipcYYw9e/Zg7NixGDNmDDZt2oSbb74ZDzzwgMfrt2zZgmHDhuGyyy5DYWEh3n33XaxatQq33XabHqtDREQhgtE+IiIKa3379sW4ceNw++23o66uDpmZmXj77bcxePBg3Hvvvfjkk0+wZcsW9/IPPvggHnvsMRw7dgzJycm49tprYbVa8cILL7iXWbVqFQYMGIDKykpYLBY9VouIiHTGO1JERBS2fvnlF/z444+48sorAQBRUVG44oor8Morr7ifP+ecczxec+6553r8vX79eixYsAAJCQnu/4YNGwan04ndu3cHZ0WIiCjkROndACIiokB5+eWXUVdXh7Zt27ofE0IgOjoax44dgxACkiR5vKZxUMPpdOLmm2/G5MmTm7w/i1YQEUUudqSIiCgs1dXV4bXXXsMTTzyBoUOHejz35z//GW+++Sa6du2KTz/91OO5devWefzdq1cv/PTTT+jcuXPA20xERMbBMVJERBSWPvjgA1xxxRUoLS1FUlKSx3MPPPAAPv30UyxevBhdunTBHXfcgYkTJ2LTpk2YOnUqiouLUVZWhqSkJBQWFuK8887D9ddfj7/+9a+Ij4/H9u3bsWzZMsydO1entSMiIr1xjBQREYWll19+GYMHD27SiQLq70ht2rQJx44dw3vvvYfFixcjPz8f8+fPd1fti42NBQDk/397d2srIRQEYHR6QGEoYRMEkqpQCBwoPKEdBFVciaeGZ5/bjNkl2XMqmCu/3L/XK47jiFJK9H0fbdvGNE1R1/VH1wPAs9iRAoB/lmWJfd/juq5vjwLAg7kjBcBP27Ytuq6LqqriPM9Y19UfUQC8JaQA+GmllJjnOe77jqZpYhiGGMfx22MB8HCO9gEAACR5bAIAACBJSAEAACQJKQAAgCQhBQAAkCSkAAAAkoQUAABAkpACAABIElIAAABJf5zzPaA//YvfAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#crate another figure\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "#scatter with positive\n",
    "plt.scatter(df.age[df.target==1],\n",
    "            df.thalach[df.target==1],\n",
    "            c=\"salmon\")\n",
    "\n",
    "#scatter with negative\n",
    "plt.scatter(df.age[df.target==0],\n",
    "            df.thalach[df.target==0],\n",
    "            c=\"lightblue\")\n",
    "\n",
    "#additional info\n",
    "plt.title(\"Heart disease age and max heart rate\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Max heart rate\")\n",
    "plt.legend([\"Disease\",\"No Disease\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "55b8f64a-985c-452d-b93f-5dce2e0f789e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check a list of age column with histogram\n",
    "df.age.plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dca3c4f-4613-4643-9e61-7eb8a74286c5",
   "metadata": {},
   "source": [
    "## Heart disease  frequency per chest pain type\n",
    "\n",
    "cp - chest pain type\n",
    "\n",
    "    0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "    1: Atypical angina: chest pain not related to heart\n",
    "    2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "    3: Asymptomatic: chest pain not showing signs of disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f0811338-c820-4c4a-998f-ead9f912e776",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "cp             \n",
       "0       104  39\n",
       "1         9  41\n",
       "2        18  69\n",
       "3         7  16"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.cp,df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8d825287-2f59-44d1-b558-5a26d374bb56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.cp,df.target).plot(kind=\"bar\",figsize=(10,6),color=(\"salmon\",\"lightblue\"))\n",
    "plt.title(\"Heart Disease frequency per chest pain type\")\n",
    "plt.xlabel(\"Chest pain type\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"No disease\", \"disease\"])\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
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       "      <th>0</th>\n",
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       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
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       "      <td>150</td>\n",
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       "      <td>187</td>\n",
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       "      <td>130</td>\n",
       "      <td>204</td>\n",
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       "      <td>172</td>\n",
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       "      <th>3</th>\n",
       "      <td>56</td>\n",
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       "      <td>236</td>\n",
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       "      <th>4</th>\n",
       "      <td>57</td>\n",
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       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
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       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ac30a73e-a674-4cec-9b7b-6e152f36341f",
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    {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
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       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098447</td>\n",
       "      <td>-0.068653</td>\n",
       "      <td>0.279351</td>\n",
       "      <td>0.213678</td>\n",
       "      <td>0.121308</td>\n",
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       "      <td>-0.398522</td>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.210013</td>\n",
       "      <td>-0.168814</td>\n",
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       "      <th>sex</th>\n",
       "      <td>-0.098447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>0.210041</td>\n",
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       "      <th>cp</th>\n",
       "      <td>-0.068653</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.433798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>0.279351</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>-0.144931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>0.213678</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.085239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>0.121308</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.028046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>0.137230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>-0.398522</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.421741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>-0.436757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.210013</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.430696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>-0.168814</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.345877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>-0.391724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>0.068001</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.225439</td>\n",
       "      <td>-0.280937</td>\n",
       "      <td>0.433798</td>\n",
       "      <td>-0.144931</td>\n",
       "      <td>-0.085239</td>\n",
       "      <td>-0.028046</td>\n",
       "      <td>0.137230</td>\n",
       "      <td>0.421741</td>\n",
       "      <td>-0.436757</td>\n",
       "      <td>-0.430696</td>\n",
       "      <td>0.345877</td>\n",
       "      <td>-0.391724</td>\n",
       "      <td>-0.344029</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       sex        cp  trestbps      chol       fbs  \\\n",
       "age       1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n",
       "sex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n",
       "cp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n",
       "trestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n",
       "chol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n",
       "fbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n",
       "restecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n",
       "thalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
       "exang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n",
       "oldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n",
       "slope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
       "ca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
       "thal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
       "target   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
       "\n",
       "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
       "age      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n",
       "sex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n",
       "cp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n",
       "trestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n",
       "chol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n",
       "fbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n",
       "restecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n",
       "thalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
       "exang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n",
       "oldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n",
       "slope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
       "ca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
       "thal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
       "target    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
       "\n",
       "              thal    target  \n",
       "age       0.068001 -0.225439  \n",
       "sex       0.210041 -0.280937  \n",
       "cp       -0.161736  0.433798  \n",
       "trestbps  0.062210 -0.144931  \n",
       "chol      0.098803 -0.085239  \n",
       "fbs      -0.032019 -0.028046  \n",
       "restecg  -0.011981  0.137230  \n",
       "thalach  -0.096439  0.421741  \n",
       "exang     0.206754 -0.436757  \n",
       "oldpeak   0.210244 -0.430696  \n",
       "slope    -0.104764  0.345877  \n",
       "ca        0.151832 -0.391724  \n",
       "thal      1.000000 -0.344029  \n",
       "target   -0.344029  1.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make correlation matrix\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "da9b0e82-10fe-4043-be13-ddf9f4a21326",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lets make correlation matrix visual\n",
    "corr_matrix=df.corr()\n",
    "fig,ax=plt.subplots(figsize=(15,10))\n",
    "ax=sns.heatmap(corr_matrix,annot=True,linewidth=0.5,fmt=\".2f\",cmap=\"YlGnBu\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceded404-8591-4d8d-bf07-4950d3c5eeee",
   "metadata": {},
   "source": [
    "## 5. Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "abf33d3c-2291-4917-b4c7-1e720b35efb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#split data into x and y\n",
    "X=df.drop(\"target\",axis=1)\n",
    "y=df[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2e91fd0c-e207-4f95-9567-382170f9025e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "0        0   0     1  \n",
       "1        0   0     2  \n",
       "2        2   0     2  \n",
       "3        2   0     2  \n",
       "4        2   0     2  \n",
       "..     ...  ..   ...  \n",
       "298      1   0     3  \n",
       "299      1   0     3  \n",
       "300      1   2     3  \n",
       "301      1   1     3  \n",
       "302      1   1     2  \n",
       "\n",
       "[303 rows x 13 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5cac8e81-04dc-456b-baaa-232e65243f35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "298    0\n",
       "299    0\n",
       "300    0\n",
       "301    0\n",
       "302    0\n",
       "Name: target, Length: 303, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
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  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9d9e887d-6a2c-4ba2-b5e1-dedd24c021f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#split data into train and test\n",
    "np.random.seed(42)\n",
    "#split data into training and testing \n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "942374f0-6eeb-4836-af66-04dadab2e9b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>sex</th>\n",
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       "      <th>fbs</th>\n",
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       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
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       "  <tbody>\n",
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       "      <th>132</th>\n",
       "      <td>42</td>\n",
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       "      <th>202</th>\n",
       "      <td>58</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>231</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
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       "      <th>75</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>135</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
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       "      <td>161</td>\n",
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       "      <td>1</td>\n",
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       "      <th>176</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>230</td>\n",
       "      <td>1</td>\n",
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       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>233</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>227</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>160</td>\n",
       "      <td>234</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "      <th>270</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>249</td>\n",
       "      <td>0</td>\n",
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       "      <td>144</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "<p>242 rows × 13 columns</p>\n",
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      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "132   42    1   1       120   295    0        1      162      0      0.0   \n",
       "202   58    1   0       150   270    0        0      111      1      0.8   \n",
       "196   46    1   2       150   231    0        1      147      0      3.6   \n",
       "75    55    0   1       135   250    0        0      161      0      1.4   \n",
       "176   60    1   0       117   230    1        1      160      1      1.4   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "188   50    1   2       140   233    0        1      163      0      0.6   \n",
       "71    51    1   2        94   227    0        1      154      1      0.0   \n",
       "106   69    1   3       160   234    1        0      131      0      0.1   \n",
       "270   46    1   0       120   249    0        0      144      0      0.8   \n",
       "102   63    0   1       140   195    0        1      179      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "132      2   0     2  \n",
       "202      2   0     3  \n",
       "196      1   0     2  \n",
       "75       1   0     2  \n",
       "176      2   2     3  \n",
       "..     ...  ..   ...  \n",
       "188      1   1     3  \n",
       "71       2   1     3  \n",
       "106      1   1     2  \n",
       "270      2   0     3  \n",
       "102      2   2     2  \n",
       "\n",
       "[242 rows x 13 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "938aa569-9d6b-4116-a37f-b27bab617bf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "132    1\n",
       "202    0\n",
       "196    0\n",
       "75     1\n",
       "176    0\n",
       "      ..\n",
       "188    0\n",
       "71     1\n",
       "106    1\n",
       "270    0\n",
       "102    1\n",
       "Name: target, Length: 242, dtype: int64"
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     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3c0d984-a57f-47a2-9c39-910207371c1f",
   "metadata": {},
   "source": [
    "Now you got data split into training and testing sets its time to build machine learning model\n",
    "\n",
    "we'll train it (find the pattern) on the training set\n",
    "\n",
    "and we'll test it (use the pattern) on test set\n",
    "\n",
    "We are going to try 3 different machine learning models\n",
    "1. Logistic regression\n",
    "2. K-Nearest Classifier\n",
    "3. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ac909772-c44a-47e3-a16c-2b28d5615d7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#put models in a dictonary\n",
    "models={\"Logistic Regression\": LogisticRegression(),\n",
    "        \"KNN\": KNeighborsClassifier(),\n",
    "        \"Random Forest\": RandomForestClassifier()}\n",
    "#create a function to fit and score model\n",
    "def fit_and_score(models,X_train,X_test,y_train,y_test):\n",
    "    \"\"\"\n",
    "    Fits and evaluate ML models\n",
    "    models:a dict of different ML models\n",
    "    X_train:training data\n",
    "    X_test:testing data\n",
    "    y_train:training label\n",
    "    y_test:testing label\n",
    "    \"\"\"\n",
    "    #set random seed\n",
    "    np.random.seed(42)\n",
    "    #make a dictonary to keep model score\n",
    "    model_score={}\n",
    "    #loop through model\n",
    "    for name,model in models.items():\n",
    "        #fit\n",
    "        model.fit(X_train,y_train)\n",
    "        #evaluate\n",
    "        model_score[name]=model.score(X_test,y_test)\n",
    "    return model_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "543664a0-2b23-4ada-9ad1-57d783ba3afc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nanda\\heart-disease-project\\env\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores=fit_and_score(models=models,\n",
    "                           X_train=X_train,\n",
    "                           X_test=X_test,\n",
    "                           y_train=y_train,\n",
    "                           y_test=y_test)\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f3c992e-faa7-4198-bb7d-03537e1a51ee",
   "metadata": {},
   "source": [
    "## Model comparision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "da727221-f7e1-431a-be01-789c9be83ef3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UnJzsMJ6cnKxOnTqVuc+tt96qX375RSdPnrSPHThwQG5ubmrUqNFlRAYAAM6kwodpYmNjtWjRIi1evFhpaWmKiYlRenq6Ro8eLencIZbhw4fbtx86dKj8/f318MMPa9++fdqyZYueeeYZPfLII6pRo8bV+04AAECVVOFTewcPHqzc3FzNmDFDWVlZCg8PV1JSkkJCQiRJWVlZSk9Pt29fq1YtJScna+zYsYqMjJS/v78GDRqkF1988ep9FwAAoMqqcBmRpDFjxmjMmDFlvrZ06dJSY61atSp1aAcAAEDi3jQAAMAwyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoy6rjMyfP1+hoaHy8vJSRESEtm7dekn7bd++XdWqVVP79u0v520BAIATqnAZSUxM1Pjx4zV58mSlpqaqc+fO6tWrl9LT0y+4X0FBgYYPH65u3bpddlgAAOB8KlxGZs+erVGjRik6OlqtW7dWXFycgoODtWDBggvu9/jjj2vo0KHq2LHjZYcFAADOp0Jl5PTp00pJSVFUVJTDeFRUlHbs2FHufkuWLNGhQ4c0bdq0S3qf4uJiFRYWOjwAAIBzqlAZOX78uKxWqwIDAx3GAwMDlZ2dXeY+Bw8e1MSJE7Vs2TJVq1btkt5n1qxZ8vHxsT+Cg4MrEhMAAFQhl7WA1WKxODy32WylxiTJarVq6NChmj59ulq0aHHJX3/SpEkqKCiwPzIyMi4nJgAAqAIubari/6lXr57c3d1LzYLk5OSUmi2RpBMnTmjXrl1KTU3Vk08+KUkqKSmRzWZTtWrVtG7dOnXt2rXUfp6envL09KxINAAAUEVVaGbEw8NDERERSk5OdhhPTk5Wp06dSm1fp04dffvtt9qzZ4/9MXr0aLVs2VJ79uzRzTfffGXpAQBAlVehmRFJio2N1UMPPaTIyEh17NhRCxcuVHp6ukaPHi3p3CGWzMxMJSQkyM3NTeHh4Q77BwQEyMvLq9Q4AABwTRUuI4MHD1Zubq5mzJihrKwshYeHKykpSSEhIZKkrKysi15zBAAA4LwKlxFJGjNmjMaMGVPma0uXLr3gvs8//7yef/75y3lbAADghLg3DQAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADDqssrI/PnzFRoaKi8vL0VERGjr1q3lbrtq1Sr16NFD1113nerUqaOOHTvqiy++uOzAAADAuVS4jCQmJmr8+PGaPHmyUlNT1blzZ/Xq1Uvp6ellbr9lyxb16NFDSUlJSklJUZcuXdS3b1+lpqZecXgAAFD1VbiMzJ49W6NGjVJ0dLRat26tuLg4BQcHa8GCBWVuHxcXp2effVY33nijmjdvrpdeeknNmzfXZ599dsXhAQBA1VehMnL69GmlpKQoKirKYTwqKko7duy4pK9RUlKiEydOyM/Pr9xtiouLVVhY6PAAAADOqUJl5Pjx47JarQoMDHQYDwwMVHZ29iV9jddff12///67Bg0aVO42s2bNko+Pj/0RHBxckZgAAKAKuawFrBaLxeG5zWYrNVaW5cuX6/nnn1diYqICAgLK3W7SpEkqKCiwPzIyMi4nJgAAqAKqVWTjevXqyd3dvdQsSE5OTqnZkj9LTEzUqFGjtHLlSnXv3v2C23p6esrT07Mi0QAAQBVVoZkRDw8PRUREKDk52WE8OTlZnTp1Kne/5cuXa+TIkfrggw/Up0+fy0sKAACcUoVmRiQpNjZWDz30kCIjI9WxY0ctXLhQ6enpGj16tKRzh1gyMzOVkJAg6VwRGT58uObMmaNbbrnFPqtSo0YN+fj4XMVvBQAAVEUVLiODBw9Wbm6uZsyYoaysLIWHhyspKUkhISGSpKysLIdrjrzzzjs6e/asnnjiCT3xxBP28REjRmjp0qVX/h0AAIAqrcJlRJLGjBmjMWPGlPnanwvGpk2bLuctAACAi+DeNAAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjLquMzJ8/X6GhofLy8lJERIS2bt16we03b96siIgIeXl5qUmTJnr77bcvKywAAHA+FS4jiYmJGj9+vCZPnqzU1FR17txZvXr1Unp6epnbHzlyRL1791bnzp2Vmpqq5557TuPGjdNHH310xeEBAEDVV+EyMnv2bI0aNUrR0dFq3bq14uLiFBwcrAULFpS5/dtvv63rr79ecXFxat26taKjo/XII4/otddeu+LwAACg6qtWkY1Pnz6tlJQUTZw40WE8KipKO3bsKHOfL7/8UlFRUQ5jd911l+Lj43XmzBlVr1691D7FxcUqLi62Py8oKJAkFRYWViRupSspLjIdwWlc6z/rqoTP5dXBZ/Lq4TN59Vzrn8vz+Ww22wW3q1AZOX78uKxWqwIDAx3GAwMDlZ2dXeY+2dnZZW5/9uxZHT9+XA0aNCi1z6xZszR9+vRS48HBwRWJiyrMJ850AsARn0lci6rK5/LEiRPy8fEp9/UKlZHzLBaLw3ObzVZq7GLblzV+3qRJkxQbG2t/XlJSory8PPn7+1/wfXBxhYWFCg4OVkZGhurUqWM6DsBnEtccPpNXj81m04kTJxQUFHTB7SpURurVqyd3d/dSsyA5OTmlZj/Oq1+/fpnbV6tWTf7+/mXu4+npKU9PT4cxX1/fikTFRdSpU4d/ZLim8JnEtYbP5NVxoRmR8yq0gNXDw0MRERFKTk52GE9OTlanTp3K3Kdjx46ltl+3bp0iIyPLXC8CAABcS4XPpomNjdWiRYu0ePFipaWlKSYmRunp6Ro9erSkc4dYhg8fbt9+9OjR+vnnnxUbG6u0tDQtXrxY8fHxmjBhwtX7LgAAQJVV4TUjgwcPVm5urmbMmKGsrCyFh4crKSlJISEhkqSsrCyHa46EhoYqKSlJMTExeuuttxQUFKS5c+dq4MCBV++7wCXz9PTUtGnTSh0GA0zhM4lrDZ/JymexXex8GwAAgL8Q96YBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAuLSEhASH+6Gdd/r0aSUkJBhI5Ho4m8bJ/frrr5owYYLWr1+vnJycUjcrslqthpIBwLXB3d1dWVlZCggIcBjPzc1VQEAA/09Wgsu6Nw2qjpEjRyo9PV1TpkxRgwYNuLcPAPxJefdXO3r06CVdyhxXjjLi5LZt26atW7eqffv2pqMAmjFjxiVtN3Xq1L84CSDdcMMNslgsslgs6tatm6pV+79fiVarVUeOHFHPnj0NJnQdlBEnFxwcXOrQDGDKxx9/XO5rFotFP/zwg06dOkUZQaUYMGCAJGnPnj266667VKtWLftrHh4eaty4MVcLrySsGXFy69at0+uvv6533nlHjRs3Nh0HKNOePXs0ceJEbdiwQY888ojefvtt05HgQt577z098MADXP7dIMqIk6tbt66Kiop09uxZeXt7l7pTcl5enqFkgHTkyBFNmTJFiYmJuvfee/Xiiy+qefPmpmPBxWRkZMhisahRo0aSpK+//loffPCBwsLC9NhjjxlO5xo4TOPk4uLiTEcASjl+/LimT5+uhQsX6rbbbtOOHTt04403mo4FFzV06FA99thjeuihh5Sdna3u3bsrPDxc77//vrKzszlsWAmYGQFQaX7//Xe99tprmj17tpo1a6ZZs2YpKirKdCy4uLp162rnzp1q2bKl5s6dq8TERG3fvl3r1q3T6NGjdfjwYdMRnR4zIy7AarXqk08+UVpamiwWi8LCwtSvXz+5u7ubjgYX07RpU504cUJjx47VkCFDZLFY9N///rfUdm3btjWQDq7qzJkz9vUi//nPf9SvXz9JUqtWrZSVlWUymstgZsTJ/fjjj+rdu7cyMzPVsmVL2Ww2HThwQMHBwVqzZo2aNm1qOiJciJvb/1302WKxOJzpdf65xWLhIlOoVDfffLO6dOmiPn36KCoqSjt37lS7du20c+dO3XfffTp69KjpiE6PMuLkevfuLZvNpmXLlsnPz0/SuasKPvjgg3Jzc9OaNWsMJ4Qr+fnnny9pu5CQkL84CfB/Nm3apHvuuUeFhYUaMWKEFi9eLEl67rnntH//fq1atcpwQudHGXFyNWvW1M6dO9WmTRuH8b179+rWW2/VyZMnDSUDgGuH1WpVYWGh6tatax/76aef5O3tXeoy8bj6WDPi5Dw9PXXixIlS4ydPnpSHh4eBRHBl6enpl7Td9ddf/xcnARzZbDalpKTo0KFDGjp0qGrXri0PDw95e3ubjuYSmBlxcsOHD9fu3bsVHx+vm266SZL01Vdf6dFHH1VERISWLl1qNiBcipubW5n3APn/3xvEYrHo7NmzlR0NLuznn39Wz549lZ6eruLiYh04cEBNmjTR+PHjderUKS7CVwmYGXFyc+fO1YgRI9SxY0f7Bc/Onj2rfv36ac6cOYbTwdWkpqaWOW6z2bRixQrNnTvX4ZLcQGV46qmnFBkZqb1798rf398+fs899yg6OtpgMtfBzIiLOHjwoPbv3y+bzaawsDA1a9bMdCRA0rlTKSdOnKgDBw4oNjZWEyZMoJCgUtWrV0/bt29Xy5YtVbt2be3du1dNmjTRTz/9pLCwMBUVFZmO6PSYGXERzZs35zLbuKakpKRo4sSJ2rp1q6Kjo5WUlMRCQRhRUlJS5unkR48eVe3atQ0kcj2UEScUGxurF154QTVr1lRsbOwFt509e3YlpQLO+fHHHzV58mR99NFHGjRokPbt26cmTZqYjgUX1qNHD8XFxWnhwoWSzq1bOnnypKZNm6bevXsbTucaKCNOKDU1VWfOnLH/uTxlLSQE/kpjxoxRfHy8unTpol27dql9+/amIwGaPXu2unbtqrCwMJ06dUpDhw7VwYMHVa9ePS1fvtx0PJfAmhEAlcbNzU1eXl5q1arVBbfbvXt3JSUCzvnjjz+0YsUKpaSkqKSkRB06dNCwYcNUo0YN09FcAmXExRQWFmrDhg1q1arVRX8hAFfb9OnTL7pNQUEBhw9Rac6cOaOWLVvq888/V1hYmOk4LovDNE5u0KBBuv322/Xkk0/qjz/+UGRkpH766Sf7qZQDBw40HREupGbNmpowYUK5rxcWFnIXX1Sq6tWrq7i4mMPWhrldfBNUZVu2bFHnzp0lSR9//LFsNpvy8/M1d+5cvfjii4bTwdVMmTJFS5YsKfO1kydPqmfPniosLKzkVHB1Y8eO1SuvvMLF9gxiZsTJFRQU2G+Qt3btWg0cOFDe3t7q06ePnnnmGcPp4Gr+93//Vw8++KDq1q2rAQMG2MdPnjypqKgo5ebmavPmzeYCwiV99dVXWr9+vdatW6c2bdqoZs2aDq9zo7y/HmXEyQUHB+vLL7+Un5+f1q5dqxUrVkiSfvvtN3l5eRlOB1dz3333KT8/X0OHDtWaNWvUpUsX+4zI8ePHtXnzZtWvX990TLgYX19fDlkbRhlxcuPHj9ewYcNUq1YthYSE6M4775R07vDNn+/kC1SG6Oho5eXlacCAAfr00081ZcoUZWdna/PmzWrQoIHpeHBB5R06ROXhbBoXsGvXLmVkZKhHjx72y2yvWbNGvr6+uvXWWw2ng6uaNGmSXn31VTVu3FibN29Wo0aNTEeCizt27Jh++OEHWSwWtWjRQtddd53pSC6DMuJirFarvv32W4WEhKhu3bqm48DF3HvvvQ7Pk5KS1K5dOzVs2NBhnGP0qEy///67xo4dq4SEBJWUlEiS3N3dNXz4cL355pvy9vY2nND5cTaNkxs/frzi4+MlnSsid9xxhzp06KDg4GBt2rTJbDi4HB8fH4fHkCFDFBYWVmocqEyxsbHavHmzPvvsM+Xn5ys/P1+ffvqpNm/erKefftp0PJfAzIiTa9SokT755BNFRkbqk08+0RNPPKGNGzcqISFBGzdu1Pbt201HBACj6tWrpw8//NC+pu68jRs3atCgQTp27JiZYC6EmREnd/z4cfvZCUlJSbr//vvVokULjRo1St9++63hdABgXlFRkQIDA0uNBwQEqKioyEAi10MZcXKBgYHat2+frFar1q5dq+7du0s694/P3d3dcDoAMK9jx46aNm2aTp06ZR/7448/NH36dHXs2NFgMtfBqb1O7uGHH9agQYPUoEEDWSwW9ejRQ9K5i/xwbxoAkObMmaOePXuqUaNGateunSwWi/bs2SMvLy998cUXpuO5BNaMuIAPP/xQGRkZuv/+++2nT7733nvy9fVV//79DacDAPP++OMPvf/++9q/f79sNpvCwsK4a28looy4kFOnTnHVVQD4fzZs2KDbb79d1apxkMA01ow4OavVqhdeeEENGzZUrVq1dPjwYUnnblh2/pRfAHBFPXr0UF5env35LbfcoszMTIOJXBdlxMnNnDlTS5cu1auvvioPDw/7eJs2bbRo0SKDyQDArD8fGPj+++9VXFxsKI1ro4w4uYSEBC1cuFDDhg1zOHumbdu22r9/v8FkAACcQxlxcpmZmWrWrFmp8ZKSEp05c8ZAIgC4NlgsFlkslnKfo/KwasfJ/e1vf9PWrVsVEhLiML5y5UrdcMMNhlIBgHk2m03dunWzL2AtKipS3759HQ5pS9Lu3btNxHMplBEnN23aND300EPKzMxUSUmJVq1apR9++EEJCQn6/PPPTccDAGOmTZvm8JxLHZjDqb0u4IsvvtBLL72klJQUlZSUqEOHDpo6daqioqJMRwMAgDLizM6ePauZM2fqkUceUXBwsOk4AACUiTLi5GrVqqXvvvtOjRs3Nh0FAIAycTaNk+vevbs2bdpkOgYAAOViAauT69WrlyZNmqTvvvtOERERqlmzpsPr/fr1M5QMAIBzOEzj5Nzcyp/8slgsslqtlZgGAIDSKCMAAJf39ddfa9OmTcrJyVFJSYnDa7NnzzaUynVwmAYA4NJeeukl/eMf/1DLli0VGBhY6qqs+OsxM+Lk5s6dW+a4xWKRl5eXmjVrpttvv93hvjUA4EoCAwP1yiuvaOTIkaajuCzKiJMLDQ3VsWPHVFRUpLp168pmsyk/P1/e3t6qVauWcnJy1KRJE23cuJFrkQBwSQ0aNNCWLVvUvHlz01FcFqf2OrmXXnpJN954ow4ePKjc3Fzl5eXpwIEDuvnmmzVnzhylp6erfv36iomJMR0VAIyIiYnRW2+9ZTqGS2NmxMk1bdpUH330kdq3b+8wnpqaqoEDB+rw4cPasWOHBg4cqKysLDMhAcCgkpIS9enTRwcOHFBYWJiqV6/u8PqqVasMJXMdLGB1cllZWTp79myp8bNnzyo7O1uSFBQUpBMnTlR2NAC4JowdO1YbN25Uly5d5O/vz6JVAygjTq5Lly56/PHHtWjRIt1www2Szs2K/M///I+6du0qSfr2228VGhpqMiYAGJOQkKCPPvpIffr0MR3FZbFmxMnFx8fLz89PERER8vT0lKenpyIjI+Xn56f4+HhJ5+5f8/rrrxtOCgBm+Pn5qWnTpqZjuDTWjLiI/fv368CBA7LZbGrVqpVatmxpOhIAXBOWLFmitWvXasmSJfL29jYdxyVRRlzE6dOndeTIETVt2lTVqnF0DgDOu+GGG3To0CHZbDY1bty41ALW3bt3G0rmOvit5OSKioo0duxYvffee5KkAwcOqEmTJho3bpyCgoI0ceJEwwkBwKwBAwaYjuDymBlxck899ZS2b9+uuLg49ezZU//973/VpEkTrV69WtOmTVNqaqrpiAAAF8fMiJP75JNPlJiYqFtuucXhdLWwsDAdOnTIYDIAuLakpKQoLS1NFotFYWFh9jMQ8dejjDi5Y8eOKSAgoNT477//zrn0ACApJydHDzzwgDZt2iRfX1/ZbDYVFBSoS5cuWrFiha677jrTEZ0ep/Y6uRtvvFFr1qyxPz9fQN5991117NjRVCwAuGaMHTtWhYWF+v7775WXl6fffvtN3333nQoLCzVu3DjT8VwCa0ac3I4dO9SzZ08NGzZMS5cu1eOPP67vv/9eX375pTZv3qyIiAjTEQHAKB8fH/3nP//RjTfe6DD+9ddfKyoqSvn5+WaCuRBmRpxcp06dtH37dhUVFalp06Zat26dAgMD9eWXX1JEAEDn7k3z59N5Jal69eoqKSkxkMj1MDPiwj788EPdd999pmMAgFH9+/dXfn6+li9frqCgIElSZmamhg0bprp16+rjjz82nND5MTPixM6ePavvv/9eBw4ccBj/9NNP1a5dOw0bNsxQMgC4dsybN08nTpxQ48aN1bRpUzVr1kyhoaE6ceKE3nzzTdPxXAIzI05q3759uvvuu/Xzzz9LOtf8FyxYoEGDBmnv3r2Kjo7WU089peDgYMNJAeDakJycrP3798tmsyksLEzdu3c3HcllUEacVL9+/fT7778rJiZGy5YtU2Jiopo1a6YHH3xQMTExql27tumIAABIoow4rfr16yspKUkdOnRQfn6+/Pz89M477+jRRx81HQ0AjJs7d+4lb8vpvX89yoiTcnNzU1ZWlgIDAyVJtWrV0u7du9WiRQvDyQDAvNDQUIfnx44dU1FRkXx9fSVJ+fn58vb2VkBAgA4fPmwgoWthAauTslgscnP7vx+vm5tbmaeuAYArOnLkiP0xc+ZMtW/fXmlpacrLy1NeXp7S0tLUoUMHvfDCC6ajugRmRpyUm5ubfHx87Fdczc/PV506dRwKiiTl5eWZiAcA14ymTZvqww8/LHUvmpSUFN133306cuSIoWSug3vTOKklS5aYjgAAVUJWVpbOnDlTatxqterXX381kMj1MDMCAHBpffv2VXp6uuLj4xURESGLxaJdu3bp0UcfVXBwsFavXm06otNjzQgAwKUtXrxYDRs21E033SQvLy95enrq5ptvVoMGDbRo0SLT8VwCMyMAAEg6cOCA/aJnrVu35uzDSkQZAQAARrGAFQDg0qxWq5YuXar169crJyen1J16N2zYYCiZ66CMAABc2lNPPaWlS5eqT58+Cg8Pt18SAZWHwzRO7r777lNkZKQmTpzoMP7Pf/5TX3/9tVauXGkoGQBcG+rVq6eEhAT17t3bdBSXxdk0Tm7z5s3q06dPqfGePXtqy5YtBhIBwLXFw8NDzZo1Mx3DpVFGnNzJkyfl4eFRarx69eoqLCw0kAgAri1PP/205syZIw4UmMOaEScXHh6uxMRETZ061WF8xYoVCgsLM5QKAK4d27Zt08aNG/Xvf/9bf/vb30rdx2vVqlWGkrkOyoiTmzJligYOHKhDhw6pa9eukqT169dr+fLlrBcBAEm+vr665557TMdwaSxgdQFr1qzRSy+9pD179qhGjRpq27atpk2bpjvuuMN0NAAAKCMAAMAsDtMAAFzehx9+qH/9619KT0/X6dOnHV7bvXu3oVSug7NpnJCfn5+OHz8uSapbt678/PzKfQCAq5s7d64efvhhBQQEKDU1VTfddJP8/f11+PBh9erVy3Q8l8DMiBN64403VLt2bfufuZogAJRv/vz5WrhwoYYMGaL33ntPzz77rJo0aaKpU6cqLy/PdDyXwJoRAIBL8/b2VlpamkJCQhQQEKDk5GS1a9dOBw8e1C233KLc3FzTEZ0eh2mcnLu7u3JyckqN5+bmyt3d3UAiALi21K9f3144QkJCtHPnTknSkSNHuBBaJaGMOLny/iEVFxeXeWVWAHA1Xbt21WeffSZJGjVqlGJiYtSjRw8NHjyY649UEtaMOKm5c+dKkiwWixYtWqRatWrZX7NardqyZYtatWplKh4AXDMWLlyokpISSdLo0aPl5+enbdu2qW/fvho9erThdK6BNSNOKjQ0VJL0888/q1GjRg6HZDw8PNS4cWPNmDFDN998s6mIAHDNy8zMVMOGDU3HcHqUESfXpUsXrVq1SnXr1jUdBQCqjOzsbM2cOVOLFi3SH3/8YTqO02PNiJPbuHGjQxGxWq3as2ePfvvtN4OpAMC8/Px8DRs2TNddd52CgoI0d+5clZSUaOrUqWrSpIl27typxYsXm47pEigjTm78+PGKj4+XdK6I3H777erQoYOCg4O1adMms+EAwKDnnntOW7Zs0YgRI+Tn56eYmBjdfffd2rZtm/7973/rm2++0ZAhQ0zHdAmUESe3cuVKtWvXTpL02Wef6aefftL+/fs1fvx4TZ482XA6ADBnzZo1WrJkiV577TWtXr1aNptNLVq00IYNG7iRaCVjzYiT8/Ly0o8//qhGjRrpsccek7e3t+Li4nTkyBG1a9dOhYWFpiMCgBHVq1fXzz//rKCgIEnnLn729ddfKzw83HAy18PMiJMLDAzUvn37ZLVatXbtWnXv3l2SVFRUxEXPALi0kpISVa9e3f7c3d1dNWvWNJjIdXGdESf38MMPa9CgQWrQoIEsFot69OghSfrqq6+4zggAl2az2TRy5Eh5enpKkk6dOqXRo0eXKiSrVq0yEc+lUEac3PPPP6/w8HBlZGTo/vvvt/+jc3d318SJEw2nAwBzRowY4fD8wQcfNJQErBkBAABGMTPihObOnavHHntMXl5e9svCl2fcuHGVlAoAgLIxM+KEQkNDtWvXLvn7+9svC18Wi8Wiw4cPV2IyAABKo4wAAACjOLUXAAAYxZoRJxcbG1vmuMVikZeXl5o1a6b+/fvLz8+vkpMBAHAOh2mcXJcuXbR7925ZrVa1bNlSNptNBw8elLu7u1q1aqUffvhBFotF27ZtU1hYmOm4AAAXxGEaJ9e/f391795dv/zyi1JSUrR7925lZmaqR48eGjJkiDIzM3X77bcrJibGdFQAgItiZsTJNWzYUMnJyaVmPb7//ntFRUUpMzNTu3fvVlRUlI4fP24oJQDAlTEz4uQKCgqUk5NTavzYsWP2m+T5+vrq9OnTlR0NAABJlBGn179/fz3yyCP6+OOPdfToUWVmZurjjz/WqFGjNGDAAEnS119/rRYtWpgNCgBwWRymcXInT55UTEyMEhISdPbsWUlStWrVNGLECL3xxhuqWbOm9uzZI0lq3769uaAAAJdFGXERJ0+e1OHDh2Wz2dS0aVPVqlXLdCQAACRxnRGXUatWLfn5+clisVBEAADXFNaMOLmSkhLNmDFDPj4+CgkJ0fXXXy9fX1+98MILKikpMR0PAABmRpzd5MmTFR8fr5dfflm33nqrbDabtm/frueff16nTp3SzJkzTUcEALg41ow4uaCgIL399tvq16+fw/inn36qMWPGKDMz01AyAADO4TCNk8vLy1OrVq1Kjbdq1Up5eXkGEgEA4Igy4uTatWunefPmlRqfN2+e2rVrZyARAACOOEzj5DZv3qw+ffro+uuvV8eOHWWxWLRjxw5lZGQoKSlJnTt3Nh0RAODiKCMu4JdfftFbb72l/fv3y2azKSwsTGPGjFFQUJDpaAAAUEZcVUZGhqZNm6bFixebjgIAcHGUERe1d+9edejQQVar1XQUAICLYwErAAAwijICAACMoowAAACjuBy8k7r33nsv+Hp+fn7lBAEA4CIoI07Kx8fnoq8PHz68ktIAAFA+zqYBAABGsWYEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGPX/ARosJIpaC/DeAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare=pd.DataFrame(model_scores,index=[\"accuracy\"])\n",
    "model_compare.T.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e270ec6-1539-4850-8947-26458fb6dc33",
   "metadata": {},
   "source": [
    "Now we'ev got the baseline model... and we know a model first predictions are'nt always what we should based uor next steps off\n",
    "\n",
    "Let's look at the foll\n",
    "* Hyperparameter tuning\n",
    "* Feature importance\n",
    "* Confusion matrix\n",
    "* Crossvalidation\n",
    "* precision\n",
    "* Recall\n",
    "* F1 score\n",
    "* Classsifcation report\n",
    "* ROC curve\n",
    "* Area under the curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74c65ff5-5e6a-4981-a713-200a143ca728",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning (hand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e064d6e3-27e4-4fc6-ae16-1716c42e1b63",
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets tune KNN\n",
    "train_score=[]\n",
    "test_score=[]\n",
    "\n",
    "#create a list of different value for n_neighbors\n",
    "neighbors=range(1,21)\n",
    "\n",
    "#setup KNN instance\n",
    "knn=KNeighborsClassifier()\n",
    "\n",
    "#loop through diff n_neighbors\n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors=i)\n",
    "    #fit\n",
    "    knn.fit(X_train,y_train)\n",
    "    #update training score\n",
    "    train_score.append(knn.score(X_train,y_train))\n",
    "    #update the test score\n",
    "    test_score.append(knn.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3df11580-95a9-405c-adbc-81fb8e2edbb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0,\n",
       " 0.8099173553719008,\n",
       " 0.7727272727272727,\n",
       " 0.743801652892562,\n",
       " 0.7603305785123967,\n",
       " 0.7520661157024794,\n",
       " 0.743801652892562,\n",
       " 0.7231404958677686,\n",
       " 0.71900826446281,\n",
       " 0.6942148760330579,\n",
       " 0.7272727272727273,\n",
       " 0.6983471074380165,\n",
       " 0.6900826446280992,\n",
       " 0.6942148760330579,\n",
       " 0.6859504132231405,\n",
       " 0.6735537190082644,\n",
       " 0.6859504132231405,\n",
       " 0.6652892561983471,\n",
       " 0.6818181818181818,\n",
       " 0.6694214876033058]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0ca1281e-531a-4668-b7fa-327feadfc1b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6229508196721312,\n",
       " 0.639344262295082,\n",
       " 0.6557377049180327,\n",
       " 0.6721311475409836,\n",
       " 0.6885245901639344,\n",
       " 0.7213114754098361,\n",
       " 0.7049180327868853,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.7049180327868853,\n",
       " 0.7540983606557377,\n",
       " 0.7377049180327869,\n",
       " 0.7377049180327869,\n",
       " 0.7377049180327869,\n",
       " 0.6885245901639344,\n",
       " 0.7213114754098361,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.7049180327868853,\n",
       " 0.6557377049180327]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1f7f5727-b17e-4115-8df4-65f315676d19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN score on the test data: 75.41%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(neighbors,train_score,label=\"Train_score\")\n",
    "plt.plot(neighbors,test_score,label=\"Test_score\")\n",
    "plt.xticks(np.arange(1,21,1))\n",
    "plt.xlabel(\"Number of neighbors\")\n",
    "plt.ylabel(\"Model score\")\n",
    "plt.legend()\n",
    "print(f\"Maximum KNN score on the test data: {max(test_score)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d578092-caa5-4c6a-a387-eecc98a5c793",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning with RandomizedSearchCV\n",
    "\n",
    "we'er going to tune\n",
    "* Logistic Regression\n",
    "* RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c69f2299-1bb7-4a25-a1f8-8b01bb408d8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a hyper parameter grid for logistic regression\n",
    "log_reg_grid={\"C\":np.logspace(-4,4,20),\n",
    "              \"solver\":[\"liblinear\"]}\n",
    "#create a hyper parameter grid for RandomForestRegression\n",
    "rf_grid={\"n_estimators\":np.arange(10,1000,50),\n",
    "         \"max_depth\":[None,3,5,10],\n",
    "         \"min_samples_split\":np.arange(2,20,2),\n",
    "         \"min_samples_leaf\":np.arange(1,20,2)}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5fc9453-020d-4e38-8cb6-912de6302e24",
   "metadata": {},
   "source": [
    "Now we got grid of hyperparameter setup for each of our models, lets tune them using randomsearchcv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0641b1b2-8475-4097-8fa5-9daf81c648ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "                   param_distributions={&#x27;C&#x27;: array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "                   param_distributions={&#x27;C&#x27;: array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "                   verbose=True)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.23357214690901212, solver=&#x27;liblinear&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.23357214690901212, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "                   param_distributions={'C': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        'solver': ['liblinear']},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "#setup \n",
    "rs_log_reg=RandomizedSearchCV(LogisticRegression(),\n",
    "                              param_distributions=log_reg_grid,\n",
    "                              cv=5,\n",
    "                              n_iter=10,\n",
    "                              verbose=True)\n",
    "#fit\n",
    "rs_log_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cf638d33-2076-4701-99cc-bca59b77dc12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "42041265-5acc-4462-ab7c-5ae40c8bde5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b9056e6-e2fc-4da3-ab41-bf2627ca937b",
   "metadata": {},
   "source": [
    "Lets do it for randomforestclassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4c229e90-ed46-474d-b7eb-8bc21b233eb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: RandomForestClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=3, min_samples_leaf=19, min_samples_split=4,\n",
       "                       n_estimators=210)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=3, min_samples_leaf=19, min_samples_split=4,\n",
       "                       n_estimators=210)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
       "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        'n_estimators': array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "#setup \n",
    "rs_rf=RandomizedSearchCV(RandomForestClassifier(),\n",
    "                         param_distributions=rf_grid,\n",
    "                         cv=5,\n",
    "                         n_iter=20,\n",
    "                         verbose=True)\n",
    "#fit\n",
    "rs_rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "67a75ca1-2154-4384-ab56-ebc4de580414",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "574b4052-d4c7-40bf-9e1d-0ef27c9f8f2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b80b0c5c-3a2c-4050-935f-5077315979a1",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning using with GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "239fb777-603b-4b3a-9deb-84bf1c68a1a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                         &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "             verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                         &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "             verbose=True)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.23357214690901212, solver=&#x27;liblinear&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.23357214690901212, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={'C': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                         'solver': ['liblinear']},\n",
       "             verbose=True)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a hyper parameter grid for logistic regression\n",
    "log_reg_grid={\"C\":np.logspace(-4,4,20),\n",
    "              \"solver\":[\"liblinear\"]}\n",
    "np.random.seed(42)\n",
    "#setup \n",
    "gs_log_reg=GridSearchCV(LogisticRegression(),\n",
    "                        param_grid=log_reg_grid,\n",
    "                        cv=5,\n",
    "                        verbose=True)\n",
    "#fit\n",
    "gs_log_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f12cefe6-938e-46ea-b4b9-f4a886d31ac4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.23357214690901212, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1df4dc9a-ccf4-4098-8ad3-4b3bf63c1c7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "092bc2d5-239e-4c2e-8a1c-0371b86f235d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "616cad33-e7ec-48fe-be3f-e732309a54d7",
   "metadata": {},
   "source": [
    "## Evaluate our tuned ML classifier,beyond accuracy\n",
    "\n",
    "* ROC curve and AUC curve\n",
    "* Confusion matrix\n",
    "* Classification report\n",
    "* Precision\n",
    "* Recall\n",
    "* F1-score\n",
    "\n",
    "To make comparision and evaluate our trained model, first we should make prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "20d94289-22ad-4584-905e-93a53a396890",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds=gs_log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "cadb39d7-b8d1-4cc5-8782-d98f2815a342",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "858aaf72-af1d-4b71-bc04-d0cafc7386b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179    0\n",
       "228    0\n",
       "111    1\n",
       "246    0\n",
       "60     1\n",
       "      ..\n",
       "249    0\n",
       "104    1\n",
       "300    0\n",
       "193    0\n",
       "184    0\n",
       "Name: target, Length: 61, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c57effe9-7645-47be-a969-d80a0472f2bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[25  4]\n",
      " [ 3 29]]\n"
     ]
    }
   ],
   "source": [
    "#confusion matrix\n",
    "print(confusion_matrix(y_test,y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1865ae96-63a2-45dd-bf59-7ca73f77ea2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5)\n",
    "\n",
    "def plot_conf_mat(y_test,y_preds):\n",
    "    fig,ax=plt.subplots(figsize=(3,3))\n",
    "    ax=sns.heatmap(confusion_matrix(y_test,y_preds),\n",
    "                   annot=True,\n",
    "                   cbar=False)\n",
    "    plt.xlabel(\"Predicted model\")\n",
    "    plt.ylabel(\"True label\")\n",
    "plot_conf_mat(y_test,y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "042b77a3-adf5-42f7-b261-a46986faeca9",
   "metadata": {},
   "source": [
    "Now lets get classification report as well as cv,precision,recall,f1 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "06e98044-4d73-41df-9c3a-2cd81d77e034",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.86      0.88        29\n",
      "           1       0.88      0.91      0.89        32\n",
      "\n",
      "    accuracy                           0.89        61\n",
      "   macro avg       0.89      0.88      0.88        61\n",
      "weighted avg       0.89      0.89      0.89        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42cb1fcf-e8a0-42f2-a031-8855e3d322eb",
   "metadata": {},
   "source": [
    "## caluculate classification report using cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "cdb233ae-4a91-4e1c-a593-0b2730e3dcdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.23357214690901212, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "54091520-c9ff-4902-81f7-15c847b791d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a new classifier with best params\n",
    "clf=LogisticRegression(C=0.233572146909010212,\n",
    "                       solver=\"liblinear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "94a56f48-f834-499b-aed2-4ebc6598a81b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81967213, 0.90163934, 0.8852459 , 0.88333333, 0.75      ])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cross validated accuracy\n",
    "cv_acc=cross_val_score(clf,\n",
    "                       X,\n",
    "                       y,\n",
    "                       cv=5,\n",
    "                       scoring=\"accuracy\")\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7e82de4d-020b-4e5a-a975-cc24e18317fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8479781420765027"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_acc=np.mean(cv_acc)\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "df6cca09-f2a3-4e3e-bec6-a267fd455866",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.775     , 0.88571429, 0.86111111, 0.86111111, 0.725     ])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cross validated precision\n",
    "cv_precision=cross_val_score(clf,\n",
    "                       X,\n",
    "                       y,\n",
    "                       cv=5,\n",
    "                       scoring=\"precision\")\n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5ffcf831-250e-4ff9-a580-4823f2d9ce54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8215873015873015"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_precision=np.mean(cv_precision)\n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "61361046-86dd-4bcb-8da3-df34573b6d75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9272727272727274"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cross validated recall\n",
    "cv_recall=cross_val_score(clf,\n",
    "                       X,\n",
    "                       y,\n",
    "                       cv=5,\n",
    "                       scoring=\"recall\")\n",
    "cv_recall=np.mean(cv_recall)\n",
    "cv_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f6b62f1a-e933-4e1e-8587-1fe127ee3ee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8705403543192143"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cross validated f1\n",
    "cv_f1=cross_val_score(clf,\n",
    "                       X,\n",
    "                       y,\n",
    "                       cv=5,\n",
    "                       scoring=\"f1\")\n",
    "cv_f1=np.mean(cv_f1)\n",
    "cv_f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "198e64fe-e81f-4533-b2cf-f3d29a760da8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_metrics=pd.DataFrame({\"Accuracy\":cv_acc,\n",
    "                        \"Precision\":cv_precision,\n",
    "                        \"Recall\":cv_recall,\n",
    "                        \"F1\":cv_f1},\n",
    "                       index=[0])\n",
    "cv_metrics.T.plot.bar(title=\"cross_validated_classification metrics\",\n",
    "                      legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87217190-7c49-4c7f-aa1b-3136b220119c",
   "metadata": {},
   "source": [
    "## Feature Importance\n",
    "\n",
    "which feature contributed most to outcome of the model and how did they contribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b9974b97-0eaf-42f9-91f5-98f0c927a638",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-6 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-6 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-6 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-6 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.2335721469090102, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.2335721469090102, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=0.2335721469090102, solver='liblinear')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit\n",
    "clf=LogisticRegression(C=0.233572146909010212,\n",
    "                       solver=\"liblinear\")\n",
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "9ab6af53-3003-40a5-83b7-4d2a36da30da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00369922, -0.90424091,  0.67472826, -0.0116134 , -0.00170364,\n",
       "         0.04787688,  0.33490199,  0.02472938, -0.63120407, -0.57590956,\n",
       "         0.47095144, -0.65165347, -0.69984209]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d4d117d8-2951-4d14-a160-bf097f2742f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 0.00369922110076602,\n",
       " 'sex': -0.9042409125948285,\n",
       " 'cp': 0.6747282555957682,\n",
       " 'trestbps': -0.011613400902554584,\n",
       " 'chol': -0.0017036438926708716,\n",
       " 'fbs': 0.04787688000781044,\n",
       " 'restecg': 0.3349019893449764,\n",
       " 'thalach': 0.024729382707843965,\n",
       " 'exang': -0.6312040709389142,\n",
       " 'oldpeak': -0.5759095585900958,\n",
       " 'slope': 0.47095143578161225,\n",
       " 'ca': -0.651653471712355,\n",
       " 'thal': -0.6998420883205941}"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_dict=dict(zip(df.columns,list(clf.coef_[0])))\n",
    "feature_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "0e6a165c-8704-4098-8282-36b1b3c9602d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Feature Importance'}>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Vizualize feature importance\n",
    "feature_df=pd.DataFrame(feature_dict,index=[0])\n",
    "feature_df.T.plot.bar(title=\"Feature Importance\",legend=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}