Undersampling Part-1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('travel_insurance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.select_dtypes(include = ['float64', 'int64']) \n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Duration</th>\n",
       "      <th>Net Sales</th>\n",
       "      <th>Commision (in value)</th>\n",
       "      <th>Age</th>\n",
       "      <th>Claim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61</td>\n",
       "      <td>19.8</td>\n",
       "      <td>11.88</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>93</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>54.5</td>\n",
       "      <td>13.63</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Duration  Net Sales  Commision (in value)  Age  Claim\n",
       "0        61       19.8                 11.88   29      0\n",
       "1        93       63.0                  0.00   36      0\n",
       "2        22       22.0                  0.00   25      0\n",
       "3        14       54.5                 13.63   24      0\n",
       "4        90       10.0                  0.00   23      0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 48260 entries, 0 to 48259\n",
      "Data columns (total 5 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Duration              48260 non-null  int64  \n",
      " 1   Net Sales             48260 non-null  float64\n",
      " 2   Commision (in value)  48260 non-null  float64\n",
      " 3   Age                   48260 non-null  int64  \n",
      " 4   Claim                 48260 non-null  int64  \n",
      "dtypes: float64(2), int64(3)\n",
      "memory usage: 2.2 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    47552\n",
       "1      708\n",
       "Name: Claim, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Claim'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Claim', axis = 1)\n",
    "y = df['Claim']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    31878\n",
       "1      456\n",
       "Name: Claim, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ClusterCentroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.under_sampling import ClusterCentroids\n",
    "undersampler = ClusterCentroids()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_smote, y_smote = undersampler.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    456\n",
       "0    456\n",
       "Name: Claim, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_smote.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Duration</th>\n",
       "      <th>Net Sales</th>\n",
       "      <th>Commision (in value)</th>\n",
       "      <th>Age</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46</td>\n",
       "      <td>22.679381</td>\n",
       "      <td>9.116392e+00</td>\n",
       "      <td>33</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4843</td>\n",
       "      <td>0.292857</td>\n",
       "      <td>8.285714e-02</td>\n",
       "      <td>48</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>372</td>\n",
       "      <td>215.854878</td>\n",
       "      <td>5.396390e+01</td>\n",
       "      <td>60</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>116</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>3.552714e-15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>10.898639</td>\n",
       "      <td>5.672789e-01</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>907</th>\n",
       "      <td>223</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>365</td>\n",
       "      <td>204.600000</td>\n",
       "      <td>1.329900e+02</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>909</th>\n",
       "      <td>68</td>\n",
       "      <td>148.500000</td>\n",
       "      <td>8.910000e+01</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>90</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911</th>\n",
       "      <td>76</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>912 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Duration   Net Sales  Commision (in value)  Age\n",
       "0          46   22.679381          9.116392e+00   33\n",
       "1        4843    0.292857          8.285714e-02   48\n",
       "2         372  215.854878          5.396390e+01   60\n",
       "3         116  117.000000          3.552714e-15   39\n",
       "4          21   10.898639          5.672789e-01   25\n",
       "..        ...         ...                   ...  ...\n",
       "907       223   20.000000          0.000000e+00   36\n",
       "908       365  204.600000          1.329900e+02   35\n",
       "909        68  148.500000          8.910000e+01   33\n",
       "910        90   46.000000          0.000000e+00   36\n",
       "911        76   70.000000          0.000000e+00   36\n",
       "\n",
       "[912 rows x 4 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_smote"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <th>Claim</th>\n",
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       "      <th>2583</th>\n",
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       "      <th>3195</th>\n",
       "      <td>46</td>\n",
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       "      <td>35.64</td>\n",
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       "    <tr>\n",
       "      <th>7561</th>\n",
       "      <td>46</td>\n",
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       "      <th>19499</th>\n",
       "      <td>46</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>34054</th>\n",
       "      <td>46</td>\n",
       "      <td>17.00</td>\n",
       "      <td>4.25</td>\n",
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       "      <th>35424</th>\n",
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       "      <th>40058</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>43255</th>\n",
       "      <td>46</td>\n",
       "      <td>64.35</td>\n",
       "      <td>16.09</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>43484</th>\n",
       "      <td>46</td>\n",
       "      <td>-26.50</td>\n",
       "      <td>6.63</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "       Duration  Net Sales  Commision (in value)  Age  Claim\n",
       "2583         46      10.00                  0.00   33      0\n",
       "3195         46       0.00                 35.64   33      0\n",
       "7561         46      13.00                  0.00   33      0\n",
       "19499        46      10.00                  0.00   33      0\n",
       "34054        46      17.00                  4.25   33      0\n",
       "35424        46     112.00                  0.00   33      0\n",
       "40058        46      10.00                  0.00   33      0\n",
       "43255        46      64.35                 16.09   33      0\n",
       "43484        46     -26.50                  6.63   33      0"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Duration'] == 46) & (df['Age'] == 33) & (df['Claim'] == 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   Duration  Net Sales  Commision (in value)  Age\n",
       "0        46  22.679381              9.116392   33"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_smote[(X_smote['Duration'] == 46) & (X_smote['Age'] == 33)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
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       "      <th>8110</th>\n",
       "      <td>76</td>\n",
       "      <td>70.0</td>\n",
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      "text/plain": [
       "      Duration  Net Sales  Commision (in value)  Age  Claim\n",
       "8110        76       70.0                   0.0   36      1"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Duration'] == 76) & (df['Age'] == 36) & (df['Claim'] == 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "     Duration  Net Sales  Commision (in value)  Age\n",
       "911        76       70.0                   0.0   36"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_smote[(X_smote['Duration'] == 76) & (X_smote['Age'] == 36)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RandomUnderSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    31878\n",
       "1      456\n",
       "Name: Claim, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop('Claim', axis = 1)\n",
    "y = df['Claim']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
    "\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "under = RandomUnderSampler(sampling_strategy=1) \n",
    "                     \n",
    "X_smote, y_smote = under.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    456\n",
       "0    456\n",
       "Name: Claim, dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_smote.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    31878\n",
       "1      456\n",
       "Name: Claim, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NearMiss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    31878\n",
       "1      456\n",
       "Name: Claim, dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop('Claim', axis = 1)\n",
    "y = df['Claim']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
    "\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.under_sampling import NearMiss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "undersampler = NearMiss(version = 1, n_neighbors = 3) \n",
    "                     \n",
    "X_smote, y_smote = undersampler.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    456\n",
       "0    456\n",
       "Name: Claim, dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_smote.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    31878\n",
       "1      456\n",
       "Name: Claim, dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  }
 ],
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