Fraud_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "id_raw_df = pd.read_csv(\"/Users/burwani/Downloads/train_identity.csv\")\n",
    "id_trans_df= pd.read_csv(\"/Users/burwani/Downloads/train_transaction.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144233, 41)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id_raw_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(590540, 394)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id_trans_df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_full_df = pd.merge(id_trans_df, id_raw_df, on='TransactionID', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(590540, 434)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_full_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(train_full_df[train_full_df[\"isFraud\"]==1])/len(train_full_df)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NA values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
       "        182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n",
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       "        416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428,\n",
       "        429, 430, 431, 432, 433]), <a list of 434 Text xticklabel objects>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 6480x2880 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(90,40))\n",
    "sns.barplot(y=train_full_df.isnull().sum().sort_values(ascending=False)/len(train_full_df),\n",
    "            x=train_full_df.isnull().sum().sort_values(ascending=False).index,\n",
    "            palette=\"Reds_d\")\n",
    "plt.title(\"Percent Missing Value\",size=50)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling NA's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_def=pd.DataFrame({\"na_count\":train_full_df.isnull().sum().sort_values(ascending=False)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Create column of percent columns\n",
    "columns_def[\"per\"]=columns_def[\"na_count\"]/len(train_full_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cycle throungh various limit for percent Na per column\n",
    "range=[0.05, 0.10 ,0.15, 0.25, 0.40, 0.60, 0.75]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.25806451612903225\n",
      "0.25806451612903225\n",
      "0.3686635944700461\n",
      "0.41935483870967744\n",
      "0.46543778801843316\n",
      "0.5207373271889401\n",
      "0.5207373271889401\n"
     ]
    }
   ],
   "source": [
    "for i in range: \n",
    "    print (columns_def[columns_def[\"per\"]<i].shape[0]/train_full_df.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "limit=0.10 #only columns w/ less 10% will remain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Select final columns\n",
    "columns_final=columns_def[columns_def[\"per\"]<limit].index \n",
    "\n",
    "train_full_df=train_full_df[train_full_df.columns.intersection(columns_final)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dealing with catagories "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>ProductCD</th>\n",
       "      <th>card4</th>\n",
       "      <th>card6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>W</td>\n",
       "      <td>discover</td>\n",
       "      <td>credit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>credit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>W</td>\n",
       "      <td>visa</td>\n",
       "      <td>debit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>W</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>debit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>H</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>credit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>590535</td>\n",
       "      <td>W</td>\n",
       "      <td>visa</td>\n",
       "      <td>debit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>590536</td>\n",
       "      <td>W</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>debit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>590537</td>\n",
       "      <td>W</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>debit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>590538</td>\n",
       "      <td>W</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>debit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>590539</td>\n",
       "      <td>W</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>credit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>590540 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ProductCD       card4   card6\n",
       "0              W    discover  credit\n",
       "1              W  mastercard  credit\n",
       "2              W        visa   debit\n",
       "3              W  mastercard   debit\n",
       "4              H  mastercard  credit\n",
       "...          ...         ...     ...\n",
       "590535         W        visa   debit\n",
       "590536         W  mastercard   debit\n",
       "590537         W  mastercard   debit\n",
       "590538         W  mastercard   debit\n",
       "590539         W  mastercard  credit\n",
       "\n",
       "[590540 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show categorical variables\n",
    "train_full_df[train_full_df.select_dtypes(include=['object']).columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "card6_dummy_df=pd.get_dummies(train_full_df[\"card6\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(590540, 112)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "train_full_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_full_df=pd.concat([train_full_df,card6_dummy_df],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.46278261170406"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "train_full_df=train_full_df._get_numeric_data()\n",
    "train_full_df=train_full_df.dropna()\n",
    "\n",
    "len(train_full_df[train_full_df[\"isFraud\"]==1])/len(train_full_df)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "x_full_df=train_full_df.drop([\"isFraud\"],axis=1)\n",
    "y_full_df=train_full_df[\"isFraud\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_train,X_test,y_train,y_test=train_test_split(x_full_df,y_full_df,test_size=0.50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression  \n",
    "from sklearn.preprocessing import StandardScaler  \n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "lr = LogisticRegression(solver='lbfgs')  \n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9653756335518691"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "lr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>279030</td>\n",
       "      <td>37</td>\n",
       "      <td>279067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>9971</td>\n",
       "      <td>7</td>\n",
       "      <td>9978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>All</td>\n",
       "      <td>289001</td>\n",
       "      <td>44</td>\n",
       "      <td>289045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted       0   1     All\n",
       "True                         \n",
       "0          279030  37  279067\n",
       "1            9971   7    9978\n",
       "All        289001  44  289045"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "pd.crosstab(y_test, y_pred, rownames=['True'], colnames=['Predicted'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0007015433954700341"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "recall_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9653756335518691"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0007015433954700341"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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