Untitled4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.svm import LinearSVC\n",
    "#from imblearn.combine import SMOTEENN\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, roc_auc_score,auc\n",
    "from sklearn.svm import SVC\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "from collections import Counter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   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  \\\n",
      "0  0.098698  0.363787  ...   -0.018307  0.277838 -0.110474  0.066928   \n",
      "1  0.085102 -0.255425  ...   -0.225775 -0.638672  0.101288 -0.339846   \n",
      "2  0.247676 -1.514654  ...    0.247998  0.771679  0.909412 -0.689281   \n",
      "3  0.377436 -1.387024  ...   -0.108300  0.005274 -0.190321 -1.175575   \n",
      "4 -0.270533  0.817739  ...   -0.009431  0.798278 -0.137458  0.141267   \n",
      "\n",
      "        V25       V26       V27       V28  Amount  Class  \n",
      "0  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
      "1  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
      "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
      "3  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
      "4 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
      "\n",
      "[5 rows x 31 columns]\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv(\"creditcard.csv\")\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
      "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
      "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
      "       'Class'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "V1, V2, ... V28 are the principal components obtained with PCA\n",
    "\n",
    "Since almost all the variables in the dataset are the Pricipal Components derived from the original data, we will not get into any exploratory or descriptive analysis for the simple reason that we do not know what the variables stand for!\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(284807, 31)\n",
      "0    284315\n",
      "1       492\n",
      "Name: Class, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "print(pd.value_counts(df[\"Class\"]).sort_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check for Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time      0\n",
      "V1        0\n",
      "V2        0\n",
      "V3        0\n",
      "V4        0\n",
      "V5        0\n",
      "V6        0\n",
      "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\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Time            V1            V2            V3            V4  \\\n",
      "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
      "mean    94813.859575  3.919560e-15  5.688174e-16 -8.769071e-15  2.782312e-15   \n",
      "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
      "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
      "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
      "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
      "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
      "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
      "\n",
      "                 V5            V6            V7            V8            V9  \\\n",
      "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
      "mean  -1.552563e-15  2.010663e-15 -1.694249e-15 -1.927028e-16 -3.137024e-15   \n",
      "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
      "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
      "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
      "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
      "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
      "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
      "\n",
      "           ...                 V21           V22           V23           V24  \\\n",
      "count      ...        2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
      "mean       ...        1.537294e-16  7.959909e-16  5.367590e-16  4.458112e-15   \n",
      "std        ...        7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
      "min        ...       -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
      "25%        ...       -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
      "50%        ...       -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
      "75%        ...        1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
      "max        ...        2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
      "\n",
      "                V25           V26           V27           V28         Amount  \\\n",
      "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
      "mean   1.453003e-15  1.699104e-15 -3.660161e-16 -1.206049e-16      88.349619   \n",
      "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
      "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
      "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
      "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
      "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
      "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
      "\n",
      "               Class  \n",
      "count  284807.000000  \n",
      "mean        0.001727  \n",
      "std         0.041527  \n",
      "min         0.000000  \n",
      "25%         0.000000  \n",
      "50%         0.000000  \n",
      "75%         0.000000  \n",
      "max         1.000000  \n",
      "\n",
      "[8 rows x 31 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28481, 31)\n"
     ]
    }
   ],
   "source": [
    "data=df.sample(frac=0.1,random_state=1)\n",
    "print(data.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(figsize=(20,20))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49\n",
      "28432\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c819698320>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Fraud=data[data[\"Class\"]==1]\n",
    "Valid=data[data[\"Class\"]==0]\n",
    "print(len(Fraud))\n",
    "print(len(Valid))\n",
    "import seaborn as sns\n",
    "sns.countplot(x='Class', data=data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Time        V1        V2        V3        V4        V5        V6  \\\n",
      "169876  119907.0 -0.611712 -0.769705 -0.149759 -0.224877  2.028577 -2.019887   \n",
      "127467   78340.0 -0.814682  1.319219  1.329415  0.027273 -0.284871 -0.653985   \n",
      "137900   82382.0 -0.318193  1.118618  0.969864 -0.127052  0.569563 -0.532484   \n",
      "21513    31717.0 -1.328271  1.018378  1.775426 -1.574193 -0.117696 -0.457733   \n",
      "134700   80923.0  1.276712  0.617120 -0.578014  0.879173  0.061706 -1.472002   \n",
      "\n",
      "              V7        V8        V9  ...         V21       V22       V23  \\\n",
      "169876  0.292491 -0.523020  0.358468  ...   -0.075208  0.045536  0.380739   \n",
      "127467  0.321552  0.435975 -0.704298  ...   -0.128619 -0.368565  0.090660   \n",
      "137900  0.706252 -0.064966 -0.463271  ...   -0.305402 -0.774704 -0.123884   \n",
      "21513   0.681867 -0.031641  0.383872  ...   -0.220815 -0.419013 -0.239197   \n",
      "134700  0.373692 -0.287204 -0.084482  ...   -0.160161 -0.430404 -0.076738   \n",
      "\n",
      "             V24       V25       V26       V27       V28  Amount  Class  \n",
      "169876  0.023440 -2.220686 -0.201146  0.066501  0.221180    1.79      0  \n",
      "127467  0.401147 -0.261034  0.080621  0.162427  0.059456    1.98      0  \n",
      "137900 -0.495687 -0.018148  0.121679  0.249050  0.092516    0.89      0  \n",
      "21513   0.009967  0.232829  0.814177  0.098797 -0.004273   15.98      0  \n",
      "134700  0.258708  0.552170  0.370701 -0.034255  0.041709    0.76      0  \n",
      "\n",
      "[5 rows x 31 columns]\n",
      "         V1        V2        V3        V4        V5        V6        V7  \\\n",
      "0 -0.611712 -0.769705 -0.149759 -0.224877  2.028577 -2.019887  0.292491   \n",
      "1 -0.814682  1.319219  1.329415  0.027273 -0.284871 -0.653985  0.321552   \n",
      "2 -0.318193  1.118618  0.969864 -0.127052  0.569563 -0.532484  0.706252   \n",
      "3 -1.328271  1.018378  1.775426 -1.574193 -0.117696 -0.457733  0.681867   \n",
      "4  1.276712  0.617120 -0.578014  0.879173  0.061706 -1.472002  0.373692   \n",
      "\n",
      "         V8        V9       V10  ...         V20       V21       V22  \\\n",
      "0 -0.523020  0.358468  0.070050  ...   -0.196039 -0.075208  0.045536   \n",
      "1  0.435975 -0.704298 -0.600684  ...   -0.009041 -0.128619 -0.368565   \n",
      "2 -0.064966 -0.463271 -0.528357  ...    0.206028 -0.305402 -0.774704   \n",
      "3 -0.031641  0.383872  0.334853  ...    0.315823 -0.220815 -0.419013   \n",
      "4 -0.287204 -0.084482 -0.696578  ...   -0.105286 -0.160161 -0.430404   \n",
      "\n",
      "        V23       V24       V25       V26       V27       V28  Class  \n",
      "0  0.380739  0.023440 -2.220686 -0.201146  0.066501  0.221180      0  \n",
      "1  0.090660  0.401147 -0.261034  0.080621  0.162427  0.059456      0  \n",
      "2 -0.123884 -0.495687 -0.018148  0.121679  0.249050  0.092516      0  \n",
      "3 -0.239197  0.009967  0.232829  0.814177  0.098797 -0.004273      0  \n",
      "4 -0.076738  0.258708  0.552170  0.370701 -0.034255  0.041709      0  \n",
      "\n",
      "[5 rows x 29 columns]\n",
      "(28481, 28)\n",
      "(28481, 1)\n"
     ]
    }
   ],
   "source": [
    "print(data.head())\n",
    "\n",
    "data=data.reset_index()\n",
    "data=data.drop([\"index\"],axis=1)\n",
    "cols = list(data.columns.values)\n",
    "data = data[['V1', 'V2', 'V3', 'V4', 'V5','V6','V7','V8','V9','V10','V11','V12','V13','V14','V15','V16','V17','V18','V19','V20','V21','V22','V23','V24','V25','V26','V27','V28','Class']]\n",
    "print(data.head())\n",
    "\n",
    "X = data.iloc[:, data.columns != 'Class']\n",
    "Y = data.iloc[:, data.columns == 'Class']\n",
    "\n",
    "print(X.shape)\n",
    "print(Y.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda31\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00      2847\n",
      "          1       0.50      0.50      0.50         2\n",
      "\n",
      "avg / total       1.00      1.00      1.00      2849\n",
      "\n",
      "[[2846    1]\n",
      " [   1    1]]\n"
     ]
    }
   ],
   "source": [
    "x_train, x_val, y_train, y_val = train_test_split(X, Y,test_size = 0.1,random_state=0)\n",
    "\n",
    "logR= LogisticRegression()\n",
    "\n",
    "logR.fit(x_train,y_train)\n",
    "\n",
    "predictR = logR.predict(x_val)\n",
    "\n",
    "print(classification_report(y_val,predictR))\n",
    "print(confusion_matrix(y_val,predictR))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda31\\lib\\site-packages\\ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00      2847\n",
      "          1       1.00      1.00      1.00         2\n",
      "\n",
      "avg / total       1.00      1.00      1.00      2849\n",
      "\n",
      "[[2847    0]\n",
      " [   0    2]]\n"
     ]
    }
   ],
   "source": [
    "logR= RandomForestClassifier(random_state=14,n_estimators=100)\n",
    "logR.fit(x_train,y_train)\n",
    "predictR = logR.predict(x_val)\n",
    "print(classification_report(y_val,predictR))\n",
    "print(confusion_matrix(y_val,predictR))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda31\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00      2847\n",
      "          1       1.00      0.50      0.67         2\n",
      "\n",
      "avg / total       1.00      1.00      1.00      2849\n",
      "\n",
      "[[2847    0]\n",
      " [   1    1]]\n"
     ]
    }
   ],
   "source": [
    "sv=SVC(kernel='rbf')\n",
    "sv.fit(x_train,y_train)\n",
    "predictR = sv.predict(x_val)\n",
    "print(classification_report(y_val,predictR))\n",
    "print(confusion_matrix(y_val,predictR))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# UnderSampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda31\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\hp\\Anaconda31\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       1.00      0.32      1.00      0.49      0.57      0.30      2847\n",
      "          1       0.00      1.00      0.32      0.00      0.57      0.34         2\n",
      "\n",
      "avg / total       1.00      0.32      1.00      0.48      0.57      0.30      2849\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from imblearn.under_sampling import NearMiss\n",
    "from imblearn.pipeline import make_pipeline\n",
    "\n",
    "# Create a pipeline\n",
    "pipeline = make_pipeline(NearMiss(version=2, random_state=14),\n",
    "                         LinearSVC(random_state=14))\n",
    "pipeline.fit(x_train, y_train)\n",
    "\n",
    "# Classify and report the results\n",
    "print(classification_report_imbalanced(y_val, pipeline.predict(x_val)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OverSampling\n",
    "One way to fight this issue is to generate new samples in the classes which are under-represented. The most naive strategy is to generate new samples by randomly sampling with replacement the current available samples.It's duplicating some of the original samples of minority class.\n",
    "\n",
    "### RandomOverSampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda31\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\hp\\Anaconda31\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 25585), (1, 25585)]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.96      0.98      2847\n",
      "          1       0.02      1.00      0.04         2\n",
      "\n",
      "avg / total       1.00      0.96      0.98      2849\n",
      "\n",
      "[[2744  103]\n",
      " [   0    2]]\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "ros = RandomOverSampler(random_state=0)\n",
    "X_resampled, y_resampled = ros.fit_sample(x_train, y_train)\n",
    "from collections import Counter\n",
    "print(sorted(Counter(y_resampled).items()))\n",
    "from sklearn.svm import LinearSVC\n",
    "clf = LinearSVC()\n",
    "clf.fit(X_resampled, y_resampled) \n",
    "clf.predict(x_val)\n",
    "predictR = clf.predict(x_val)\n",
    "print(classification_report(y_val,predictR))\n",
    "print(confusion_matrix(y_val,predictR))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Smote\n",
    "This technique works by not only oversampling the minority class (fraud cases), but also undersampling the majority class (non-fraud cases). It generates new samples by interpolation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda31\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\hp\\Anaconda31\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resampled dataset shape Counter({0: 25585, 1: 25585})\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.97      0.98      2847\n",
      "          1       0.02      1.00      0.04         2\n",
      "\n",
      "avg / total       1.00      0.97      0.98      2849\n",
      "\n",
      "[[2753   94]\n",
      " [   0    2]]\n"
     ]
    }
   ],
   "source": [
    "sm = SMOTE(random_state=12, ratio = 'auto', kind = 'regular')\n",
    "\n",
    "x_train_res, y_train_res = sm.fit_sample(x_train, y_train)\n",
    "\n",
    "print('Resampled dataset shape {}'.format(Counter(y_train_res)))\n",
    "\n",
    "clf = LinearSVC()\n",
    "clf.fit(x_train_res, y_train_res) \n",
    "clf.predict(x_val)\n",
    "predictR = clf.predict(x_val)\n",
    "print(classification_report(y_val,predictR))\n",
    "print(confusion_matrix(y_val,predictR))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combination Of Under And Over Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda31\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\hp\\Anaconda31\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 25520), (1, 25585)]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.97      0.98      2847\n",
      "          1       0.02      1.00      0.04         2\n",
      "\n",
      "avg / total       1.00      0.97      0.98      2849\n",
      "\n",
      "[[2762   85]\n",
      " [   0    2]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda31\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\hp\\Anaconda31\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 25585), (1, 25585)]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.97      0.98      2847\n",
      "          1       0.02      1.00      0.04         2\n",
      "\n",
      "avg / total       1.00      0.97      0.98      2849\n",
      "\n",
      "[[2752   95]\n",
      " [   0    2]]\n"
     ]
    }
   ],
   "source": [
    "from imblearn.combine import SMOTEENN\n",
    "smote_enn = SMOTEENN(random_state=0)\n",
    "X_resampled, y_resampled = smote_enn.fit_sample(x_train, y_train)\n",
    "print(sorted(Counter(y_resampled).items()))\n",
    "\n",
    "clf = LinearSVC()\n",
    "clf.fit(X_resampled, y_resampled) \n",
    "clf.predict(x_val)\n",
    "predictR = clf.predict(x_val)\n",
    "print(classification_report(y_val,predictR))\n",
    "print(confusion_matrix(y_val,predictR))\n",
    "\n",
    "from imblearn.combine import SMOTETomek\n",
    "smote_tomek = SMOTETomek(random_state=0)\n",
    "X_resampled, y_resampled = smote_tomek.fit_sample(x_train, y_train)\n",
    "print(sorted(Counter(y_resampled).items()))\n",
    "\n",
    "clf = LinearSVC()\n",
    "clf.fit(X_resampled, y_resampled)\n",
    "clf.predict(x_val)\n",
    "predictR = clf.predict(x_val)\n",
    "print(classification_report(y_val,predictR))\n",
    "print(confusion_matrix(y_val,predictR))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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