1_Data_Cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a1aa9f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All imports done\n"
     ]
    }
   ],
   "source": [
    "# Importing required libraries.\n",
    "import numpy as np\n",
    "import pandas as pd  \n",
    "pd.options.display.max_columns = 31\n",
    "\n",
    "from matplotlib import pyplot as plt                    \n",
    "import seaborn as  sns \n",
    "\n",
    "from sklearn.preprocessing import StandardScaler        \n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "print('All imports done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e46092bd",
   "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>V10</th>\n",
       "      <th>V11</th>\n",
       "      <th>V12</th>\n",
       "      <th>V13</th>\n",
       "      <th>V14</th>\n",
       "      <th>V15</th>\n",
       "      <th>V16</th>\n",
       "      <th>V17</th>\n",
       "      <th>V18</th>\n",
       "      <th>V19</th>\n",
       "      <th>V20</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>0.090794</td>\n",
       "      <td>-0.551600</td>\n",
       "      <td>-0.617801</td>\n",
       "      <td>-0.991390</td>\n",
       "      <td>-0.311169</td>\n",
       "      <td>1.468177</td>\n",
       "      <td>-0.470401</td>\n",
       "      <td>0.207971</td>\n",
       "      <td>0.025791</td>\n",
       "      <td>0.403993</td>\n",
       "      <td>0.251412</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>-0.166974</td>\n",
       "      <td>1.612727</td>\n",
       "      <td>1.065235</td>\n",
       "      <td>0.489095</td>\n",
       "      <td>-0.143772</td>\n",
       "      <td>0.635558</td>\n",
       "      <td>0.463917</td>\n",
       "      <td>-0.114805</td>\n",
       "      <td>-0.183361</td>\n",
       "      <td>-0.145783</td>\n",
       "      <td>-0.069083</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>0.207643</td>\n",
       "      <td>0.624501</td>\n",
       "      <td>0.066084</td>\n",
       "      <td>0.717293</td>\n",
       "      <td>-0.165946</td>\n",
       "      <td>2.345865</td>\n",
       "      <td>-2.890083</td>\n",
       "      <td>1.109969</td>\n",
       "      <td>-0.121359</td>\n",
       "      <td>-2.261857</td>\n",
       "      <td>0.524980</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>-0.054952</td>\n",
       "      <td>-0.226487</td>\n",
       "      <td>0.178228</td>\n",
       "      <td>0.507757</td>\n",
       "      <td>-0.287924</td>\n",
       "      <td>-0.631418</td>\n",
       "      <td>-1.059647</td>\n",
       "      <td>-0.684093</td>\n",
       "      <td>1.965775</td>\n",
       "      <td>-1.232622</td>\n",
       "      <td>-0.208038</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>0.753074</td>\n",
       "      <td>-0.822843</td>\n",
       "      <td>0.538196</td>\n",
       "      <td>1.345852</td>\n",
       "      <td>-1.119670</td>\n",
       "      <td>0.175121</td>\n",
       "      <td>-0.451449</td>\n",
       "      <td>-0.237033</td>\n",
       "      <td>-0.038195</td>\n",
       "      <td>0.803487</td>\n",
       "      <td>0.408542</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",
       "</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       V10       V11       V12       V13       V14  \\\n",
       "0  0.098698  0.363787  0.090794 -0.551600 -0.617801 -0.991390 -0.311169   \n",
       "1  0.085102 -0.255425 -0.166974  1.612727  1.065235  0.489095 -0.143772   \n",
       "2  0.247676 -1.514654  0.207643  0.624501  0.066084  0.717293 -0.165946   \n",
       "3  0.377436 -1.387024 -0.054952 -0.226487  0.178228  0.507757 -0.287924   \n",
       "4 -0.270533  0.817739  0.753074 -0.822843  0.538196  1.345852 -1.119670   \n",
       "\n",
       "        V15       V16       V17       V18       V19       V20       V21  \\\n",
       "0  1.468177 -0.470401  0.207971  0.025791  0.403993  0.251412 -0.018307   \n",
       "1  0.635558  0.463917 -0.114805 -0.183361 -0.145783 -0.069083 -0.225775   \n",
       "2  2.345865 -2.890083  1.109969 -0.121359 -2.261857  0.524980  0.247998   \n",
       "3 -0.631418 -1.059647 -0.684093  1.965775 -1.232622 -0.208038 -0.108300   \n",
       "4  0.175121 -0.451449 -0.237033 -0.038195  0.803487  0.408542 -0.009431   \n",
       "\n",
       "        V22       V23       V24       V25       V26       V27       V28  \\\n",
       "0  0.277838 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053   \n",
       "1 -0.638672  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724   \n",
       "2  0.771679  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752   \n",
       "3  0.005274 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458   \n",
       "4  0.798278 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   \n",
       "\n",
       "   Amount  Class  \n",
       "0  149.62      0  \n",
       "1    2.69      0  \n",
       "2  378.66      0  \n",
       "3  123.50      0  \n",
       "4   69.99      0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the dataset into pandas dataframe\n",
    "data = pd.read_csv(\"C:/DataSets/creditcardfraud_ds.csv\")  \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5ed03496",
   "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": [
    "# Get the info related to data.\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7c8702da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 30)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Time in this case is an unnecessary factor\n",
    "data.drop(['Time'] , axis= 1 , inplace= True) \n",
    "data.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e876fe87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(275663, 30)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing the duplicates from the data\n",
    "data.drop_duplicates(keep= 'first' , inplace= True)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bb9b9992",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking whether there are any empty cells in data\n",
    "data.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1b888f29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Class Distribution')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a pie chart showing normal transactions and fraud transactions.\n",
    "count_classes = pd.value_counts(data['Class'], sort = True)\n",
    "count_classes.plot(kind='pie')\n",
    "plt.title('Class Distribution')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db2c3767",
   "metadata": {},
   "source": [
    "From the above bar graph , We can conclude that dataset is highly imbalanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "27e4835f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x2500 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plots the histograms of the each feature.\n",
    "data.hist(figsize= (25,25) , color=\"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cfdd01f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Correlation Matrix'}>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2500x2500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Correlation Matrix\n",
    "fig = plt.figure(figsize=(25,25))\n",
    "plt.title('Correlation Matrix')\n",
    "sns.heatmap(data.corr().round(2) , linewidths= .5 , annot= True , cmap= plt.get_cmap('seismic'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5234fa42",
   "metadata": {},
   "source": [
    "As there are no highly correlated features , so I will not be deleting any features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8f44dc96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of normal transactions are : 275190\n",
      "Number of fraud transactions are : 473\n",
      "Total number of transactions are : 275663\n",
      "Outlier Fraction : 0.001718812456847996\n"
     ]
    }
   ],
   "source": [
    "# Finding the number of normal transactions and fraud transactions in the dataset.\n",
    "normal = count_classes[0] \n",
    "fraud = count_classes[1]\n",
    "outlier_fraction = fraud / normal\n",
    "\n",
    "print('Number of normal transactions are :' , normal)\n",
    "print('Number of fraud transactions are :' , fraud)\n",
    "print('Total number of transactions are :' , normal + fraud )\n",
    "print('Outlier Fraction :' , outlier_fraction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cb6017b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(275663, 29), (275663,)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting the dataset into X (input) and Y(output) . \n",
    "X = data.drop(['Class'] , axis = 1)\n",
    "Y = data['Class']\n",
    "\n",
    "X = StandardScaler().fit_transform(X)\n",
    "[X.shape,Y.shape]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "60a16ecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[((220530, 29), (220530,)), ((55133, 29), (55133,))]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X , Y , train_size= 0.8 , random_state= 4)\n",
    "[(x_train.shape,y_train.shape),(x_test.shape,y_test.shape)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4fb64133",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported Successfully\n"
     ]
    }
   ],
   "source": [
    "pd.DataFrame(x_train).to_csv('X_train.csv' , index= False)\n",
    "pd.DataFrame(y_train).to_csv('Y_train.csv' , index= False)\n",
    "pd.DataFrame(x_test).to_csv('X_test.csv' , index= False)\n",
    "pd.DataFrame(y_test).to_csv('Y_test.csv' , index= False)\n",
    "print('Exported Successfully')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "9781c75f8ebb8db568c34abc6fa56aff5b950f24b8cfd6fe8af6829c67f4ac57"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}