diff --git a/sravansmart7733@gmail.com/Classification-Imbalance.ipynb b/sravansmart7733@gmail.com/Classification-Imbalance.ipynb
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@@ -0,0 +1,944 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
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+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Time</th>\n",
+       "      <th>V1</th>\n",
+       "      <th>V2</th>\n",
+       "      <th>V3</th>\n",
+       "      <th>V4</th>\n",
+       "      <th>V5</th>\n",
+       "      <th>V6</th>\n",
+       "      <th>V7</th>\n",
+       "      <th>V8</th>\n",
+       "      <th>V9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>V21</th>\n",
+       "      <th>V22</th>\n",
+       "      <th>V23</th>\n",
+       "      <th>V24</th>\n",
+       "      <th>V25</th>\n",
+       "      <th>V26</th>\n",
+       "      <th>V27</th>\n",
+       "      <th>V28</th>\n",
+       "      <th>Amount</th>\n",
+       "      <th>Class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.359807</td>\n",
+       "      <td>-0.072781</td>\n",
+       "      <td>2.536347</td>\n",
+       "      <td>1.378155</td>\n",
+       "      <td>-0.338321</td>\n",
+       "      <td>0.462388</td>\n",
+       "      <td>0.239599</td>\n",
+       "      <td>0.098698</td>\n",
+       "      <td>0.363787</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.018307</td>\n",
+       "      <td>0.277838</td>\n",
+       "      <td>-0.110474</td>\n",
+       "      <td>0.066928</td>\n",
+       "      <td>0.128539</td>\n",
+       "      <td>-0.189115</td>\n",
+       "      <td>0.133558</td>\n",
+       "      <td>-0.021053</td>\n",
+       "      <td>149.62</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.191857</td>\n",
+       "      <td>0.266151</td>\n",
+       "      <td>0.166480</td>\n",
+       "      <td>0.448154</td>\n",
+       "      <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",
+       "      <td>...</td>\n",
+       "      <td>-0.225775</td>\n",
+       "      <td>-0.638672</td>\n",
+       "      <td>0.101288</td>\n",
+       "      <td>-0.339846</td>\n",
+       "      <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",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-1.358354</td>\n",
+       "      <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",
+       "    </tr>\n",
+       "    <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",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 31 columns</p>\n",
+       "</div>"
+      ],
+      "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": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np # linear algebra\n",
+    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
+    "import tensorflow as tf\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.manifold import TSNE\n",
+    "from sklearn.decomposition import PCA, TruncatedSVD\n",
+    "import matplotlib.patches as mpatches\n",
+    "import time\n",
+    "\n",
+    "# Classifier Libraries\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "import collections\n",
+    "\n",
+    "\n",
+    "# Other Libraries\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from imblearn.pipeline import make_pipeline as imbalanced_make_pipeline\n",
+    "from imblearn.over_sampling import SMOTE\n",
+    "from imblearn.under_sampling import NearMiss\n",
+    "from imblearn.metrics import classification_report_imbalanced\n",
+    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report\n",
+    "from collections import Counter\n",
+    "from sklearn.model_selection import KFold, StratifiedKFold\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "\n",
+    "df = pd.read_csv('D:/creditcard.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No Frauds 99.83 % of the dataset\n",
+      "Frauds 0.17 % of the dataset\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('No Frauds', round(df['Class'].value_counts()[0]/len(df) * 100,2), '% of the dataset')\n",
+    "print('Frauds', round(df['Class'].value_counts()[1]/len(df) * 100,2), '% of the dataset')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Class Distributions \\n (0: No Fraud || 1: Fraud)')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "colors = [\"#0101DF\", \"#DF0101\"]\n",
+    "\n",
+    "sns.countplot('Class', data=df, palette=colors)\n",
+    "plt.title('Class Distributions \\n (0: No Fraud || 1: Fraud)', fontsize=14)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Separate input features and target\n",
+    "y = df.Class\n",
+    "X = df.drop('Class', axis=1)\n",
+    "\n",
+    "# setting up testing and training sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=27)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy Score :  99.92135052386169 %\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0    71108\n",
+       "1       94\n",
+       "Name: 0, dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr = LogisticRegression(solver='liblinear').fit(X_train, y_train)\n",
+    " \n",
+    "# Predict on training set\n",
+    "lr_pred = lr.predict(X_test)\n",
+    "\n",
+    "# Checking accuracy\n",
+    "a = accuracy_score(y_test, lr_pred)\n",
+    "print(\"Accuracy Score : \", a*100,\"%\")\n",
+    "\n",
+    "\n",
+    "# Checking unique values\n",
+    "predictions = pd.DataFrame(lr_pred)\n",
+    "predictions[0].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Score :  0.7522123893805309\n",
+      "Recall Score :  0.6439393939393939\n"
+     ]
+    }
+   ],
+   "source": [
+    "# f1 score\n",
+    "print(\"F1 Score : \", f1_score(y_test, lr_pred))\n",
+    "\n",
+    "# recall score\n",
+    "print(\"Recall Score : \", recall_score(y_test, lr_pred))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "# train model\n",
+    "rfc = RandomForestClassifier(n_estimators=10).fit(X_train, y_train)\n",
+    "\n",
+    "# predict on test set\n",
+    "rfc_pred = rfc.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9995365298727564"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_test, rfc_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 score :  0.8607594936708862\n",
+      "Recall score :  0.7727272727272727\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"F1 score : \",f1_score(y_test, rfc_pred))\n",
+    "\n",
+    "print(\"Recall score : \", recall_score(y_test, rfc_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy Score :  99.7415802926884\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "\n",
+    "gb_clf = GradientBoostingClassifier(n_estimators=20, max_features=2, max_depth=2, random_state=0)\n",
+    "gb = gb_clf.fit(X_train, y_train)\n",
+    "gb_pred = gb.predict(X_test)\n",
+    "\n",
+    "print(\"Accuracy Score : \", accuracy_score(y_test, gb_pred)*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 score :  0.17117117117117117\n",
+      "Recall score :  0.14393939393939395\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"F1 score : \",f1_score(y_test, gb_pred))\n",
+    "\n",
+    "print(\"Recall score : \", recall_score(y_test, gb_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Time</th>\n",
+       "      <th>V1</th>\n",
+       "      <th>V2</th>\n",
+       "      <th>V3</th>\n",
+       "      <th>V4</th>\n",
+       "      <th>V5</th>\n",
+       "      <th>V6</th>\n",
+       "      <th>V7</th>\n",
+       "      <th>V8</th>\n",
+       "      <th>V9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>V21</th>\n",
+       "      <th>V22</th>\n",
+       "      <th>V23</th>\n",
+       "      <th>V24</th>\n",
+       "      <th>V25</th>\n",
+       "      <th>V26</th>\n",
+       "      <th>V27</th>\n",
+       "      <th>V28</th>\n",
+       "      <th>Amount</th>\n",
+       "      <th>Class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>59318</th>\n",
+       "      <td>48789.0</td>\n",
+       "      <td>-0.892344</td>\n",
+       "      <td>1.725185</td>\n",
+       "      <td>2.014072</td>\n",
+       "      <td>2.969493</td>\n",
+       "      <td>-0.358103</td>\n",
+       "      <td>0.208247</td>\n",
+       "      <td>0.173195</td>\n",
+       "      <td>0.508835</td>\n",
+       "      <td>-1.421750</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.219772</td>\n",
+       "      <td>-0.467348</td>\n",
+       "      <td>-0.120825</td>\n",
+       "      <td>0.396310</td>\n",
+       "      <td>0.197382</td>\n",
+       "      <td>0.121402</td>\n",
+       "      <td>0.220645</td>\n",
+       "      <td>0.091821</td>\n",
+       "      <td>8.27</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74794</th>\n",
+       "      <td>55760.0</td>\n",
+       "      <td>-6.003422</td>\n",
+       "      <td>-3.930731</td>\n",
+       "      <td>-0.007045</td>\n",
+       "      <td>1.714669</td>\n",
+       "      <td>3.414667</td>\n",
+       "      <td>-2.329583</td>\n",
+       "      <td>-1.901512</td>\n",
+       "      <td>-2.746111</td>\n",
+       "      <td>0.887673</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.101671</td>\n",
+       "      <td>-0.992494</td>\n",
+       "      <td>-0.698259</td>\n",
+       "      <td>0.139898</td>\n",
+       "      <td>-0.205151</td>\n",
+       "      <td>-0.472412</td>\n",
+       "      <td>1.775378</td>\n",
+       "      <td>-0.104285</td>\n",
+       "      <td>311.91</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36734</th>\n",
+       "      <td>38667.0</td>\n",
+       "      <td>1.179743</td>\n",
+       "      <td>-1.164141</td>\n",
+       "      <td>1.015352</td>\n",
+       "      <td>-0.405885</td>\n",
+       "      <td>-1.850985</td>\n",
+       "      <td>-0.503236</td>\n",
+       "      <td>-1.136412</td>\n",
+       "      <td>0.074776</td>\n",
+       "      <td>0.133414</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.344607</td>\n",
+       "      <td>0.785058</td>\n",
+       "      <td>-0.164374</td>\n",
+       "      <td>0.411598</td>\n",
+       "      <td>0.369617</td>\n",
+       "      <td>-0.051243</td>\n",
+       "      <td>0.022254</td>\n",
+       "      <td>0.038167</td>\n",
+       "      <td>101.37</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120837</th>\n",
+       "      <td>75978.0</td>\n",
+       "      <td>-5.140723</td>\n",
+       "      <td>3.568751</td>\n",
+       "      <td>-5.896245</td>\n",
+       "      <td>4.164720</td>\n",
+       "      <td>-4.091193</td>\n",
+       "      <td>-1.989960</td>\n",
+       "      <td>-5.472436</td>\n",
+       "      <td>2.422821</td>\n",
+       "      <td>-2.909735</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.131130</td>\n",
+       "      <td>0.118022</td>\n",
+       "      <td>-0.332704</td>\n",
+       "      <td>0.139941</td>\n",
+       "      <td>0.324758</td>\n",
+       "      <td>-0.180769</td>\n",
+       "      <td>0.177810</td>\n",
+       "      <td>0.661555</td>\n",
+       "      <td>99.90</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6427</th>\n",
+       "      <td>7610.0</td>\n",
+       "      <td>0.725646</td>\n",
+       "      <td>2.300894</td>\n",
+       "      <td>-5.329976</td>\n",
+       "      <td>4.007683</td>\n",
+       "      <td>-1.730411</td>\n",
+       "      <td>-1.732193</td>\n",
+       "      <td>-3.968593</td>\n",
+       "      <td>1.063728</td>\n",
+       "      <td>-0.486097</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.589669</td>\n",
+       "      <td>0.109541</td>\n",
+       "      <td>0.601045</td>\n",
+       "      <td>-0.364700</td>\n",
+       "      <td>-1.843078</td>\n",
+       "      <td>0.351909</td>\n",
+       "      <td>0.594550</td>\n",
+       "      <td>0.099372</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 31 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Time        V1        V2        V3        V4        V5        V6  \\\n",
+       "59318   48789.0 -0.892344  1.725185  2.014072  2.969493 -0.358103  0.208247   \n",
+       "74794   55760.0 -6.003422 -3.930731 -0.007045  1.714669  3.414667 -2.329583   \n",
+       "36734   38667.0  1.179743 -1.164141  1.015352 -0.405885 -1.850985 -0.503236   \n",
+       "120837  75978.0 -5.140723  3.568751 -5.896245  4.164720 -4.091193 -1.989960   \n",
+       "6427     7610.0  0.725646  2.300894 -5.329976  4.007683 -1.730411 -1.732193   \n",
+       "\n",
+       "              V7        V8        V9  ...       V21       V22       V23  \\\n",
+       "59318   0.173195  0.508835 -1.421750  ... -0.219772 -0.467348 -0.120825   \n",
+       "74794  -1.901512 -2.746111  0.887673  ...  1.101671 -0.992494 -0.698259   \n",
+       "36734  -1.136412  0.074776  0.133414  ...  0.344607  0.785058 -0.164374   \n",
+       "120837 -5.472436  2.422821 -2.909735  ...  1.131130  0.118022 -0.332704   \n",
+       "6427   -3.968593  1.063728 -0.486097  ...  0.589669  0.109541  0.601045   \n",
+       "\n",
+       "             V24       V25       V26       V27       V28  Amount  Class  \n",
+       "59318   0.396310  0.197382  0.121402  0.220645  0.091821    8.27      0  \n",
+       "74794   0.139898 -0.205151 -0.472412  1.775378 -0.104285  311.91      1  \n",
+       "36734   0.411598  0.369617 -0.051243  0.022254  0.038167  101.37      0  \n",
+       "120837  0.139941  0.324758 -0.180769  0.177810  0.661555   99.90      1  \n",
+       "6427   -0.364700 -1.843078  0.351909  0.594550  0.099372    1.00      1  \n",
+       "\n",
+       "[5 rows x 31 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df.sample(frac=1)\n",
+    "\n",
+    "# amount of fraud classes 492 rows.\n",
+    "fraud_df = df.loc[df['Class'] == 1]\n",
+    "non_fraud_df = df.loc[df['Class'] == 0][:492]\n",
+    "\n",
+    "normal_distributed_df = pd.concat([fraud_df, non_fraud_df])\n",
+    "\n",
+    "# Shuffle dataframe rows\n",
+    "new_df = normal_distributed_df.sample(frac=1, random_state=42)\n",
+    "\n",
+    "new_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Distribution of the Classes in the subsample dataset\n",
+      "1    0.5\n",
+      "0    0.5\n",
+      "Name: Class, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print('Distribution of the Classes in the subsample dataset')\n",
+    "print(new_df['Class'].value_counts()/len(new_df))\n",
+    "\n",
+    "\n",
+    "\n",
+    "sns.countplot('Class', data=new_df, palette=colors)\n",
+    "plt.title('Equally Distributed Classes', fontsize=14)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classifiers:  LogisticRegression\n",
+      "Accuracy Score :  99.89993328885924\n",
+      "F1 score :  0.6459627329192548\n",
+      "Recall score :  0.5306122448979592\n",
+      "--------------------------------------------\n",
+      "Classifiers:  RandomForestClassifier\n",
+      "Accuracy Score :  99.95611109160492\n",
+      "F1 score :  0.8587570621468926\n",
+      "Recall score :  0.7755102040816326\n",
+      "--------------------------------------------\n",
+      "Classifiers:  GradientBoostingClassifier\n",
+      "Accuracy Score :  99.89466661985183\n",
+      "F1 score :  0.6629213483146067\n",
+      "Recall score :  0.6020408163265306\n",
+      "--------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Our data is already scaled we should split our training and test sets\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# This is explicitly used for undersampling.\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Turn the values into an array for feeding the classification algorithms.\n",
+    "X_train = X_train.values\n",
+    "X_test = X_test.values\n",
+    "y_train = y_train.values\n",
+    "y_test = y_test.values\n",
+    "\n",
+    "# Let's implement simple classifiers\n",
+    "\n",
+    "classifiers = {\n",
+    "    \"LogisiticRegression\": LogisticRegression(),\n",
+    "    \"RandomTreeClassifier\": RandomForestClassifier(),\n",
+    "    \"GradientBoostingClassifier\": GradientBoostingClassifier() \n",
+    "}\n",
+    "\n",
+    "# Wow our scores are getting even high scores even when applying cross validation.\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "\n",
+    "for key, classifier in classifiers.items():\n",
+    "    train = classifier.fit(X_train, y_train)\n",
+    "    pred = train.predict(X_test)\n",
+    "    print(\"Classifiers: \", classifier.__class__.__name__)\n",
+    "    print(\"Accuracy Score : \", accuracy_score(y_test, pred)*100)\n",
+    "    print(\"F1 score : \",f1_score(y_test, pred))\n",
+    "    print(\"Recall score : \", recall_score(y_test, pred))\n",
+    "    print(\"--------------------------------------------\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Separate input features and target\n",
+    "y = df.Class\n",
+    "X = df.drop('Class', axis=1)\n",
+    "\n",
+    "# setting up testing and training sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=27)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import imblearn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Separate input features and target\n",
+    "y = df.Class\n",
+    "X = df.drop('Class', axis=1)\n",
+    "\n",
+    "# setting up testing and training sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=27)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from imblearn.over_sampling import SMOTE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# SMOTE Technique (OverSampling) After splitting and Cross Validating\n",
+    "sm = SMOTE(ratio='minority', random_state=42)\n",
+    "# Xsm_train, ysm_train = sm.fit_sample(X_train, y_train)\n",
+    "\n",
+    "\n",
+    "# This will be the data were we are going to \n",
+    "Xsm_train, ysm_train = sm.fit_sample(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classifiers:  LogisticRegression\n",
+      "Accuracy Score :  98.71492373809726\n",
+      "F1 score :  0.16893732970027248\n",
+      "Recall score :  0.8773584905660378\n",
+      "--------------------------------------------\n",
+      "Classifiers:  RandomForestClassifier\n",
+      "Accuracy Score :  99.96067526193085\n",
+      "F1 score :  0.8600000000000001\n",
+      "Recall score :  0.8113207547169812\n",
+      "--------------------------------------------\n",
+      "Classifiers:  GradientBoostingClassifier\n",
+      "Accuracy Score :  99.47332940085953\n",
+      "F1 score :  0.3267504488330341\n",
+      "Recall score :  0.8584905660377359\n",
+      "--------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "classifiers = {\n",
+    "    \"LogisiticRegression\": LogisticRegression(),\n",
+    "    \"RandomTreeClassifier\": RandomForestClassifier(),\n",
+    "    \"GradientBoostingClassifier\": GradientBoostingClassifier() \n",
+    "}\n",
+    "\n",
+    "# Wow our scores are getting even high scores even when applying cross validation.\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "\n",
+    "for key, classifier in classifiers.items():\n",
+    "    train = classifier.fit(Xsm_train, ysm_train)\n",
+    "    pred = train.predict(X_test)\n",
+    "    print(\"Classifiers: \", classifier.__class__.__name__)\n",
+    "    print(\"Accuracy Score : \", accuracy_score(y_test, pred)*100)\n",
+    "    print(\"F1 score : \",f1_score(y_test, pred))\n",
+    "    print(\"Recall score : \", recall_score(y_test, pred))\n",
+    "    print(\"--------------------------------------------\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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