end-to-end-heart-disease-classification.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "illegal-lithuania",
   "metadata": {},
   "source": [
    "# Predicting heart disease using machine learning\n",
    "This notebook looks into using various Python-based machine learning and data science libraries in attempt to build a machine learning model capable of predicting whether or not someone has heart disease based on their medical attributes.\n",
    "\n",
    "We're going to take the following approach:\n",
    "1. Problem definition\n",
    "2. Data\n",
    "3. Evaluation\n",
    "4. Features\n",
    "5. Model\n",
    "6. Experimentation\n",
    "\n",
    "## 1. Problem Definition\n",
    "\n",
    "In a statement,\n",
    "> Given clinical parameters about a patient, can we predict whether or not they have a heart disease?\n",
    "\n",
    "## 2. Data\n",
    "\n",
    "The orginal data came from the Cleveland data from the UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/heart+disease\n",
    "\n",
    "\n",
    "There's also version of it avalable on Kaggle. https://www.kaggle.com/ronitf/heart-disease-uci\n",
    "\n",
    "## 3. Evaluation\n",
    "\n",
    "> If we can reach 95% accuracy at predicting whether or not a patient has heart disease during the proof of concept, we'll pursue the project.\n",
    "\n",
    "## 4. Features\n",
    "\n",
    "This is where you'll get different information about each of the features in your data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "functional-sussex",
   "metadata": {},
   "source": [
    "***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",
    "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "atomic-regard",
   "metadata": {},
   "source": [
    "## Preparing the tools\n",
    "\n",
    "We're going to use Pandas, Matplotlib and NumPy fro data analysis and manipulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "renewable-locking",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import all the tools we need\n",
    "\n",
    "# Regular EDA (exploratory data analysis) and plotting libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# we want our plots to appear inside the notebook\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 Evaluations\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 plot_roc_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "conditional-precipitation",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "pleasant-weekly",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"heart-disease.csv\")\n",
    "df.shape # (rows, columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "commercial-teacher",
   "metadata": {},
   "source": [
    "## Data Exploration (exploratory data analysis or EDA)\n",
    "\n",
    "The goal here is to find out more about the data and become a subject matter export on the dataset you're working with.\n",
    "\n",
    "1. What question(s) 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": 3,
   "id": "prompt-coverage",
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <td>0</td>\n",
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       "      <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",
       "      <td>1</td>\n",
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       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
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       "      <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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "technological-harmony",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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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",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
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       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
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       "      <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",
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       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "economic-dictionary",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's find out how many of each class there\n",
    "df[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "mature-military",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"target\"].value_counts().plot(kind=\"bar\", color=[\"salmon\", \"lightblue\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "useful-dietary",
   "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": 8,
   "id": "focused-million",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are there any missing values?\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "removed-alberta",
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "finite-flashing",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency accorind to Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "productive-charlotte",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    207\n",
       "0     96\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bright-municipality",
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare target column with sex column\n",
    "pd.crosstab(df.target, df.sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "demographic-concentrate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot of crosstab\n",
    "pd.crosstab(df.target, df.sex).plot(kind=\"bar\",\n",
    "                                   figsize=(10, 6),\n",
    "                                   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); #change rotation of label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "lesbian-christianity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "162    11\n",
       "163     9\n",
       "160     9\n",
       "152     8\n",
       "173     8\n",
       "       ..\n",
       "128     1\n",
       "129     1\n",
       "134     1\n",
       "137     1\n",
       "202     1\n",
       "Name: thalach, Length: 91, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"thalach\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "needed-desktop",
   "metadata": {},
   "source": [
    "### Age VS. Max Heart Rate for Heart Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "difficult-receptor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create another figure\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Scatter with positive example\n",
    "plt.scatter(df.age[df.target==1],\n",
    "           df.thalach[df.target==1],\n",
    "           c=\"salmon\");\n",
    "\n",
    "# Scatter with negative example\n",
    "plt.scatter(df.age[df.target==0],\n",
    "           df.thalach[df.target==0],\n",
    "           c=\"lightblue\");\n",
    "\n",
    "# Add some helpful info\n",
    "plt.title(\"Heart Disease in function of 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": 32,
   "id": "needed-express",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the distribution of the age column with histogram\n",
    "df.age.plot.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incorrect-reality",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency per Chest Pain Type\n",
    "\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "occupational-windsor",
   "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "atlantic-judges",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make the crosstab more visual\n",
    "pd.crosstab(df.cp, df.target).plot(kind=\"bar\",\n",
    "                                  figsize=(10, 6),\n",
    "                                  color=[\"salmon\", \"lightblue\"])\n",
    "\n",
    "#Add some communication\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",
   "execution_count": 38,
   "id": "assumed-collectible",
   "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>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",
       "      <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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "higher-brain",
   "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>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",
       "      <td>-0.116211</td>\n",
       "      <td>-0.398522</td>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.210013</td>\n",
       "      <td>-0.168814</td>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.068001</td>\n",
       "      <td>-0.225439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "      <td>-0.280937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a correlation matrix\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "expensive-layer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's make our correlation matrix a little prettier\n",
    "corr_matrix = df.corr()\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "ax = sns.heatmap(corr_matrix,\n",
    "                annot=True,\n",
    "                linewidth=0.5,\n",
    "                fmt=\".2f\",\n",
    "                cmap=\"YlGnBu\")\n",
    "# bottom, top = ax.get_ylim()\n",
    "# ax.set_ylim(bottom + 0.5, top - 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "african-graphic",
   "metadata": {},
   "source": [
    "## 5. Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "asian-emerald",
   "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>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",
       "      <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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "possible-plaza",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into x and y\n",
    "x = df.drop(\"target\", axis=1)\n",
    "\n",
    "y = df[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "stylish-interval",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>sex</th>\n",
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       "      <td>...</td>\n",
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       "      <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",
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       "      <td>1</td>\n",
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       "      <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",
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       "    <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",
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       "      <td>3</td>\n",
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      ],
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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  \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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "x"
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  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "favorite-cleanup",
   "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"
      ]
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     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
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    "y"
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  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "informed-gravity",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into train and test sets\n",
    "np.random.seed(42)\n",
    "\n",
    "# Split into train and test set\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, \n",
    "                                                    y, \n",
    "                                                    test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "spoken-madness",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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       "      <td>131</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>63</td>\n",
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       "      <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",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>242 rows × 13 columns</p>\n",
       "</div>"
      ],
      "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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "periodic-fetish",
   "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,\n",
       " 242)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train, len(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "thrown-mouth",
   "metadata": {},
   "source": [
    "Now we've got our data split into training and test sets, it's 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 the test set.\n",
    "\n",
    "We're going to try 3 different machine learning models:\n",
    "1. Logistic Regression\n",
    "2. K-Nearest Neighbours Classifier\n",
    "3. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "isolated-handle",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put models in a dictionary\n",
    "models = {\"Logistic Regression\": LogisticRegression(),\n",
    "         \"KNN\" : KNeighborsClassifier(),\n",
    "         \"Random Forest\": RandomForestClassifier()}\n",
    "\n",
    "# Create a function to fit and score a models\n",
    "def fit_and_score(models, x_train, x_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    Fits and evaluates given machine learning models.\n",
    "    models : a dict of different Scikit-Learn machine learning models\n",
    "    x_train : training data (no labels)\n",
    "    x_test : testing data (no labels)\n",
    "    y_train : training labels\n",
    "    y_test : test labels\n",
    "    \"\"\"\n",
    "    # Set random seed\n",
    "    np.random.seed(42)\n",
    "    # Make a dictionary to keep model scores\n",
    "    model_scores = {}\n",
    "    # Loop through models\n",
    "    for name, model in models.items():\n",
    "        # Fit the model to the data\n",
    "        model.fit(x_train, y_train)\n",
    "        # Evaluate the model and append its score to model_scores\n",
    "        model_scores[name] = model.score(x_test, y_test)\n",
    "    return model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "sorted-chess",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\MLearning\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: 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": 55,
     "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",
    "\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "settled-trademark",
   "metadata": {},
   "source": [
    "### Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "electric-accuracy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(model_scores, index=[\"accuracy\"])\n",
    "model_compare.T.plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lyric-marketplace",
   "metadata": {},
   "source": [
    "Now we've got baseline model... and we know a model's first predictions aren't always what we should based our next step off. What should we do?\n",
    "\n",
    "Let's look at the following :\n",
    "* Hyperparameter tuning\n",
    "* Feature importance\n",
    "* Confusion matrix\n",
    "* Cross-validation\n",
    "* Precision\n",
    "* Recall\n",
    "* F1 score\n",
    "* Classification report\n",
    "* ROC curve\n",
    "* Area under the curve (AUC)\n",
    "\n",
    "### Hyperparameter tuning (By Hand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "outer-stations",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's tune KNN\n",
    "\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "\n",
    "# Create a list of different values for n_neigbors\n",
    "neighbors = range(1, 21)\n",
    "\n",
    "# Setup KNN instance\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "#Loop through different n_neighbors\n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors=i)\n",
    "\n",
    "    # Fit the algorithm\n",
    "    knn.fit(x_train, y_train)\n",
    "    \n",
    "    # Update the training scores list\n",
    "    train_scores.append(knn.score(x_train, y_train))\n",
    "    \n",
    "    # Update the test scores list\n",
    "    test_scores.append(knn.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "solved-class",
   "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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "opposite-redhead",
   "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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "northern-casting",
   "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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(neighbors, train_scores, label=\"Train score\")\n",
    "plt.plot(neighbors, test_scores, label=\"Test scores\")\n",
    "plt.xticks(np.arange(1, 21, 1))\n",
    "plt.xlabel(\"Number of neighbors\")\n",
    "plt.ylabel(\"Model Score\")\n",
    "plt.legend()\n",
    "\n",
    "print(f\"Maximum KNN score on the test data: {max(test_scores)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "authorized-attachment",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning with RandomizedSearchCV\n",
    "\n",
    "We're going to tune:\n",
    "* LogisticRegression()\n",
    "* RandomForestClassifier()\n",
    "... using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "fatty-penalty",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a hyperparameter grid for LogisticRegression\n",
    "log_reg_grid = {\"C\": np.logspace(-4, 4, 20),\n",
    "               \"solver\":[\"liblinear\"]}\n",
    "\n",
    "# Create a hyperparameter grid for RandomForestClassifier\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": "rotary-perry",
   "metadata": {},
   "source": [
    "Now we've got hyperparameter grids setup for each of our models, let's tune them using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "italian-correspondence",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.9s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune LogisticRegression\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for LogisticRegression\n",
    "rs_log_reg = RandomizedSearchCV(LogisticRegression(),\n",
    "                               param_distributions=log_reg_grid,\n",
    "                                cv=5,\n",
    "                               n_iter=20,\n",
    "                               verbose=True)\n",
    "\n",
    "# Fit random hyperparameter search for LogisticRegression\n",
    "rs_log_reg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "revolutionary-casting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "adverse-engine",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "genetic-waters",
   "metadata": {},
   "source": [
    "Now we've tuned LogisticRegression(), Let's do the same for RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "geological-corporation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:  1.4min finished\n"
     ]
    },
    {
     "data": {
      "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": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune RandomForestClassifier\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for RandomForestClassifier\n",
    "rs_rf = RandomizedSearchCV(RandomForestClassifier(),\n",
    "                           param_distributions=rf_grid,\n",
    "                           cv=5,\n",
    "                           n_iter=20,\n",
    "                           verbose=True)\n",
    "\n",
    "# Fit random hyperparameter search model for RandomForestClassifier()\n",
    "rs_rf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "continental-trunk",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the best hyperparameters\n",
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "unauthorized-consensus",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the randomized search RandomForestClassifier model\n",
    "rs_rf.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "advisory-million",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "respective-polymer",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning with GridSearchCV\n",
    "\n",
    "Since our LogisticRegression model provides the best score so far, we'll try improve them again using GridSearchCV.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "mineral-addition",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.7s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={'C': array([1.00000000e-04, 1.88739182e-04, 3.56224789e-04, 6.72335754e-04,\n",
       "       1.26896100e-03, 2.39502662e-03, 4.52035366e-03, 8.53167852e-03,\n",
       "       1.61026203e-02, 3.03919538e-02, 5.73615251e-02, 1.08263673e-01,\n",
       "       2.04335972e-01, 3.85662042e-01, 7.27895384e-01, 1.37382380e+00,\n",
       "       2.59294380e+00, 4.89390092e+00, 9.23670857e+00, 1.74332882e+01,\n",
       "       3.29034456e+01, 6.21016942e+01, 1.17210230e+02, 2.21221629e+02,\n",
       "       4.17531894e+02, 7.88046282e+02, 1.48735211e+03, 2.80721620e+03,\n",
       "       5.29831691e+03, 1.00000000e+04]),\n",
       "                         'solver': ['liblinear']},\n",
       "             verbose=True)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Different hyperparameters for our LogisticRegression model\n",
    "log_reg_grid = {\"C\": np.logspace(-4, 4, 30),\n",
    "               \"solver\": [\"liblinear\"]}\n",
    "\n",
    "# Setup grid hyperparameter search for LogisticRegression\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
    "                         param_grid=log_reg_grid,\n",
    "                         cv=5,\n",
    "                         verbose=True)\n",
    "\n",
    "# Fit grid hyperparamter search model\n",
    "gs_log_reg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "magnetic-bangladesh",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the best hyperparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "regional-underwear",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the grid search LogisticRegression model\n",
    "gs_log_reg.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "actual-phase",
   "metadata": {},
   "source": [
    "## Evaluating our tuned machine learning classifier, beyond accuracy\n",
    "\n",
    "* ROC curve and AUC score\n",
    "* Confusion matrix\n",
    "* Classification report\n",
    "* Precision\n",
    "* Recall\n",
    "* F1-score\n",
    "\n",
    "... and it would be great if cross-validation was used where possible.\n",
    "\n",
    "To make comparisons and evaluate our trained model, first we need to make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "designing-shell",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions with tuned model\n",
    "y_preds = gs_log_reg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "crazy-inspector",
   "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": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "essential-westminster",
   "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": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "valid-investment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x1ca69707340>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC curve and calculate and calculate AUC metric\n",
    "plot_roc_curve(gs_log_reg, x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "vital-sacramento",
   "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": 96,
   "id": "dependent-priest",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns\n",
    "sns.set(font_scale=1.5)\n",
    "\n",
    "def plot_conf_mat(y_test, y_preds):\n",
    "    \"\"\"\n",
    "    Plot a nice looking confusion matrix using seaborn's heatmap()\n",
    "    \"\"\"\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(\"True label\")\n",
    "    plt.ylabel(\"Predicted label\")\n",
    "    \n",
    "#     bottom, top = ax.get_ylim()\n",
    "#     ax.set_ylim(bottom + 0.5, top - 0.5)\n",
    "    \n",
    "plot_conf_mat(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sitting-subject",
   "metadata": {},
   "source": [
    "Now we've got a ROC curve, an AUC metric and a confusion matrix, let's get a classification report as well as cross-validated precision, recall, and f1-score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "developed-egyptian",
   "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": "engaged-wrestling",
   "metadata": {},
   "source": [
    "### Calculate evaluation metrics using cross-validation\n",
    "\n",
    "We're going to calculate accuracy, precision, recall, and f1-score f our model using cross-validation and to do so we'll be using `cross_val_score()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "occupied-specialist",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check best hypeparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "varied-static",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new classifier with best parameters\n",
    "clf = LogisticRegression(C=0.20433597178569418,\n",
    "                        solver=\"liblinear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "permanent-style",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81967213, 0.90163934, 0.86885246, 0.88333333, 0.75      ])"
      ]
     },
     "execution_count": 100,
     "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": 101,
   "id": "tired-things",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8446994535519124"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_acc = np.mean(cv_acc)\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "collaborative-genealogy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8207936507936507"
      ]
     },
     "execution_count": 102,
     "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 = np.mean(cv_precision)\n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "seasonal-surveillance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9212121212121213"
      ]
     },
     "execution_count": 103,
     "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": 104,
   "id": "julian-jefferson",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8673007976269721"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated f1-score\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": 106,
   "id": "visible-there",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize cross-validated metrics\n",
    "cv_metrics = pd.DataFrame({\"Accuracy\": cv_acc,\n",
    "                          \"Precision\": cv_precision,\n",
    "                          \"Recall\": cv_recall,\n",
    "                          \"F1\": cv_f1},\n",
    "                         index=[0])\n",
    "\n",
    "cv_metrics.T.plot.bar(title=\"Cross-validated classification metrics\",\n",
    "                     legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "single-bridal",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "\n",
    "Feature importance is another way as asking, \"which features contributed most to outcomes of model and how did they contributed?\"\n",
    "\n",
    "Finding feature importance is different for each machine learning model. one way to finde feature importance is to search for \"(MODEL NAME) feature importance\".\n",
    "\n",
    "Let's find the feature importance for our LogisticeRegression model..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "broke-tiffany",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit an instance of LogisticRegression\n",
    "gs_log_reg.best_params_\n",
    "\n",
    "clf = LogisticRegression(C = 0.20433597178569418,\n",
    "                        solver=\"liblinear\")\n",
    "clf.fit(x_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "domestic-dispatch",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00316728, -0.86044651,  0.66067041, -0.01156993, -0.00166374,\n",
       "         0.04386107,  0.31275847,  0.02459361, -0.6041308 , -0.56862804,\n",
       "         0.45051628, -0.63609897, -0.67663373]])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check coef_\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "enhanced-shark",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 0.0031672801993431563,\n",
       " 'sex': -0.8604465072345515,\n",
       " 'cp': 0.6606704082033799,\n",
       " 'trestbps': -0.01156993168080875,\n",
       " 'chol': -0.001663744504776871,\n",
       " 'fbs': 0.043861071652469864,\n",
       " 'restecg': 0.31275846822418324,\n",
       " 'thalach': 0.024593613737779126,\n",
       " 'exang': -0.6041308000615746,\n",
       " 'oldpeak': -0.5686280368396555,\n",
       " 'slope': 0.4505162797258308,\n",
       " 'ca': -0.6360989676086223,\n",
       " 'thal': -0.6766337263029825}"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Match coef's of features to columns\n",
    "feature_dict = dict(zip(df.columns, list(clf.coef_[0])))\n",
    "feature_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "deluxe-information",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize feature importance\n",
    "feature_df = pd.DataFrame(feature_dict, index=[0])\n",
    "feature_df.T.plot.bar(title=\"Feature Importance\", legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "indoor-decade",
   "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>sex</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>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>114</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "sex            \n",
       "0        24  72\n",
       "1       114  93"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"sex\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "interpreted-sensitivity",
   "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>slope</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target   0    1\n",
       "slope          \n",
       "0       12    9\n",
       "1       91   49\n",
       "2       35  107"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"slope\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "soviet-light",
   "metadata": {},
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unusual-rhythm",
   "metadata": {},
   "source": [
    "## 6. Experimentation\n",
    "\n",
    "If you haven't hit your evaluation metric yet... ask yourself..\n",
    "\n",
    "* Could you collect more data?\n",
    "* Could you try a better model? Like CatBoost, or XGBoost?\n",
    "* Could we improve the current models? (beyond what we've done so far)\n",
    "* If your model is good enough (you have hit your evaluation metric) how could you share with the others?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "beneficial-grill",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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