Credit card fraud detection ANN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import BatchNormalization,Dropout,Dense,Flatten,Conv1D\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "from kerastuner.tuners import RandomSearch\n",
    "from tensorflow.keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tools\\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('creditcard.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape   #checking the shape of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Class.unique()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Class.value_counts()   #checking the total unique value count of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#its a unbalanced data so we will create a new dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28786ae9e08>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEGCAYAAACpXNjrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAShUlEQVR4nO3dfaxd113m8e9Tu2nLSxqXuCHYmTpQgwhhcBNPElHNqFCROJFGbpkEJYjaKhFGVYIoqhAp0kyqlkggKB3Sl6CUuLErpiFqKDEaF2OlgQ6atuSmWM0bVe6E0rgJsVObJEwVwOmPP8665OT6+PraWecc+/r7kbbOPr+99tprV1ae7r3X2TdVhSRJPb1i2gOQJC09hoskqTvDRZLUneEiSerOcJEkdbd82gM4UZx55pm1Zs2aaQ9Dkk4q999//9NVtXJ+3XBp1qxZw8zMzLSHIUknlSR/P6rubTFJUneGiySpO8NFktSd4SJJ6s5wkSR1Z7hIkrozXCRJ3RkukqTuDBdJUnf+Qr+jC391+7SHoBPQ/b+9adpDkCbOKxdJUneGiySpO8NFktSd4SJJ6s5wkSR1Z7hIkrozXCRJ3RkukqTuDBdJUneGiySpO8NFktSd4SJJ6s5wkSR1Z7hIkrozXCRJ3RkukqTuDBdJUneGiySpO8NFktSd4SJJ6s5wkSR1N7ZwSXJOknuTPJLkoSS/3OrvS/KNJHvacsXQPu9NMpvkq0kuG6pvaLXZJDcM1c9N8qUkjyb5oySntfqr2vfZtn3NuM5TknS4cV65HALeU1U/DFwCXJfkvLbtQ1W1ri07Adq2q4EfATYAH0uyLMky4KPA5cB5wDVD/fxW62stcBC4ttWvBQ5W1RuBD7V2kqQJGVu4VNWTVfXltv4c8AiwaoFdNgJ3VNU/V9XfAbPARW2ZrarHqupfgDuAjUkC/CTw6bb/NuBtQ31ta+ufBt7a2kuSJmAiz1zabak3AV9qpeuTfCXJ1iQrWm0V8PjQbntb7Uj17wH+saoOzau/pK+2/ZnWfv64tiSZSTKzf//+l3WOkqQXjT1cknwXcBfw7qp6FrgF+AFgHfAk8MG5piN2r+OoL9TXSwtVt1bV+qpav3LlygXPQ5K0eGMNlySvZBAsf1hVfwxQVU9V1QtV9W3g4wxue8HgyuOcod1XA08sUH8aOCPJ8nn1l/TVtr8WOND37CRJRzLO2WIBbgMeqarfHaqfPdTs7cCDbX0HcHWb6XUusBb4a+A+YG2bGXYag4f+O6qqgHuBK9v+m4G7h/ra3NavBD7X2kuSJmD50ZsctzcD7wAeSLKn1X6dwWyvdQxuU30N+EWAqnooyZ3Awwxmml1XVS8AJLke2AUsA7ZW1UOtv18D7kjyG8DfMAgz2ucnk8wyuGK5eoznKUmaZ2zhUlV/xehnHzsX2Ocm4KYR9Z2j9quqx3jxttpw/XngqmMZrySpH3+hL0nqznCRJHVnuEiSujNcJEndGS6SpO4MF0lSd4aLJKk7w0WS1J3hIknqznCRJHVnuEiSujNcJEndGS6SpO4MF0lSd4aLJKk7w0WS1J3hIknqznCRJHVnuEiSujNcJEndGS6SpO4MF0lSd4aLJKk7w0WS1J3hIknqznCRJHVnuEiSuhtbuCQ5J8m9SR5J8lCSX2711yXZneTR9rmi1ZPk5iSzSb6S5IKhvja39o8m2TxUvzDJA22fm5NkoWNIkiZjnFcuh4D3VNUPA5cA1yU5D7gBuKeq1gL3tO8AlwNr27IFuAUGQQHcCFwMXATcOBQWt7S2c/ttaPUjHUOSNAFjC5eqerKqvtzWnwMeAVYBG4Ftrdk24G1tfSOwvQa+CJyR5GzgMmB3VR2oqoPAbmBD23Z6VX2hqgrYPq+vUceQJE3ARJ65JFkDvAn4EnBWVT0JgwACXt+arQIeH9ptb6stVN87os4Cx5g/ri1JZpLM7N+//3hPT5I0z9jDJcl3AXcB766qZxdqOqJWx1FftKq6tarWV9X6lStXHsuukqQFjDVckrySQbD8YVX9cSs/1W5p0T73tfpe4Jyh3VcDTxylvnpEfaFjSJImYJyzxQLcBjxSVb87tGkHMDfjazNw91B9U5s1dgnwTLultQu4NMmK9iD/UmBX2/ZckkvasTbN62vUMSRJE7B8jH2/GXgH8ECSPa3268BvAncmuRb4OnBV27YTuAKYBb4FvBOgqg4k+QBwX2v3/qo60NbfBdwOvAb4bFtY4BiSpAkYW7hU1V8x+rkIwFtHtC/guiP0tRXYOqI+A5w/ov7NUceQJE2Gv9CXJHVnuEiSujNcJEndGS6SpO4MF0lSd4aLJKk7w0WS1J3hIknqznCRJHVnuEiSujNcJEndGS6SpO4MF0lSd4aLJKk7w0WS1J3hIknqznCRJHVnuEiSujNcJEndGS6SpO4WFS5J7llMTZIkgOULbUzyauA7gDOTrADSNp0OfN+YxyZJOkktGC7ALwLvZhAk9/NiuDwLfHSM45IkncQWDJeq+j3g95L8UlV9eEJjkiSd5I525QJAVX04yY8Da4b3qartYxqXJOkktqhwSfJJ4AeAPcALrVyA4SJJOsyiwgVYD5xXVTXOwUiSlobF/s7lQeB7j6XjJFuT7Evy4FDtfUm+kWRPW64Y2vbeJLNJvprksqH6hlabTXLDUP3cJF9K8miSP0pyWqu/qn2fbdvXHMu4JUkv32LD5Uzg4SS7kuyYW46yz+3AhhH1D1XVurbsBEhyHnA18CNtn48lWZZkGYNZaZcD5wHXtLYAv9X6WgscBK5t9WuBg1X1RuBDrZ0kaYIWe1vsfcfacVV9/hiuGjYCd1TVPwN/l2QWuKhtm62qxwCS3AFsTPII8JPAz7Y229oYb2l9zY3308BHksRbepI0OYudLfaXHY95fZJNwAzwnqo6CKwCvjjUZm+rATw+r34x8D3AP1bVoRHtV83tU1WHkjzT2j/d8RwkSQtY7OtfnkvybFueT/JCkmeP43i3MJh1tg54Evjg3CFGtK3jqC/U12GSbEkyk2Rm//79C41bknQMFhUuVfXdVXV6W14N/DfgI8d6sKp6qqpeqKpvAx/nxVtfe4FzhpquBp5YoP40cEaS5fPqL+mrbX8tcOAI47m1qtZX1fqVK1ce6+lIko7guN6KXFV/wuCZxzFJcvbQ17czmIUGsAO4us30OhdYC/w1cB+wts0MO43BQ/8d7fnJvcCVbf/NwN1DfW1u61cCn/N5iyRN1mJ/RPnTQ19fweB3Lwv+BzvJp4C3MHjp5V7gRuAtSda1fb/G4N1lVNVDSe4EHgYOAddV1Qutn+uBXcAyYGtVPdQO8WvAHUl+A/gb4LZWvw34ZJsUcIBBIEmSJmixs8X+69D6IQbBsHGhHarqmhHl20bU5trfBNw0or4T2Dmi/hgv3lYbrj8PXLXQ2CRJ47XY2WLvHPdAJElLx2Jni61O8pn2i/unktyVZPW4BydJOjkt9oH+Jxg8KP8+Br8j+dNWkyTpMIsNl5VV9YmqOtSW2wHn7kqSRlpsuDyd5Ofm3veV5OeAb45zYJKkk9diw+XngZ8B/oHBL+uvBHzIL0kaabFTkT8AbG7vASPJ64DfYRA6kiS9xGKvXP7jXLAAVNUB4E3jGZIk6WS32HB5RZIVc1/alctir3okSaeYxQbEB4H/m+TTDF7d8jOM+DW9JEmw+F/ob08yw+BllQF+uqoeHuvIJEknrUXf2mphYqBIko7quF65L0nSQgwXSVJ3hoskqTvDRZLUneEiSerOcJEkdWe4SJK6M1wkSd0ZLpKk7gwXSVJ3hoskqTvDRZLUneEiSerOcJEkdWe4SJK6M1wkSd2NLVySbE2yL8mDQ7XXJdmd5NH2uaLVk+TmJLNJvpLkgqF9Nrf2jybZPFS/MMkDbZ+bk2ShY0iSJmecVy63Axvm1W4A7qmqtcA97TvA5cDatmwBboFBUAA3AhcDFwE3DoXFLa3t3H4bjnIMSdKEjC1cqurzwIF55Y3Atra+DXjbUH17DXwROCPJ2cBlwO6qOlBVB4HdwIa27fSq+kJVFbB9Xl+jjiFJmpBJP3M5q6qeBGifr2/1VcDjQ+32ttpC9b0j6gsd4zBJtiSZSTKzf//+4z4pSdJLnSgP9DOiVsdRPyZVdWtVra+q9StXrjzW3SVJRzDpcHmq3dKife5r9b3AOUPtVgNPHKW+ekR9oWNIkiZk0uGyA5ib8bUZuHuovqnNGrsEeKbd0toFXJpkRXuQfymwq217LsklbZbYpnl9jTqGJGlClo+r4ySfAt4CnJlkL4NZX78J3JnkWuDrwFWt+U7gCmAW+BbwToCqOpDkA8B9rd37q2puksC7GMxIew3w2bawwDEkSRMytnCpqmuOsOmtI9oWcN0R+tkKbB1RnwHOH1H/5qhjSJIm50R5oC9JWkIMF0lSd4aLJKk7w0WS1J3hIknqznCRJHVnuEiSujNcJEndGS6SpO4MF0lSd4aLJKk7w0WS1J3hIknqznCRJHVnuEiSujNcJEndGS6SpO4MF0lSd4aLJKk7w0WS1J3hIknqznCRJHVnuEiSujNcJEndGS6SpO4MF0lSd4aLJKm7qYRLkq8leSDJniQzrfa6JLuTPNo+V7R6ktycZDbJV5JcMNTP5tb+0SSbh+oXtv5n276Z/FlK0qlrmlcuP1FV66pqfft+A3BPVa0F7mnfAS4H1rZlC3ALDMIIuBG4GLgIuHEukFqbLUP7bRj/6UiS5pxIt8U2Atva+jbgbUP17TXwReCMJGcDlwG7q+pAVR0EdgMb2rbTq+oLVVXA9qG+JEkTMK1wKeDPk9yfZEurnVVVTwK0z9e3+irg8aF997baQvW9I+qHSbIlyUySmf3797/MU5IkzVk+peO+uaqeSPJ6YHeSv12g7ajnJXUc9cOLVbcCtwKsX79+ZBtJ0rGbypVLVT3RPvcBn2HwzOSpdkuL9rmvNd8LnDO0+2rgiaPUV4+oS5ImZOLhkuQ7k3z33DpwKfAgsAOYm/G1Gbi7re8ANrVZY5cAz7TbZruAS5OsaA/yLwV2tW3PJbmkzRLbNNSXJGkCpnFb7CzgM2128HLgf1XVnyW5D7gzybXA14GrWvudwBXALPAt4J0AVXUgyQeA+1q791fVgbb+LuB24DXAZ9siSZqQiYdLVT0G/NiI+jeBt46oF3DdEfraCmwdUZ8Bzn/Zg5UkHZcTaSqyJGmJMFwkSd0ZLpKk7gwXSVJ3hoskqTvDRZLUneEiSerOcJEkdWe4SJK6M1wkSd0ZLpKk7gwXSVJ3hoskqTvDRZLUneEiSerOcJEkdWe4SJK6M1wkSd0ZLpKk7gwXSVJ3hoskqTvDRZLUneEiSerOcJEkdWe4SJK6M1wkSd0ZLpKk7gwXSVJ3SzZckmxI8tUks0lumPZ4JOlUsiTDJcky4KPA5cB5wDVJzpvuqCTp1LF82gMYk4uA2ap6DCDJHcBG4OGpjkqakq+//0enPQSdgP7D/3hgbH0v1XBZBTw+9H0vcPH8Rkm2AFva139K8tUJjO1UcSbw9LQHcSLI72ye9hD0Uv7bnHNjevTyhlHFpRouo/4Xq8MKVbcCt45/OKeeJDNVtX7a45Dm89/mZCzJZy4MrlTOGfq+GnhiSmORpFPOUg2X+4C1Sc5NchpwNbBjymOSpFPGkrwtVlWHklwP7AKWAVur6qEpD+tU4+1Gnaj8tzkBqTrsUYQkSS/LUr0tJkmaIsNFktSd4aKufO2OTlRJtibZl+TBaY/lVGC4qBtfu6MT3O3AhmkP4lRhuKinf3/tTlX9CzD32h1p6qrq88CBaY/jVGG4qKdRr91ZNaWxSJoiw0U9Leq1O5KWPsNFPfnaHUmA4aK+fO2OJMBwUUdVdQiYe+3OI8CdvnZHJ4oknwK+APxQkr1Jrp32mJYyX/8iSerOKxdJUneGiySpO8NFktSd4SJJ6s5wkSR1Z7hIU5Dke5PckeT/JXk4yc4kP+gbe7VULMk/cyydyJIE+AywraqubrV1wFlTHZjUkVcu0uT9BPCvVfX7c4Wq2sPQSz+TrEnyf5J8uS0/3upnJ/l8kj1JHkzyn5MsS3J7+/5Akl+Z/ClJL+WVizR55wP3H6XNPuCnqur5JGuBTwHrgZ8FdlXVTe3v53wHsA5YVVXnAyQ5Y3xDlxbHcJFOTK8EPtJul70A/GCr3wdsTfJK4E+qak+Sx4DvT/Jh4H8Dfz6VEUtDvC0mTd5DwIVHafMrwFPAjzG4YjkN/v0PXv0X4BvAJ5NsqqqDrd1fANcBfzCeYUuLZ7hIk/c54FVJfmGukOQ/AW8YavNa4Mmq+jbwDmBZa/cGYF9VfRy4DbggyZnAK6rqLuC/AxdM5jSkI/O2mDRhVVVJ3g78zyQ3AM8DXwPePdTsY8BdSa4C7gX+f6u/BfjVJP8K/BOwicFf+/xEkrn/s/jesZ+EdBS+FVmS1J23xSRJ3RkukqTuDBdJUneGiySpO8NFktSd4SJJ6s5wkSR192/3qTsoBAXzIQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#taking the class 0, 1 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "nf = df[df.Class==0]\n",
    "f = df[df.Class==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#extracting the class 0 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Total entries are 1.5* NO. of class-1 entries\n",
    "nf = nf.sample(738)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating new Dataframe\n",
    "data = f.append(nf,ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1230, 31)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop(['Class'],axis=1)\n",
    "y=data['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting the DATAset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((984, 30), (246, 30))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape,X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Applying StandardScaler to obtain all the features in similar range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=StandardScaler()\n",
    "X_train=scaler.fit_transform(X_train)\n",
    "X_test=scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Searching the best Parameters for the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(hp):\n",
    "    model = Sequential()\n",
    "    i = 0\n",
    "    for i in range(hp.Int('num_layers', 2,5)):\n",
    "        if i==0:\n",
    "            model.add(layers.Dense(units=hp.Int('units_' + str(i), 32, 150, 32),kernel_initializer='he_uniform',input_dim=X_train.shape[1],\n",
    "                               activation='relu'))\n",
    "            i += 1\n",
    "        else:\n",
    "            model.add(layers.Dense(units=hp.Int('units_' + str(i), 32, 150, 32),kernel_initializer='he_uniform',\n",
    "                               activation='relu'))\n",
    "        \n",
    "        \n",
    "    model.add(layers.Dense(1, activation='sigmoid'))\n",
    "    model.compile(\n",
    "        optimizer=keras.optimizers.Adam(\n",
    "            hp.Choice('learning_rate', [0.001, 0.0001,0.01])),\n",
    "        loss='binary_crossentropy',\n",
    "        metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "tuner = RandomSearch(\n",
    "            build_model,\n",
    "            objective='accuracy',\n",
    "            max_trials=5,\n",
    "            executions_per_trial=3,\n",
    "            directory='project1',\n",
    "            project_name='Credit card fraud'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Search space summary\n",
      "Default search space size: 4\n",
      "num_layers (Int)\n",
      "{'default': None, 'conditions': [], 'min_value': 2, 'max_value': 5, 'step': 1, 'sampling': None}\n",
      "units_0 (Int)\n",
      "{'default': None, 'conditions': [], 'min_value': 32, 'max_value': 150, 'step': 32, 'sampling': None}\n",
      "units_1 (Int)\n",
      "{'default': None, 'conditions': [], 'min_value': 32, 'max_value': 150, 'step': 32, 'sampling': None}\n",
      "learning_rate (Choice)\n",
      "{'default': 0.001, 'conditions': [], 'values': [0.001, 0.0001, 0.01], 'ordered': True}\n"
     ]
    }
   ],
   "source": [
    "tuner.search_space_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 5 Complete [00h 00m 04s]\n",
      "accuracy: 0.9200542171796163\n",
      "\n",
      "Best accuracy So Far: 0.9715447425842285\n",
      "Total elapsed time: 00h 00m 24s\n",
      "INFO:tensorflow:Oracle triggered exit\n"
     ]
    }
   ],
   "source": [
    "tuner.search(X_train,y_train,epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results summary\n",
      "Results in project1\\Credit card fraud\n",
      "Showing 10 best trials\n",
      "Objective(name='accuracy', direction='max')\n",
      "Trial summary\n",
      "Hyperparameters:\n",
      "num_layers: 4\n",
      "units_0: 128\n",
      "units_1: 128\n",
      "learning_rate: 0.01\n",
      "units_2: 32\n",
      "units_3: 32\n",
      "Score: 0.9715447425842285\n",
      "Trial summary\n",
      "Hyperparameters:\n",
      "num_layers: 2\n",
      "units_0: 128\n",
      "units_1: 128\n",
      "learning_rate: 0.001\n",
      "units_2: 64\n",
      "units_3: 96\n",
      "units_4: 128\n",
      "Score: 0.963075856367747\n",
      "Trial summary\n",
      "Hyperparameters:\n",
      "num_layers: 2\n",
      "units_0: 128\n",
      "units_1: 64\n",
      "learning_rate: 0.001\n",
      "Score: 0.9627371033032736\n",
      "Trial summary\n",
      "Hyperparameters:\n",
      "num_layers: 5\n",
      "units_0: 128\n",
      "units_1: 64\n",
      "learning_rate: 0.0001\n",
      "units_2: 128\n",
      "units_3: 128\n",
      "units_4: 32\n",
      "Score: 0.924119253953298\n",
      "Trial summary\n",
      "Hyperparameters:\n",
      "num_layers: 4\n",
      "units_0: 96\n",
      "units_1: 128\n",
      "learning_rate: 0.0001\n",
      "units_2: 96\n",
      "units_3: 64\n",
      "units_4: 128\n",
      "Score: 0.9200542171796163\n"
     ]
    }
   ],
   "source": [
    "tuner.results_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "##using the best  parameter got from Randomsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(layers.Dense(units=128,kernel_initializer='he_uniform',input_dim=X_train.shape[1],activation='relu'))\n",
    "model.add(layers.Dense(units=96,kernel_initializer='he_uniform',activation='relu'))\n",
    "model.add(layers.Dense(units=128,kernel_initializer='he_uniform',activation='relu'))\n",
    "model.add(layers.Dense(units=128,kernel_initializer='he_uniform',activation='relu'))\n",
    "\n",
    "model.add(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.0001),loss='binary_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "31/31 [==============================] - 2s 26ms/step - loss: 1.0930 - accuracy: 0.4027 - val_loss: 0.6553 - val_accuracy: 0.5325\n",
      "Epoch 2/20\n",
      "31/31 [==============================] - 0s 4ms/step - loss: 0.5771 - accuracy: 0.6779 - val_loss: 0.4587 - val_accuracy: 0.8659\n",
      "Epoch 3/20\n",
      "31/31 [==============================] - 0s 5ms/step - loss: 0.4099 - accuracy: 0.8866 - val_loss: 0.3775 - val_accuracy: 0.8943\n",
      "Epoch 4/20\n",
      "31/31 [==============================] - 0s 5ms/step - loss: 0.3234 - accuracy: 0.9138 - val_loss: 0.3279 - val_accuracy: 0.9106\n",
      "Epoch 5/20\n",
      "31/31 [==============================] - 0s 5ms/step - loss: 0.2838 - accuracy: 0.9170 - val_loss: 0.2925 - val_accuracy: 0.9187\n",
      "Epoch 6/20\n",
      "31/31 [==============================] - 0s 4ms/step - loss: 0.2328 - accuracy: 0.9334 - val_loss: 0.2699 - val_accuracy: 0.9187\n",
      "Epoch 7/20\n",
      "31/31 [==============================] - 0s 3ms/step - loss: 0.2009 - accuracy: 0.9370 - val_loss: 0.2539 - val_accuracy: 0.9228\n",
      "Epoch 8/20\n",
      "31/31 [==============================] - 0s 4ms/step - loss: 0.1914 - accuracy: 0.9394 - val_loss: 0.2402 - val_accuracy: 0.9268\n",
      "Epoch 9/20\n",
      "31/31 [==============================] - 0s 5ms/step - loss: 0.1761 - accuracy: 0.9466 - val_loss: 0.2309 - val_accuracy: 0.9268\n",
      "Epoch 10/20\n",
      "31/31 [==============================] - 0s 4ms/step - loss: 0.1616 - accuracy: 0.9481 - val_loss: 0.2248 - val_accuracy: 0.9309\n",
      "Epoch 11/20\n",
      "31/31 [==============================] - 0s 4ms/step - loss: 0.1474 - accuracy: 0.9521 - val_loss: 0.2196 - val_accuracy: 0.9350\n",
      "Epoch 12/20\n",
      "31/31 [==============================] - 0s 4ms/step - loss: 0.1146 - accuracy: 0.9698 - val_loss: 0.2136 - val_accuracy: 0.9309\n",
      "Epoch 13/20\n",
      "31/31 [==============================] - 0s 4ms/step - loss: 0.1281 - accuracy: 0.9547 - val_loss: 0.2118 - val_accuracy: 0.9350\n",
      "Epoch 14/20\n",
      "31/31 [==============================] - 0s 4ms/step - loss: 0.1260 - accuracy: 0.9582 - val_loss: 0.2084 - val_accuracy: 0.9309\n",
      "Epoch 15/20\n",
      "31/31 [==============================] - 0s 10ms/step - loss: 0.1091 - accuracy: 0.9649 - val_loss: 0.2075 - val_accuracy: 0.9309\n",
      "Epoch 16/20\n",
      "31/31 [==============================] - 0s 5ms/step - loss: 0.1040 - accuracy: 0.9709 - val_loss: 0.2053 - val_accuracy: 0.9309\n",
      "Epoch 17/20\n",
      "31/31 [==============================] - 0s 4ms/step - loss: 0.1041 - accuracy: 0.9657 - val_loss: 0.2032 - val_accuracy: 0.9309\n",
      "Epoch 18/20\n",
      "31/31 [==============================] - 0s 4ms/step - loss: 0.1013 - accuracy: 0.9659 - val_loss: 0.2016 - val_accuracy: 0.9309\n",
      "Epoch 19/20\n",
      "31/31 [==============================] - 0s 4ms/step - loss: 0.1036 - accuracy: 0.9630 - val_loss: 0.2030 - val_accuracy: 0.9309\n",
      "Epoch 20/20\n",
      "31/31 [==============================] - 0s 4ms/step - loss: 0.1079 - accuracy: 0.9619 - val_loss: 0.2004 - val_accuracy: 0.9309\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train,y_train,epochs=20,validation_data=(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotLearningCurve(history,epochs):\n",
    "  epochRange = range(1,epochs+1)\n",
    "  plt.plot(epochRange,history.history['accuracy'])\n",
    "  plt.plot(epochRange,history.history['val_accuracy'])\n",
    "  plt.title('Model Accuracy')\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Accuracy')\n",
    "  plt.legend(['Train','Validation'],loc='upper left')\n",
    "  plt.show()\n",
    "\n",
    "  plt.plot(epochRange,history.history['loss'])\n",
    "  plt.plot(epochRange,history.history['val_loss'])\n",
    "  plt.title('Model Loss')\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Loss')\n",
    "  plt.legend(['Train','Validation'],loc='upper left')\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "plotLearningCurve(history,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting the test result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = y_pred>0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#making the confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[144,   4],\n",
       "       [ 13,  85]], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate the accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9308943089430894"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "score = accuracy_score(y_pred,y_test)\n",
    "score"
   ]
  },
  {
   "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
}