water_quality_analysis.ipynb

{
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  {
   "cell_type": "code",
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   "metadata": {
    "id": "hbuhwu3LbjMi"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import scale,StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "DxyPc1xJdJTY"
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"water_quality.csv\")"
   ]
  },
  {
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   "execution_count": 3,
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    "outputId": "9819259f-800f-47cd-cce0-9df2bcf94b1c"
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       "   Unnamed: 0        ph    Hardness        Solids  Chloramines     Sulfate  \\\n",
       "0           0       NaN  204.890455  20791.318981     7.300212  368.516441   \n",
       "1           1  3.716080  129.422921  18630.057858     6.635246         NaN   \n",
       "2           2  8.099124  224.236259  19909.541732     9.275884         NaN   \n",
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       "4           4  9.092223  181.101509  17978.986339     6.546600  310.135738   \n",
       "\n",
       "   Conductivity  Organic_carbon  Trihalomethanes  Turbidity  Potability  \n",
       "0    564.308654       10.379783        86.990970   2.963135           0  \n",
       "1    592.885359       15.180013        56.329076   4.500656           0  \n",
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    "df.shape"
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       "      <td>47580.991603</td>\n",
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       "      <td>392.449580</td>\n",
       "      <td>19.903225</td>\n",
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       "      <td>11983.869376</td>\n",
       "      <td>6.303357</td>\n",
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       "      <td>402.883113</td>\n",
       "      <td>11.168946</td>\n",
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       "      <td>16.140368</td>\n",
       "      <td>78.698446</td>\n",
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       "            ph    Hardness        Solids  Chloramines     Sulfate  \\\n",
       "0          NaN  204.890455  20791.318981     7.300212  368.516441   \n",
       "1     3.716080  129.422921  18630.057858     6.635246         NaN   \n",
       "2     8.099124  224.236259  19909.541732     9.275884         NaN   \n",
       "3     8.316766  214.373394  22018.417441     8.059332  356.886136   \n",
       "4     9.092223  181.101509  17978.986339     6.546600  310.135738   \n",
       "...        ...         ...           ...          ...         ...   \n",
       "6547  4.668102  193.681735  47580.991603     7.166639  359.948574   \n",
       "6548  7.808856  193.553212  17329.802160     8.061362         NaN   \n",
       "6549  9.419510  175.762646  33155.578218     7.350233         NaN   \n",
       "6550  5.126763  230.603758  11983.869376     6.303357         NaN   \n",
       "6551  7.874671  195.102299  17404.177061     7.509306         NaN   \n",
       "\n",
       "      Conductivity  Organic_carbon  Trihalomethanes  Turbidity  Potability  \n",
       "0       564.308654       10.379783        86.990970   2.963135           0  \n",
       "1       592.885359       15.180013        56.329076   4.500656           0  \n",
       "2       418.606213       16.868637        66.420093   3.055934           0  \n",
       "3       363.266516       18.436524       100.341674   4.628771           0  \n",
       "4       398.410813       11.558279        31.997993   4.075075           0  \n",
       "...            ...             ...              ...        ...         ...  \n",
       "6547    526.424171       13.894419        66.687695   4.435821           1  \n",
       "6548    392.449580       19.903225              NaN   2.798243           1  \n",
       "6549    432.044783       11.039070        69.845400   3.298875           1  \n",
       "6550    402.883113       11.168946        77.488213   4.708658           1  \n",
       "6551    327.459760       16.140368        78.698446   2.309149           1  \n",
       "\n",
       "[6552 rows x 10 columns]"
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   "source": [
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6552 entries, 0 to 6551\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Unnamed: 0       6552 non-null   int64  \n",
      " 1   ph               5570 non-null   float64\n",
      " 2   Hardness         6552 non-null   float64\n",
      " 3   Solids           6552 non-null   float64\n",
      " 4   Chloramines      6552 non-null   float64\n",
      " 5   Sulfate          4990 non-null   float64\n",
      " 6   Conductivity     6552 non-null   float64\n",
      " 7   Organic_carbon   6552 non-null   float64\n",
      " 8   Trihalomethanes  6228 non-null   float64\n",
      " 9   Turbidity        6552 non-null   float64\n",
      " 10  Potability       6552 non-null   int64  \n",
      "dtypes: float64(9), int64(2)\n",
      "memory usage: 563.2 KB\n"
     ]
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   "source": [
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  {
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       "      <th>mean</th>\n",
       "      <td>1637.500000</td>\n",
       "      <td>7.080795</td>\n",
       "      <td>196.369496</td>\n",
       "      <td>22014.092526</td>\n",
       "      <td>7.122277</td>\n",
       "      <td>333.775777</td>\n",
       "      <td>426.205111</td>\n",
       "      <td>14.284970</td>\n",
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       "      <td>3.966786</td>\n",
       "      <td>0.390110</td>\n",
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       "      <th>std</th>\n",
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       "      <td>1.594176</td>\n",
       "      <td>32.877252</td>\n",
       "      <td>8767.901548</td>\n",
       "      <td>1.582964</td>\n",
       "      <td>41.412689</td>\n",
       "      <td>80.817895</td>\n",
       "      <td>3.307909</td>\n",
       "      <td>16.173710</td>\n",
       "      <td>0.780323</td>\n",
       "      <td>0.487812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.432000</td>\n",
       "      <td>320.942611</td>\n",
       "      <td>0.352000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>181.483754</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.738000</td>\n",
       "      <td>1.450000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>818.750000</td>\n",
       "      <td>6.093092</td>\n",
       "      <td>176.850538</td>\n",
       "      <td>15666.690297</td>\n",
       "      <td>6.127421</td>\n",
       "      <td>307.697010</td>\n",
       "      <td>365.734414</td>\n",
       "      <td>12.065801</td>\n",
       "      <td>55.835966</td>\n",
       "      <td>3.439711</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1637.500000</td>\n",
       "      <td>7.036752</td>\n",
       "      <td>196.967627</td>\n",
       "      <td>20927.833607</td>\n",
       "      <td>7.130299</td>\n",
       "      <td>333.073546</td>\n",
       "      <td>421.884968</td>\n",
       "      <td>14.218338</td>\n",
       "      <td>66.622485</td>\n",
       "      <td>3.955028</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2456.250000</td>\n",
       "      <td>8.062066</td>\n",
       "      <td>216.667456</td>\n",
       "      <td>27332.762127</td>\n",
       "      <td>8.114887</td>\n",
       "      <td>359.950968</td>\n",
       "      <td>481.792304</td>\n",
       "      <td>16.557652</td>\n",
       "      <td>77.339918</td>\n",
       "      <td>4.500320</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3275.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>323.124000</td>\n",
       "      <td>61227.196008</td>\n",
       "      <td>13.127000</td>\n",
       "      <td>481.030642</td>\n",
       "      <td>753.342620</td>\n",
       "      <td>28.300000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>6.739000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0           ph     Hardness        Solids  Chloramines  \\\n",
       "count  6552.000000  5570.000000  6552.000000   6552.000000  6552.000000   \n",
       "mean   1637.500000     7.080795   196.369496  22014.092526     7.122277   \n",
       "std     945.771874     1.594176    32.877252   8767.901548     1.582964   \n",
       "min       0.000000     0.000000    47.432000    320.942611     0.352000   \n",
       "25%     818.750000     6.093092   176.850538  15666.690297     6.127421   \n",
       "50%    1637.500000     7.036752   196.967627  20927.833607     7.130299   \n",
       "75%    2456.250000     8.062066   216.667456  27332.762127     8.114887   \n",
       "max    3275.000000    14.000000   323.124000  61227.196008    13.127000   \n",
       "\n",
       "           Sulfate  Conductivity  Organic_carbon  Trihalomethanes  \\\n",
       "count  4990.000000   6552.000000     6552.000000      6228.000000   \n",
       "mean    333.775777    426.205111       14.284970        66.396293   \n",
       "std      41.412689     80.817895        3.307909        16.173710   \n",
       "min     129.000000    181.483754        2.200000         0.738000   \n",
       "25%     307.697010    365.734414       12.065801        55.835966   \n",
       "50%     333.073546    421.884968       14.218338        66.622485   \n",
       "75%     359.950968    481.792304       16.557652        77.339918   \n",
       "max     481.030642    753.342620       28.300000       124.000000   \n",
       "\n",
       "         Turbidity   Potability  \n",
       "count  6552.000000  6552.000000  \n",
       "mean      3.966786     0.390110  \n",
       "std       0.780323     0.487812  \n",
       "min       1.450000     0.000000  \n",
       "25%       3.439711     0.000000  \n",
       "50%       3.955028     0.000000  \n",
       "75%       4.500320     1.000000  \n",
       "max       6.739000     1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3v9K9aEzeUAb",
    "outputId": "5a683520-fa8f-4932-c5da-c9e4c25b68a5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0            0\n",
       "ph                  982\n",
       "Hardness              0\n",
       "Solids                0\n",
       "Chloramines           0\n",
       "Sulfate            1562\n",
       "Conductivity          0\n",
       "Organic_carbon        0\n",
       "Trihalomethanes     324\n",
       "Turbidity             0\n",
       "Potability            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "yqF8bEngCXMs"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "\n",
    "df = pd.DataFrame(imputer.fit_transform(df))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "h1DS2L-fCkan",
    "outputId": "32f98282-8b94-442e-b8c9-53e4331e1fe4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.080795</td>\n",
       "      <td>204.890455</td>\n",
       "      <td>20791.318981</td>\n",
       "      <td>7.300212</td>\n",
       "      <td>368.516441</td>\n",
       "      <td>564.308654</td>\n",
       "      <td>10.379783</td>\n",
       "      <td>86.990970</td>\n",
       "      <td>2.963135</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.716080</td>\n",
       "      <td>129.422921</td>\n",
       "      <td>18630.057858</td>\n",
       "      <td>6.635246</td>\n",
       "      <td>333.775777</td>\n",
       "      <td>592.885359</td>\n",
       "      <td>15.180013</td>\n",
       "      <td>56.329076</td>\n",
       "      <td>4.500656</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>8.099124</td>\n",
       "      <td>224.236259</td>\n",
       "      <td>19909.541732</td>\n",
       "      <td>9.275884</td>\n",
       "      <td>333.775777</td>\n",
       "      <td>418.606213</td>\n",
       "      <td>16.868637</td>\n",
       "      <td>66.420093</td>\n",
       "      <td>3.055934</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>8.316766</td>\n",
       "      <td>214.373394</td>\n",
       "      <td>22018.417441</td>\n",
       "      <td>8.059332</td>\n",
       "      <td>356.886136</td>\n",
       "      <td>363.266516</td>\n",
       "      <td>18.436524</td>\n",
       "      <td>100.341674</td>\n",
       "      <td>4.628771</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>9.092223</td>\n",
       "      <td>181.101509</td>\n",
       "      <td>17978.986339</td>\n",
       "      <td>6.546600</td>\n",
       "      <td>310.135738</td>\n",
       "      <td>398.410813</td>\n",
       "      <td>11.558279</td>\n",
       "      <td>31.997993</td>\n",
       "      <td>4.075075</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6547</th>\n",
       "      <td>3271.0</td>\n",
       "      <td>4.668102</td>\n",
       "      <td>193.681735</td>\n",
       "      <td>47580.991603</td>\n",
       "      <td>7.166639</td>\n",
       "      <td>359.948574</td>\n",
       "      <td>526.424171</td>\n",
       "      <td>13.894419</td>\n",
       "      <td>66.687695</td>\n",
       "      <td>4.435821</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6548</th>\n",
       "      <td>3272.0</td>\n",
       "      <td>7.808856</td>\n",
       "      <td>193.553212</td>\n",
       "      <td>17329.802160</td>\n",
       "      <td>8.061362</td>\n",
       "      <td>333.775777</td>\n",
       "      <td>392.449580</td>\n",
       "      <td>19.903225</td>\n",
       "      <td>66.396293</td>\n",
       "      <td>2.798243</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6549</th>\n",
       "      <td>3273.0</td>\n",
       "      <td>9.419510</td>\n",
       "      <td>175.762646</td>\n",
       "      <td>33155.578218</td>\n",
       "      <td>7.350233</td>\n",
       "      <td>333.775777</td>\n",
       "      <td>432.044783</td>\n",
       "      <td>11.039070</td>\n",
       "      <td>69.845400</td>\n",
       "      <td>3.298875</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6550</th>\n",
       "      <td>3274.0</td>\n",
       "      <td>5.126763</td>\n",
       "      <td>230.603758</td>\n",
       "      <td>11983.869376</td>\n",
       "      <td>6.303357</td>\n",
       "      <td>333.775777</td>\n",
       "      <td>402.883113</td>\n",
       "      <td>11.168946</td>\n",
       "      <td>77.488213</td>\n",
       "      <td>4.708658</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>6551</th>\n",
       "      <td>3275.0</td>\n",
       "      <td>7.874671</td>\n",
       "      <td>195.102299</td>\n",
       "      <td>17404.177061</td>\n",
       "      <td>7.509306</td>\n",
       "      <td>333.775777</td>\n",
       "      <td>327.459760</td>\n",
       "      <td>16.140368</td>\n",
       "      <td>78.698446</td>\n",
       "      <td>2.309149</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6552 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1           2             3         4           5   \\\n",
       "0        0.0  7.080795  204.890455  20791.318981  7.300212  368.516441   \n",
       "1        1.0  3.716080  129.422921  18630.057858  6.635246  333.775777   \n",
       "2        2.0  8.099124  224.236259  19909.541732  9.275884  333.775777   \n",
       "3        3.0  8.316766  214.373394  22018.417441  8.059332  356.886136   \n",
       "4        4.0  9.092223  181.101509  17978.986339  6.546600  310.135738   \n",
       "...      ...       ...         ...           ...       ...         ...   \n",
       "6547  3271.0  4.668102  193.681735  47580.991603  7.166639  359.948574   \n",
       "6548  3272.0  7.808856  193.553212  17329.802160  8.061362  333.775777   \n",
       "6549  3273.0  9.419510  175.762646  33155.578218  7.350233  333.775777   \n",
       "6550  3274.0  5.126763  230.603758  11983.869376  6.303357  333.775777   \n",
       "6551  3275.0  7.874671  195.102299  17404.177061  7.509306  333.775777   \n",
       "\n",
       "              6          7           8         9    10  \n",
       "0     564.308654  10.379783   86.990970  2.963135  0.0  \n",
       "1     592.885359  15.180013   56.329076  4.500656  0.0  \n",
       "2     418.606213  16.868637   66.420093  3.055934  0.0  \n",
       "3     363.266516  18.436524  100.341674  4.628771  0.0  \n",
       "4     398.410813  11.558279   31.997993  4.075075  0.0  \n",
       "...          ...        ...         ...       ...  ...  \n",
       "6547  526.424171  13.894419   66.687695  4.435821  1.0  \n",
       "6548  392.449580  19.903225   66.396293  2.798243  1.0  \n",
       "6549  432.044783  11.039070   69.845400  3.298875  1.0  \n",
       "6550  402.883113  11.168946   77.488213  4.708658  1.0  \n",
       "6551  327.459760  16.140368   78.698446  2.309149  1.0  \n",
       "\n",
       "[6552 rows x 11 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "euwUrq9TGk6T",
    "outputId": "ec3d40f1-adaa-4014-c8f6-9b16a038ca04"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ArENBAQAA1qGgAAAA61BQAACAdSgoAADAOudUUB5//HHFxcXpgQceCG1ra2tTeXm5+vfvrz59+qi0tFRNTU1ht2tsbNT48eOVkpKijIwMTZ8+XSdPnjyXQwEAAD3IWReU7du36yc/+YmGDx8etv3BBx/Uq6++qlWrVmnjxo06cOCA7rzzztD+9vZ2jR8/XidOnNDmzZv1wgsvaPny5Zo9e/bZrwIAAPQoZ1VQjh49qokTJ+rf//3f1a9fv9D2w4cPa+nSpXriiSd08803a9SoUVq2bJk2b96sLVu2SJKqq6u1d+9e/fznP9eIESM0btw4LViwQFVVVTpx4kR0VgUAALq1Xmdzo/Lyco0fP15FRUV69NFHQ9vr6+sVDAZVVFQU2jZkyBDl5uaqrq5OhYWFqqur07Bhw+T1ekNjSkpKNHXqVO3Zs0cjR4503F8gEFAgEAhdb21tlSQFg0EFg8GzWcJpdc7njjdRnbe76syBPP6CTJzIxIlMnMjEyeZMov38GumcEReUlStX6ne/+522b9/u2Of3+5WYmKi0tLSw7V6vV36/PzTm8+Wkc3/nvlNZuHCh5s2b59heXV2tlJSUSJdwRhaM7uiSebsr8nAiEycycSITJzJxsjGTtWvXRn3O48ePn/HYiArKhx9+qO9///uqqalRUlJSxAd2tiorK1VRURG63traqpycHBUXF8vj8UT1voLBoGpqajRrR7wCHXFRnbs7cscbLRjdQR6fQyZOZOJEJk5k4mRzJrvnlkR9zs53QM5ERAWlvr5ezc3Nuvrqq0Pb2tvbtWnTJv3bv/2bXn/9dZ04cUItLS1hr6I0NTUpMzNTkpSZmalt27aFzdv5LZ/OMV/kdrvldrsd210ul1wuVyRLOGOBjjgF2u06WWKJPJzIxIlMnMjEiUycbMykK55fI5kzog/JjhkzRrt27dLOnTtDl9GjR2vixImhf7tcLtXW1oZu09DQoMbGRvl8PkmSz+fTrl271NzcHBpTU1Mjj8ej/Pz8SA4HAAD0UBG9gtK3b18NHTo0bFvv3r3Vv3//0PZJkyapoqJC6enp8ng8uv/+++Xz+VRYWChJKi4uVn5+vu655x4tWrRIfr9fM2fOVHl5+SlfJQEAABees/oWz5d58sknFR8fr9LSUgUCAZWUlOi5554L7U9ISNCaNWs0depU+Xw+9e7dW2VlZZo/f360DwUAAHRT51xQNmzYEHY9KSlJVVVVqqqqOu1t8vLyuuTTwQAAoGfgb/EAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdSIqKIsXL9bw4cPl8Xjk8Xjk8/n02muvhfa3tbWpvLxc/fv3V58+fVRaWqqmpqawORobGzV+/HilpKQoIyND06dP18mTJ6OzGgAA0CNEVFAuueQSPf7446qvr9eOHTt0880367bbbtOePXskSQ8++KBeffVVrVq1Shs3btSBAwd05513hm7f3t6u8ePH68SJE9q8ebNeeOEFLV++XLNnz47uqgAAQLfWK5LBt956a9j1f/7nf9bixYu1ZcsWXXLJJVq6dKlWrFihm2++WZK0bNkyXXHFFdqyZYsKCwtVXV2tvXv36o033pDX69WIESO0YMECzZgxQ3PnzlViYmL0VgYAALqtiArK57W3t2vVqlU6duyYfD6f6uvrFQwGVVRUFBozZMgQ5ebmqq6uToWFhaqrq9OwYcPk9XpDY0pKSjR16lTt2bNHI0eOPOV9BQIBBQKB0PXW1lZJUjAYVDAYPNslnFLnfO54E9V5u6vOHMjjL8jEiUycyMSJTJxsziTaz6+RzhlxQdm1a5d8Pp/a2trUp08frV69Wvn5+dq5c6cSExOVlpYWNt7r9crv90uS/H5/WDnp3N+573QWLlyoefPmObZXV1crJSUl0iWckQWjO7pk3u6KPJzIxIlMnMjEiUycbMxk7dq1UZ/z+PHjZzw24oIyePBg7dy5U4cPH9ZLL72ksrIybdy4MdJpIlJZWamKiorQ9dbWVuXk5Ki4uFgejyeq9xUMBlVTU6NZO+IV6IiL6tzdkTveaMHoDvL4HDJxIhMnMnEiEyebM9k9tyTqc3a+A3ImIi4oiYmJGjRokCRp1KhR2r59u55++mndddddOnHihFpaWsJeRWlqalJmZqYkKTMzU9u2bQubr/NbPp1jTsXtdsvtdju2u1wuuVyuSJdwRgIdcQq023WyxBJ5OJGJE5k4kYkTmTjZmElXPL9GMuc5/w5KR0eHAoGARo0aJZfLpdra2tC+hoYGNTY2yufzSZJ8Pp927dql5ubm0Jiamhp5PB7l5+ef66EAAIAeIqJXUCorKzVu3Djl5ubqyJEjWrFihTZs2KDXX39dqampmjRpkioqKpSeni6Px6P7779fPp9PhYWFkqTi4mLl5+frnnvu0aJFi+T3+zVz5kyVl5ef8hUSAABwYYqooDQ3N+s73/mODh48qNTUVA0fPlyvv/66vvnNb0qSnnzyScXHx6u0tFSBQEAlJSV67rnnQrdPSEjQmjVrNHXqVPl8PvXu3VtlZWWaP39+dFcFAAC6tYgKytKlS790f1JSkqqqqlRVVXXaMXl5eV3yyWAAANBz8Ld4AACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOtQUAAAgHUoKAAAwDoUFAAAYJ2ICsrChQt1zTXXqG/fvsrIyNDtt9+uhoaGsDFtbW0qLy9X//791adPH5WWlqqpqSlsTGNjo8aPH6+UlBRlZGRo+vTpOnny5LmvBgAA9AgRFZSNGzeqvLxcW7ZsUU1NjYLBoIqLi3Xs2LHQmAcffFCvvvqqVq1apY0bN+rAgQO68847Q/vb29s1fvx4nThxQps3b9YLL7yg5cuXa/bs2dFbFQAA6NZ6RTJ43bp1YdeXL1+ujIwM1dfX68Ybb9Thw4e1dOlSrVixQjfffLMkadmyZbriiiu0ZcsWFRYWqrq6Wnv37tUbb7whr9erESNGaMGCBZoxY4bmzp2rxMTE6K0OAAB0SxEVlC86fPiwJCk9PV2SVF9fr2AwqKKiotCYIUOGKDc3V3V1dSosLFRdXZ2GDRsmr9cbGlNSUqKpU6dqz549GjlypON+AoGAAoFA6Hpra6skKRgMKhgMnssSHDrnc8ebqM7bXXXmQB5/QSZOZOJEJk5k4mRzJtF+fo10zrMuKB0dHXrggQd03XXXaejQoZIkv9+vxMREpaWlhY31er3y+/2hMZ8vJ537O/edysKFCzVv3jzH9urqaqWkpJztEr7UgtEdXTJvd0UeTmTiRCZOZOJEJk42ZrJ27dqoz3n8+PEzHnvWBaW8vFy7d+/WW2+9dbZTnLHKykpVVFSErre2tionJ0fFxcXyeDxRva9gMKiamhrN2hGvQEdcVOfujtzxRgtGd5DH55CJE5k4kYkTmTjZnMnuuSVRn7PzHZAzcVYFZdq0aVqzZo02bdqkSy65JLQ9MzNTJ06cUEtLS9irKE1NTcrMzAyN2bZtW9h8nd/y6RzzRW63W26327Hd5XLJ5XKdzRK+UqAjToF2u06WWCIPJzJxIhMnMnEiEycbM+mK59dI5ozoWzzGGE2bNk2rV6/W+vXrNXDgwLD9o0aNksvlUm1tbWhbQ0ODGhsb5fP5JEk+n0+7du1Sc3NzaExNTY08Ho/y8/MjORwAANBDRfQKSnl5uVasWKFXXnlFffv2DX1mJDU1VcnJyUpNTdWkSZNUUVGh9PR0eTwe3X///fL5fCosLJQkFRcXKz8/X/fcc48WLVokv9+vmTNnqry8/JSvkgAAgAtPRAVl8eLFkqSbbropbPuyZct07733SpKefPJJxcfHq7S0VIFAQCUlJXruuedCYxMSErRmzRpNnTpVPp9PvXv3VllZmebPn39uKwEAAD1GRAXFmK/+GlRSUpKqqqpUVVV12jF5eXld8ulgAADQM/C3eAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGCdiAvKpk2bdOuttyo7O1txcXF6+eWXw/YbYzR79mxlZWUpOTlZRUVFev/998PGHDp0SBMnTpTH41FaWpomTZqko0ePntNCAABAzxFxQTl27JiuuuoqVVVVnXL/okWL9Mwzz2jJkiXaunWrevfurZKSErW1tYXGTJw4UXv27FFNTY3WrFmjTZs2acqUKWe/CgAA0KP0ivQG48aN07hx4065zxijp556SjNnztRtt90mSfrZz34mr9erl19+WRMmTNC7776rdevWafv27Ro9erQk6dlnn9Utt9yiH//4x8rOzj6H5QAAgJ4g4oLyZfbv3y+/36+ioqLQttTUVBUUFKiurk4TJkxQXV2d0tLSQuVEkoqKihQfH6+tW7fqjjvucMwbCAQUCARC11tbWyVJwWBQwWAwmksIzeeON1Gdt7vqzIE8/oJMnMjEiUycyMTJ5kyi/fwa6ZxRLSh+v1+S5PV6w7Z7vd7QPr/fr4yMjPCD6NVL6enpoTFftHDhQs2bN8+xvbq6WikpKdE4dIcFozu6ZN7uijycyMSJTJzIxIlMnGzMZO3atVGf8/jx42c8NqoFpatUVlaqoqIidL21tVU5OTkqLi6Wx+OJ6n0Fg0HV1NRo1o54BTriojp3d+SON1owuoM8PodMnMjEiUycyMTJ5kx2zy2J+pyd74CciagWlMzMTElSU1OTsrKyQtubmpo0YsSI0Jjm5uaw2508eVKHDh0K3f6L3G633G63Y7vL5ZLL5YrS0YcLdMQp0G7XyRJL5OFEJk5k4kQmTmTiZGMmXfH8GsmcUf0dlIEDByozM1O1tbWhba2trdq6dat8Pp8kyefzqaWlRfX19aEx69evV0dHhwoKCqJ5OAAAoJuK+BWUo0ePat++faHr+/fv186dO5Wenq7c3Fw98MADevTRR3XZZZdp4MCBmjVrlrKzs3X77bdLkq644gqNHTtWkydP1pIlSxQMBjVt2jRNmDCBb/AAAABJZ1FQduzYob/+678OXe/8bEhZWZmWL1+uhx9+WMeOHdOUKVPU0tKi66+/XuvWrVNSUlLoNi+++KKmTZumMWPGKD4+XqWlpXrmmWeisBwAANATRFxQbrrpJhlz+q9DxcXFaf78+Zo/f/5px6Snp2vFihWR3jUAALhA8Ld4AACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOtQUAAAgHUoKAAAwDoxLShVVVX62te+pqSkJBUUFGjbtm2xPBwAAGCJmBWU//zP/1RFRYXmzJmj3/3ud7rqqqtUUlKi5ubmWB0SAACwRMwKyhNPPKHJkyfrvvvuU35+vpYsWaKUlBT99Kc/jdUhAQAAS/SKxZ2eOHFC9fX1qqysDG2Lj49XUVGR6urqHOMDgYACgUDo+uHDhyVJhw4dUjAYjOqxBYNBHT9+XL2C8WrviIvq3N1Rrw6j48c7yONzyMSJTJzIxIlMnGzO5OOPP476nEeOHJEkGWO+erCJgY8++shIMps3bw7bPn36dHPttdc6xs+ZM8dI4sKFCxcuXLj0gMuHH374lV0hJq+gRKqyslIVFRWh6x0dHTp06JD69++vuLjoNs7W1lbl5OToww8/lMfjierc3RF5OJGJE5k4kYkTmThdaJkYY3TkyBFlZ2d/5diYFJQBAwYoISFBTU1NYdubmpqUmZnpGO92u+V2u8O2paWldeUhyuPxXBAny5kiDycycSITJzJxIhOnCymT1NTUMxoXkw/JJiYmatSoUaqtrQ1t6+joUG1trXw+XywOCQAAWCRmb/FUVFSorKxMo0eP1rXXXqunnnpKx44d03333RerQwIAAJaIWUG566679Oc//1mzZ8+W3+/XiBEjtG7dOnm93lgdkqTP3k6aM2eO4y2lCxV5OJGJE5k4kYkTmTiRyenFGXMm3/UBAAA4f/hbPAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArENB+Zyqqip97WtfU1JSkgoKCrRt27ZYH1KXmDt3ruLi4sIuQ4YMCe1va2tTeXm5+vfvrz59+qi0tNTxq7+NjY0aP368UlJSlJGRoenTp+vkyZPneylnbdOmTbr11luVnZ2tuLg4vfzyy2H7jTGaPXu2srKylJycrKKiIr3//vthYw4dOqSJEyfK4/EoLS1NkyZN0tGjR8PGvPPOO7rhhhuUlJSknJwcLVq0qKuXdta+KpN7773Xcd6MHTs2bExPy2ThwoW65ppr1LdvX2VkZOj2229XQ0ND2JhoPV42bNigq6++Wm63W4MGDdLy5cu7enkRO5M8brrpJsd58t3vfjdsTE/JQ5IWL16s4cOHh34J1ufz6bXXXgvtv5DOj6iLyl//6wFWrlxpEhMTzU9/+lOzZ88eM3nyZJOWlmaamppifWhRN2fOHHPllVeagwcPhi5//vOfQ/u/+93vmpycHFNbW2t27NhhCgsLzTe+8Y3Q/pMnT5qhQ4eaoqIi8/bbb5u1a9eaAQMGmMrKylgs56ysXbvW/L//9//Mr371KyPJrF69Omz/448/blJTU83LL79sfv/735u/+Zu/MQMHDjSffvppaMzYsWPNVVddZbZs2WL++7//2wwaNMjcfffdof2HDx82Xq/XTJw40ezevdv84he/MMnJyeYnP/nJ+VpmRL4qk7KyMjN27Niw8+bQoUNhY3paJiUlJWbZsmVm9+7dZufOneaWW24xubm55ujRo6Ex0Xi8/OEPfzApKSmmoqLC7N271zz77LMmISHBrFu37ryu96ucSR5/9Vd/ZSZPnhx2nhw+fDi0vyflYYwxv/71r81vfvMb895775mGhgbzyCOPGJfLZXbv3m2MubDOj2ijoPz/rr32WlNeXh663t7ebrKzs83ChQtjeFRdY86cOeaqq6465b6WlhbjcrnMqlWrQtveffddI8nU1dUZYz57IouPjzd+vz80ZvHixcbj8ZhAINClx94Vvvhk3NHRYTIzM82PfvSj0LaWlhbjdrvNL37xC2OMMXv37jWSzPbt20NjXnvtNRMXF2c++ugjY4wxzz33nOnXr19YJjNmzDCDBw/u4hWdu9MVlNtuu+20t+npmRhjTHNzs5FkNm7caIyJ3uPl4YcfNldeeWXYfd11112mpKSkq5d0Tr6YhzGfFZTvf//7p71NT86jU79+/cx//Md/XPDnx7niLR5JJ06cUH19vYqKikLb4uPjVVRUpLq6uhgeWdd5//33lZ2drUsvvVQTJ05UY2OjJKm+vl7BYDAsiyFDhig3NzeURV1dnYYNGxb2q78lJSVqbW3Vnj17zu9CusD+/fvl9/vDMkhNTVVBQUFYBmlpaRo9enRoTFFRkeLj47V169bQmBtvvFGJiYmhMSUlJWpoaNAnn3xynlYTXRs2bFBGRoYGDx6sqVOn6uOPPw7tuxAyOXz4sCQpPT1dUvQeL3V1dWFzdI6x/b8/X8yj04svvqgBAwZo6NChqqys1PHjx0P7enIe7e3tWrlypY4dOyafz3fBnx/nKmY/dW+T//u//1N7e7vjZ/a9Xq/+53/+J0ZH1XUKCgq0fPlyDR48WAcPHtS8efN0ww03aPfu3fL7/UpMTHT8tWiv1yu/3y9J8vv9p8yqc19317mGU63x8xlkZGSE7e/Vq5fS09PDxgwcONAxR+e+fv36dcnxd5WxY8fqzjvv1MCBA/XBBx/okUce0bhx41RXV6eEhIQen0lHR4ceeOABXXfddRo6dKgkRe3xcroxra2t+vTTT5WcnNwVSzonp8pDkv7+7/9eeXl5ys7O1jvvvKMZM2aooaFBv/rVryT1zDx27doln8+ntrY29enTR6tXr1Z+fr527tx5wZ4f0UBBuQCNGzcu9O/hw4eroKBAeXl5+uUvf9ljT3ScuwkTJoT+PWzYMA0fPlxf//rXtWHDBo0ZMyaGR3Z+lJeXa/fu3XrrrbdifShWOF0eU6ZMCf172LBhysrK0pgxY/TBBx/o61//+vk+zPNi8ODB2rlzpw4fPqyXXnpJZWVl2rhxY6wPq9vjLR5JAwYMUEJCguOT1U1NTcrMzIzRUZ0/aWlpuvzyy7Vv3z5lZmbqxIkTamlpCRvz+SwyMzNPmVXnvu6ucw1fdj5kZmaqubk5bP/Jkyd16NChCyanSy+9VAMGDNC+ffsk9exMpk2bpjVr1ujNN9/UJZdcEtoercfL6cZ4PB4r/6fhdHmcSkFBgSSFnSc9LY/ExEQNGjRIo0aN0sKFC3XVVVfp6aefvmDPj2ihoOizk2vUqFGqra0Nbevo6FBtba18Pl8Mj+z8OHr0qD744ANlZWVp1KhRcrlcYVk0NDSosbExlIXP59OuXbvCnoxqamrk8XiUn59/3o8/2gYOHKjMzMywDFpbW7V169awDFpaWlRfXx8as379enV0dIT+g+zz+bRp0yYFg8HQmJqaGg0ePNjqtzLO1J/+9Cd9/PHHysrKktQzMzHGaNq0aVq9erXWr1/veHsqWo8Xn88XNkfnGNv++/NVeZzKzp07JSnsPOkpeZxOR0eHAoHABXd+RF2sP6Vri5UrVxq3222WL19u9u7da6ZMmWLS0tLCPlndU/zgBz8wGzZsMPv37ze//e1vTVFRkRkwYIBpbm42xnz2tbjc3Fyzfv16s2PHDuPz+YzP5wvdvvNrccXFxWbnzp1m3bp15qKLLupWXzM+cuSIefvtt83bb79tJJknnnjCvP322+Z///d/jTGffc04LS3NvPLKK+add94xt9122ym/Zjxy5EizdetW89Zbb5nLLrss7Cu1LS0txuv1mnvuucfs3r3brFy50qSkpFj7ldovy+TIkSPmoYceMnV1dWb//v3mjTfeMFdffbW57LLLTFtbW2iOnpbJ1KlTTWpqqtmwYUPY12aPHz8eGhONx0vn10inT59u3n33XVNVVWXl10i/Ko99+/aZ+fPnmx07dpj9+/ebV155xVx66aXmxhtvDM3Rk/Iwxpgf/vCHZuPGjWb//v3mnXfeMT/84Q9NXFycqa6uNsZcWOdHtFFQPufZZ581ubm5JjEx0Vx77bVmy5YtsT6kLnHXXXeZrKwsk5iYaC6++GJz1113mX379oX2f/rpp+Z73/ue6devn0lJSTF33HGHOXjwYNgcf/zjH824ceNMcnKyGTBggPnBD35ggsHg+V7KWXvzzTeNJMelrKzMGPPZV41nzZplvF6vcbvdZsyYMaahoSFsjo8//tjcfffdpk+fPsbj8Zj77rvPHDlyJGzM73//e3P99dcbt9ttLr74YvP444+fryVG7MsyOX78uCkuLjYXXXSRcblcJi8vz0yePNlR4HtaJqfKQ5JZtmxZaEy0Hi9vvvmmGTFihElMTDSXXnpp2H3Y4qvyaGxsNDfeeKNJT083brfbDBo0yEyfPj3sd1CM6Tl5GGPMP/7jP5q8vDyTmJhoLrroIjNmzJhQOTHmwjo/oi3OGGPO3+s1AAAAX43PoAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdSgoAADAOv8fcneX254gNiQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "for column in df:\n",
    "    plt.figure()\n",
    "    df[column].hist()\n",
    "    plt.title(column)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "Cq1bVaj-C3Ve"
   },
   "outputs": [],
   "source": [
    "X=df.iloc[:,:-1]\n",
    "y=df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "SLwdgUdTHBfm"
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "scaler = preprocessing.StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "ZDGwztq2HLSW"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wNVxosG3eP6n",
    "outputId": "f72d429b-727e-4540-d18f-fe2fb0cae210"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A:\\py\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression: Accuracy - 0.6215\n",
      "Random Forest: Accuracy - 0.9695\n",
      "SVM: Accuracy - 0.6276\n",
      "K-Nearest Neighbors: Accuracy - 0.7350\n",
      "Naive Bayes: Accuracy - 0.6215\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "\n",
    "models = {\n",
    "    'Logistic Regression': LogisticRegression(),\n",
    "    'Random Forest': RandomForestClassifier(),\n",
    "    'SVM': SVC(),\n",
    "    'K-Nearest Neighbors': KNeighborsClassifier(),\n",
    "    'Naive Bayes': GaussianNB(),\n",
    "}\n",
    "\n",
    "# Train and evaluate each model\n",
    "for name, model in models.items():\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    print(f'{name}: Accuracy - {accuracy:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9731379731379731\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model=RandomForestClassifier()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[1014   14]\n",
      " [  30  580]]\n",
      "Accuracy: 0.9731379731379731\n",
      "Precision: 0.9764309764309764\n",
      "Recall: 0.9508196721311475\n",
      "F1 Score: 0.9634551495016611\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Confusion Matrix:\\n{cm}\")\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "print(f\"Precision: {precision}\")\n",
    "print(f\"Recall: {recall}\")\n",
    "print(f\"F1 Score: {f1}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=['Predicted Negative', 'Predicted Positive'], yticklabels=['Actual Negative', 'Actual Positive'])\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}