Live Discussion On Handling Imbalanced Dataset- Machine Learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6b5d79e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "adbdab48",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
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       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
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       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
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       "      <th>1</th>\n",
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       "      <td>0.166480</td>\n",
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       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
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       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
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       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
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       "      <th>2</th>\n",
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       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 31 columns</p>\n",
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      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
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       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"creditcard.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6b56f2a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Time    284807 non-null  float64\n",
      " 1   V1      284807 non-null  float64\n",
      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
      " 6   V6      284807 non-null  float64\n",
      " 7   V7      284807 non-null  float64\n",
      " 8   V8      284807 non-null  float64\n",
      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
      " 12  V12     284807 non-null  float64\n",
      " 13  V13     284807 non-null  float64\n",
      " 14  V14     284807 non-null  float64\n",
      " 15  V15     284807 non-null  float64\n",
      " 16  V16     284807 non-null  float64\n",
      " 17  V17     284807 non-null  float64\n",
      " 18  V18     284807 non-null  float64\n",
      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
      " 23  V23     284807 non-null  float64\n",
      " 24  V24     284807 non-null  float64\n",
      " 25  V25     284807 non-null  float64\n",
      " 26  V26     284807 non-null  float64\n",
      " 27  V27     284807 non-null  float64\n",
      " 28  V28     284807 non-null  float64\n",
      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "67c82310",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.139097</td>\n",
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       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
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       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
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       "      <td>0.095921</td>\n",
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       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
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      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "11899a8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      0\n",
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       "V7        0\n",
       "V8        0\n",
       "V9        0\n",
       "V10       0\n",
       "V11       0\n",
       "V12       0\n",
       "V13       0\n",
       "V14       0\n",
       "V15       0\n",
       "V16       0\n",
       "V17       0\n",
       "V18       0\n",
       "V19       0\n",
       "V20       0\n",
       "V21       0\n",
       "V22       0\n",
       "V23       0\n",
       "V24       0\n",
       "V25       0\n",
       "V26       0\n",
       "V27       0\n",
       "V28       0\n",
       "Amount    0\n",
       "Class     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6714a3b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Class\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53dd3f71",
   "metadata": {},
   "source": [
    "## Over sampling and Under sampling\n",
    "\n",
    "1. Oversampling — Duplicating samples from the minority class.\n",
    "\n",
    "2. Undersampling — Deleting samples from the majority class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "556265a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.drop(\"Class\",axis=1)\n",
    "y=df.Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2afaf6b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0\n",
       "1         0\n",
       "2         0\n",
       "3         0\n",
       "4         0\n",
       "         ..\n",
       "284802    0\n",
       "284803    0\n",
       "284804    0\n",
       "284805    0\n",
       "284806    0\n",
       "Name: Class, Length: 284807, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fa85551d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9c83cda5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "23daafad",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_class=LogisticRegression()\n",
    "grid={\"C\":10.0**np.arange(-2,3),'penalty':['l1','l2']}\n",
    "cv=KFold(n_splits=5,shuffle=False,random_state=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cddf04aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KFold(n_splits=5, random_state=None, shuffle=False)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4bf1b1bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bipla\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:918: UserWarning: One or more of the test scores are non-finite: [       nan 0.82335184        nan 0.83825353        nan 0.84036735\n",
      "        nan 0.83597939        nan 0.84064032]\n",
      "  warnings.warn(\n",
      "C:\\Users\\bipla\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: 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": [
       "GridSearchCV(cv=KFold(n_splits=5, random_state=None, shuffle=False),\n",
       "             estimator=LogisticRegression(), n_jobs=-1,\n",
       "             param_grid={'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02]),\n",
       "                         'penalty': ['l1', 'l2']},\n",
       "             scoring='f1_macro')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf=GridSearchCV(log_class,grid,cv=cv,n_jobs=-1,scoring='f1_macro')\n",
    "clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8cc5d38f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[85265    44]\n",
      " [   52    82]]\n",
      "0.9988764439450862\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85309\n",
      "           1       0.65      0.61      0.63       134\n",
      "\n",
      "    accuracy                           1.00     85443\n",
      "   macro avg       0.83      0.81      0.82     85443\n",
      "weighted avg       1.00      1.00      1.00     85443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred=clf.predict(x_test)\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(accuracy_score(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f8f0c5e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classfier=RandomForestClassifier()\n",
    "classfier.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a824eba5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[85303     6]\n",
      " [   33   101]]\n",
      "0.9995435553526912\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85309\n",
      "           1       0.94      0.75      0.84       134\n",
      "\n",
      "    accuracy                           1.00     85443\n",
      "   macro avg       0.97      0.88      0.92     85443\n",
      "weighted avg       1.00      1.00      1.00     85443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred=classfier.predict(x_test)\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(accuracy_score(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76c675a9",
   "metadata": {},
   "source": [
    "## Under sampling => Remove/Reduce the data points belonging to the majority class \n",
    "\n",
    "preferable used for small sample set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "41247880",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bipla\\anaconda3\\lib\\site-packages\\imblearn\\utils\\_validation.py:587: FutureWarning: Pass sampling_strategy=0.8 as keyword args. From version 0.9 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before under sampling: Y value Counter({0: 199006, 1: 358})\n",
      "after under sampling: Y value Counter({0: 447, 1: 358})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "from imblearn.under_sampling import NearMiss\n",
    "ns=NearMiss(.8)\n",
    "x_train_ns,y_train_ns=ns.fit_resample(x_train,y_train)\n",
    "print(\"before under sampling: Y value\",format(Counter(y_train)))\n",
    "print(\"after under sampling: Y value\",format(Counter(y_train_ns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f6c0f46b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## checking the result after under sampling\n",
    "classfier=RandomForestClassifier()\n",
    "classfier.fit(x_train_ns,y_train_ns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b37c083e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[74280 11029]\n",
      " [   14   120]]\n",
      "0.8707559425581967\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.87      0.93     85309\n",
      "           1       0.01      0.90      0.02       134\n",
      "\n",
      "    accuracy                           0.87     85443\n",
      "   macro avg       0.51      0.88      0.48     85443\n",
      "weighted avg       1.00      0.87      0.93     85443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred=classfier.predict(x_test)\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(accuracy_score(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49062042",
   "metadata": {},
   "source": [
    "## Over sampling => Increasing the data points belonging to the minority class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "deea6e3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bipla\\anaconda3\\lib\\site-packages\\imblearn\\utils\\_validation.py:587: FutureWarning: Pass sampling_strategy=0.5 as keyword args. From version 0.9 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before over sampling: Y value Counter({0: 199006, 1: 358})\n",
      "after over sampling: Y value Counter({0: 199006, 1: 99503})\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "os=RandomOverSampler(.5)\n",
    "x_train_os,y_train_os=os.fit_resample(x_train,y_train)\n",
    "print(\"before over sampling: Y value\",format(Counter(y_train)))\n",
    "print(\"after over sampling: Y value\",format(Counter(y_train_os)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "00b8e120",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[85304     5]\n",
      " [   29   105]]\n",
      "0.999602073897218\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85309\n",
      "           1       0.95      0.78      0.86       134\n",
      "\n",
      "    accuracy                           1.00     85443\n",
      "   macro avg       0.98      0.89      0.93     85443\n",
      "weighted avg       1.00      1.00      1.00     85443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## checking the result after over sampling\n",
    "classfier=RandomForestClassifier()\n",
    "classfier.fit(x_train_os,y_train_os)\n",
    "y_pred=classfier.predict(x_test)\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(accuracy_score(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a16e53e",
   "metadata": {},
   "source": [
    "## SMOTE\n",
    "\n",
    "SMOTE first selects a minority class instance a at random and \n",
    "finds its k nearest minority class neighbors.\n",
    "The synthetic instance is then created by choosing one of the k nearest neighbors b at random and\n",
    "connecting a and b to form a line segment in the feature space.\n",
    "The synthetic instances are generated as a convex combination of the two chosen instances a and b.\n",
    "\n",
    "The combination of SMOTE and under-sampling performs better than plain under-sampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8a5bd79c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.combine import SMOTETomek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2371b654",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bipla\\anaconda3\\lib\\site-packages\\imblearn\\utils\\_validation.py:587: FutureWarning: Pass sampling_strategy=0.75 as keyword args. From version 0.9 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before smote sampling: Y value Counter({0: 199006, 1: 358})\n",
      "after smote sampling: Y value Counter({0: 198312, 1: 148560})\n"
     ]
    }
   ],
   "source": [
    "cs=SMOTETomek(.75)\n",
    "x_train_cs,y_train_cs=cs.fit_resample(x_train,y_train)\n",
    "print(\"before smote sampling: Y value\",format(Counter(y_train)))\n",
    "print(\"after smote sampling: Y value\",format(Counter(y_train_cs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cad40dd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "## checking the result after smote sampling\n",
    "classfier=RandomForestClassifier()\n",
    "classfier.fit(x_train_cs,y_train_cs)\n",
    "y_pred=classfier.predict(x_test)\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(accuracy_score(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ab7933d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a2c07bd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8817861b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2b488bf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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