ds4400_fp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DS 4400 Final Project : Credit Card Fraud Detection\n",
    "\n",
    "#### Emily Chen, Glen Damian Lim, Tara Sawhney\n",
    "\n",
    "#### Dataset : https://www.kaggle.com/datasets/kartik2112/fraud-detection\n",
    "\n",
    "#### ML models: Logistic Regression, Decision Trees, Feedforward Neural Networks, Recurrent Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Segmentation Models: using `tf.keras` framework.\n"
     ]
    }
   ],
   "source": [
    "import os \n",
    "os.environ[\"SM_FRAMEWORK\"] = \"tf.keras\"\n",
    "import segmentation_models as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import datetime as dt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from collections import Counter\n",
    "\n",
    "# ML libraries\n",
    "from sklearn import metrics\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from tensorflow import keras\n",
    "import segmentation_models as sm\n",
    "# Neural Networks libraries\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "from keras.optimizers import SGD, Adam\n",
    "from keras.losses import BinaryCrossentropy\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.wrappers.scikit_learn import KerasClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(rc = {'figure.figsize':(20,10)})\n",
    "fraud_df = pd.read_csv('data/fraudTrain.csv')\n",
    "fraud_df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.296675e+06\n",
       "mean     7.035104e+01\n",
       "std      1.603160e+02\n",
       "min      1.000000e+00\n",
       "25%      9.650000e+00\n",
       "50%      4.752000e+01\n",
       "75%      8.314000e+01\n",
       "max      2.894890e+04\n",
       "Name: amt, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud_df['amt'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "545.9926000000002"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(fraud_df['amt'],99)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transaction Amount vs Fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28bb7ca30>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# amount vs fraud\n",
    "\n",
    "# Plotting a histogram of transaction amount vs fraud\n",
    "# Filtering the data to only include transactions with amount less than or equal to 1000\n",
    "# Specifying 'is_fraud' column as hue to differentiate between fraudulent and non-fraudulent transactions\n",
    "# Using 'percent' as the statistic to show the percentage of transactions in each category\n",
    "# Specifying 'dodge' as the multiple parameter to show the histograms side-by-side\n",
    "# Turning off the common normalization to display the raw counts\n",
    "# Using 25 bins to display the data more clearly\n",
    "ax=sns.histplot(x='amt',data=fraud_df[fraud_df['amt']<=1000],hue='is_fraud',stat='percent',multiple='dodge',common_norm=False,bins=25)\n",
    "ax.set_title('Transaction Amount vs Fraud', fontsize=14)\n",
    "ax.set_ylabel('Percentage in Each Type')\n",
    "ax.set_xlabel('Transaction Amount in USD')\n",
    "plt.legend(labels=['Fraud', 'Not Fraud'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While normal transactions tend to be around 200 or less, fraud transactions are typically peak around 300 as well as 800-1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gender vs Fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28bb72d30>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot gender vs fraud \n",
    "\n",
    "# Plotting a histogram of gender vs fraud\n",
    "# Specifying the 'gender' column as the x-axis and the 'is_fraud' column as the hue to differentiate between fraudulent and non-fraudulent transactions\n",
    "# Using 'percent' as the statistic to show the percentage of transactions in each category\n",
    "# Specifying 'dodge' as the multiple parameter to show the histograms side-by-side\n",
    "# Turning off the common normalization to display the raw counts\n",
    "ax = sns.histplot(x=fraud_df['gender'], data=fraud_df, hue=fraud_df['is_fraud'], \n",
    "                  stat='percent', multiple='dodge', common_norm=False)\n",
    "\n",
    "ax.set_title(\"Gender vs Fraud\", fontsize=14)\n",
    "ax.set_xlabel(\"Gender\")\n",
    "ax.set_ylabel(\"Percentage\")\n",
    "\n",
    "plt.legend(labels=['Fraud', 'Non-Fraud'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no clear difference between genders when it comes to fraud vs non-fraud transactions. According to the figure about, both males and females are equally prone to fraud transaction and gender is not an indicative amount of fraud transaction. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Spending Category vs Fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Percent Difference of Fraudulent over Non-Fraudulent Transactions in Each Spending Category')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#calculating the percentage difference between fraudulent and non-fraudulent transactions in each spending category\n",
    "#filtering the data to only include non-fraudulent transactions, then counting the percentage of transactions in each spending category\n",
    "spending_cat_1 = fraud_df[fraud_df['is_fraud'] == 0]['category'].value_counts(normalize=True).to_frame().reset_index()\n",
    "spending_cat_1.columns = ['Category', 'Non-Fraud Percentage']\n",
    "\n",
    "#filtering the data to only include fraudulent transactions, then counting the percentage of transactions in each spending category\n",
    "spending_cat_2 = fraud_df[fraud_df['is_fraud'] == 1]['category'].value_counts(normalize=True).to_frame().reset_index()\n",
    "spending_cat_2.columns = ['Category', 'Fraud Percentage']\n",
    "\n",
    "#merging the two dataframes on the 'Category' column\n",
    "merge = spending_cat_1.merge(spending_cat_2, on='Category')\n",
    "\n",
    "#adding a column to the merged dataframe that calculates the percentage difference between the fraudulent and non-fraudulent transactions in each spending category\n",
    "merge['diff'] = merge['Fraud Percentage'] - merge['Non-Fraud Percentage']\n",
    "\n",
    "#plotting a bar chart of the percentage difference of fraudulent over non-fraudulent transactions in each spending category\n",
    "#sorting the data by the 'diff' column in descending order\n",
    "fig = sns.barplot(y='Category', x='diff', data=merge.sort_values('diff', ascending=False))\n",
    "\n",
    "#adding labels to the plot\n",
    "fig.set_xlabel('Percentage Difference')\n",
    "fig.set_ylabel('Transaction Category')\n",
    "plt.title('Percent Difference of Fraudulent over Non-Fraudulent Transactions in Each Spending Category', fontsize=14)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some categories have more fraudulent transactions than others. These tend to be Shopping, Groceries, and Miscellaneous. Home, Kids/Pets tend to have more normal transactions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Age vs Fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28ddee370>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#calculating the age of each customer by subtracting their year of birth from the current year\n",
    "fraud_df['age'] = dt.date.today().year - pd.to_datetime(fraud_df['dob']).dt.year\n",
    "\n",
    "#plotting a kernel density estimate (KDE) plot of the age distribution of fraudulent and non-fraudulent transactions\n",
    "#using 'age' as the x-axis and the 'is_fraud' column as the hue to differentiate between fraudulent and non-fraudulent transactions\n",
    "#setting 'linewidth' to 5 for a thicker line\n",
    "#turning off the common normalization to display the raw counts\n",
    "fig = sns.kdeplot(x=fraud_df['age'], data=fraud_df, hue=fraud_df['is_fraud'], linewidth=5, common_norm=False)\n",
    "\n",
    "#adding labels to the plot\n",
    "fig.set_xlabel('Age')\n",
    "fig.set_ylabel('Density')\n",
    "\n",
    "#setting the x-axis tick marks to show every 5 years\n",
    "plt.xticks(np.arange(0, 110, 5))\n",
    "\n",
    "#adding a title and legend to the plot\n",
    "plt.title('Age Distribution in Fraud vs Non-Fraud Transactions', fontsize=14)\n",
    "plt.legend(title='Type', labels=['Fraud', 'Not Fraud'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the figure above, normal transactions are highest at ages 37-38 and 49-50, whereas fraud transactions are smoother, second peak includes a bigger group (ages 50-65). This indicated that older people are more likely to be subject to fraud transactions than younger people. "
   ]
  },
  {
   "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>age</th>\n",
       "      <th>is_fraud</th>\n",
       "      <th>Transaction Count</th>\n",
       "      <th>age_count</th>\n",
       "      <th>Transaction percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1952</td>\n",
       "      <td>1959</td>\n",
       "      <td>99.642675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1959</td>\n",
       "      <td>0.357325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>7464</td>\n",
       "      <td>7496</td>\n",
       "      <td>99.573106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>7496</td>\n",
       "      <td>0.426894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>3954</td>\n",
       "      <td>3975</td>\n",
       "      <td>99.471698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>4645</td>\n",
       "      <td>1.011841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>6038</td>\n",
       "      <td>6063</td>\n",
       "      <td>99.587663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>6063</td>\n",
       "      <td>0.412337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>536</td>\n",
       "      <td>536</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  is_fraud  Transaction Count  age_count  Transaction percentage\n",
       "0     18         0               1952       1959               99.642675\n",
       "1     18         1                  7       1959                0.357325\n",
       "2     19         0               7464       7496               99.573106\n",
       "3     19         1                 32       7496                0.426894\n",
       "4     20         0               3954       3975               99.471698\n",
       "..   ...       ...                ...        ...                     ...\n",
       "155   96         1                 47       4645                1.011841\n",
       "156   97         0               6038       6063               99.587663\n",
       "157   97         1                 25       6063                0.412337\n",
       "158   98         1                 11         11              100.000000\n",
       "159   99         0                536        536              100.000000\n",
       "\n",
       "[160 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#constructing the age-transaction count distribution\n",
    "age = fraud_df[['age','trans_num']].groupby(['age']).count().reset_index()\n",
    "age.columns = ['age', 'age_count']\n",
    "\n",
    "#creating the age-fraud distribution\n",
    "fraud_age = fraud_df[['age', 'trans_num', 'is_fraud']].groupby(['age','is_fraud']).count().reset_index()\n",
    "fraud_age.columns = ['age', 'is_fraud', 'Transaction Count']\n",
    "\n",
    "fraud_age = fraud_age.merge(age[['age', 'age_count']], how='inner', on='age')\n",
    "\n",
    "fraud_age['Transaction percentage'] = (fraud_age['Transaction Count']/fraud_age['age_count'])*100\n",
    "\n",
    "fraud_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This is a bar chart that depicts the same information as the line chart above, however this just shows the entire age distribution. \n",
    "\n",
    "sns.barplot(data=fraud_age, y='Transaction Count', x='age', hue='is_fraud')\n",
    "plt.title('Age Distribution and Fraud vs Non-Fraud Transactions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Time vs Fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28ddaeac0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#HOURLY \n",
    "#extracting the hour component from the transaction timestamp\n",
    "fraud_df['hour'] = pd.to_datetime(fraud_df['trans_date_trans_time']).dt.hour\n",
    "\n",
    "#plotting a histogram of the hourly distribution of fraudulent and non-fraudulent transactions\n",
    "#using 'hour' as the x-axis and the 'is_fraud' column as the hue to differentiate between fraudulent and non-fraudulent transactions\n",
    "#setting 'common_norm' to False to display the raw counts\n",
    "#setting 'stat' to 'percent' to display the percentage of transactions for each hour\n",
    "#setting 'multiple' to 'dodge' to show the two bars side-by-side\n",
    "fig = sns.histplot(data=fraud_df, x=fraud_df['hour'], hue=fraud_df['is_fraud'], common_norm=False, \n",
    "                   stat='percent', multiple='dodge')\n",
    "\n",
    "#adding labels to the plot\n",
    "fig.set_xlabel('Hour')\n",
    "fig.set_ylabel('Percentage')\n",
    "plt.title('Hour vs Fraud and Non-Fraud Transactions')\n",
    "\n",
    "#setting the x-axis tick marks to show every hour\n",
    "plt.xticks(np.arange(0, 24, 1))\n",
    "\n",
    "#adding a legend to the plot\n",
    "plt.legend(labels=['Fraud', 'Non-Fraud'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/19/j39j68y12fb87c4ngr_0z9vr0000gn/T/ipykernel_56221/4168256504.py:6: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  fig.set_xticklabels(['',\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\",\"Sun\"])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28d5b7e80>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### WEEKLY \n",
    "fraud_df['day'] = pd.to_datetime(fraud_df['trans_date_trans_time']).dt.dayofweek\n",
    "\n",
    "fig = sns.histplot(data=fraud_df, x=fraud_df['day'], hue=fraud_df['is_fraud'], common_norm=False, stat='percent', multiple='dodge')\n",
    "\n",
    "fig.set_xticklabels(['',\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\",\"Sun\"])\n",
    "\n",
    "fig.set_xlabel('Day of the Week')\n",
    "fig.set_ylabel('Percentage')\n",
    "\n",
    "plt.title('Day of Week and Fraud vs Non-Fraud Transactions')\n",
    "plt.legend(labels=['Fraud', 'Non-Fraud'])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Normal transactions happen more on Sunday and Monday and fraud transactions tend to be spread out during the  week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/19/j39j68y12fb87c4ngr_0z9vr0000gn/T/ipykernel_56221/2188959031.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",'Aug','Sep','Oct','Nov','Dec'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28d2dcee0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## MONTHLY \n",
    "#month vs fraud\n",
    "fraud_df['month']=pd.to_datetime(fraud_df['trans_date_trans_time']).dt.month\n",
    "fig=sns.histplot(data=fraud_df, x=fraud_df[\"month\"], hue=fraud_df[\"is_fraud\"], common_norm=False,stat='percent',multiple='dodge')\n",
    "fig.set_xlabel('Month')\n",
    "fig.set_ylabel('Percentage')\n",
    "plt.title('Month and Fraud vs Non-Fraud Transactions')\n",
    "plt.xticks(np.arange(1,13,1))\n",
    "ax.set_xticklabels([\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",'Aug','Sep','Oct','Nov','Dec'])\n",
    "plt.legend(labels=['Fraud', 'Not Fraud'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normal payments are highest around holiday season (December) and sprign/summer, whereas fraud transactions are more concentrated in Jan-May"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### State vs Fraud "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#calculate the percentage difference of fraudulent vs non-fraudulent transactions in each state\n",
    "state_1=fraud_df['state'][fraud_df.is_fraud==0].value_counts(normalize=True)\n",
    "state_1=state_1.to_frame()\n",
    "state_1=state_1.reset_index()\n",
    "state_1.columns = ['State', 'Percentage']\n",
    "\n",
    "state_2=fraud_df['state'][fraud_df.is_fraud==1].value_counts(normalize=True)\n",
    "state_2=state_2.to_frame()\n",
    "state_2=state_2.reset_index()\n",
    "state_2.columns = ['State', 'Percentage']\n",
    "\n",
    "merge_cols=state_1.merge(state_2,on='State')\n",
    "merge_cols['diff']=merge_cols['Percentage_y']-merge_cols['Percentage_x']\n",
    "merge_cols['diff']=merge_cols['diff']*100\n",
    "merge_cols=merge_cols.sort_values('diff',ascending=False)\n",
    "\n",
    "#creat bar chart depicting state transactions\n",
    "fig = sns.barplot(data=merge_cols, x='diff', y='State')\n",
    "fig.set_xlabel('Percentage Difference')\n",
    "fig.set_ylabel('State')\n",
    "plt.title('Percent of Fraud vs Non-Fraud Transactions in Each State')\n",
    "plt.legend(labels=['Fraud', 'Non-Fraud'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen, NY and OH  higher percentage of fraudulent transactions than normal ones, while TX and MT are the opposite. However, it should be pointed out that the percentage differences in those states are not very significant but a correlation does exist."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Job vs Fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Film/video editor', 'Exhibition designer', 'Naval architect', 'Surveyor, land/geomatics', 'Materials engineer', 'Designer, ceramics/pottery', 'Systems developer', 'IT trainer', 'Financial adviser', 'Environmental consultant', 'Chartered public finance accountant', 'Scientist, audiological', 'Chief Executive Officer', 'Copywriter, advertising', 'Comptroller', 'Sub', 'Podiatrist', 'Agricultural consultant', 'Paramedic', 'Magazine features editor']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/emilychen/Library/Python/3.8/lib/python/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fetching top 20 jobs with high transaction frequencies\n",
    "high_trans_jobs = fraud_df['job'].value_counts().head(20).index.tolist()\n",
    "print(high_trans_jobs)\n",
    "job_plot = sns.countplot(fraud_df[fraud_df['job'].isin(high_trans_jobs)].job)\n",
    "job_plot.set_xticklabels(job_plot.get_xticklabels(), rotation=90)\n",
    "plt.title(\"Job and Fraud vs Non-Fraud Transactions\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>job</th>\n",
       "      <th>is_fraud</th>\n",
       "      <th>Transaction count</th>\n",
       "      <th>job_count</th>\n",
       "      <th>Transaction percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Armed forces technical officer</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>Engineer, site</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Information officer</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Legal secretary</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>Industrial buyer</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Careers adviser</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>Homeopath</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Broadcast journalist</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>Personnel officer</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>Forest/woodland manager</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>Sales promotion account executive</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Air traffic controller</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Contracting civil engineer</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>Ship broker</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>Solicitor</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Accountant, chartered</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>Dancer</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>906</th>\n",
       "      <td>Warehouse manager</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>Veterinary surgeon</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>Lawyer</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>540</td>\n",
       "      <td>5.185185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>TEFL teacher</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>533</td>\n",
       "      <td>4.127580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Community development worker</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>536</td>\n",
       "      <td>4.104478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Clinical cytogeneticist</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>508</td>\n",
       "      <td>3.543307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>Writer</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>504</td>\n",
       "      <td>2.976190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>Geneticist, molecular</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>545</td>\n",
       "      <td>2.935780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Conservator, museum/gallery</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>514</td>\n",
       "      <td>2.918288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>Magazine journalist</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>533</td>\n",
       "      <td>2.626642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>Field trials officer</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>518</td>\n",
       "      <td>2.509653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Civil Service administrator</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>506</td>\n",
       "      <td>2.371542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>Medical technical officer</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>1066</td>\n",
       "      <td>2.345216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   job  is_fraud  Transaction count  \\\n",
       "64      Armed forces technical officer         1                  8   \n",
       "318                     Engineer, site         1                 12   \n",
       "453                Information officer         1                  8   \n",
       "493                    Legal secretary         1                 12   \n",
       "450                   Industrial buyer         1                 10   \n",
       "111                    Careers adviser         1                 15   \n",
       "421                          Homeopath         1                 11   \n",
       "94                Broadcast journalist         1                  9   \n",
       "619                  Personnel officer         1                 12   \n",
       "374            Forest/woodland manager         1                  9   \n",
       "758  Sales promotion account executive         1                 14   \n",
       "39              Air traffic controller         1                 17   \n",
       "198         Contracting civil engineer         1                  7   \n",
       "787                        Ship broker         1                  7   \n",
       "795                          Solicitor         1                 11   \n",
       "2                Accountant, chartered         1                 11   \n",
       "218                             Dancer         1                 19   \n",
       "906                  Warehouse manager         1                  7   \n",
       "898                 Veterinary surgeon         1                  8   \n",
       "484                             Lawyer         1                 28   \n",
       "830                       TEFL teacher         1                 22   \n",
       "184       Community development worker         1                 22   \n",
       "165            Clinical cytogeneticist         1                 18   \n",
       "917                             Writer         1                 15   \n",
       "391              Geneticist, molecular         1                 16   \n",
       "197        Conservator, museum/gallery         1                 15   \n",
       "514                Magazine journalist         1                 14   \n",
       "357               Field trials officer         1                 13   \n",
       "155        Civil Service administrator         1                 12   \n",
       "543          Medical technical officer         1                 25   \n",
       "\n",
       "     job_count  Transaction percentage  \n",
       "64           8              100.000000  \n",
       "318         12              100.000000  \n",
       "453          8              100.000000  \n",
       "493         12              100.000000  \n",
       "450         10              100.000000  \n",
       "111         15              100.000000  \n",
       "421         11              100.000000  \n",
       "94           9              100.000000  \n",
       "619         12              100.000000  \n",
       "374          9              100.000000  \n",
       "758         14              100.000000  \n",
       "39          17              100.000000  \n",
       "198          7              100.000000  \n",
       "787          7              100.000000  \n",
       "795         11              100.000000  \n",
       "2           11              100.000000  \n",
       "218         19              100.000000  \n",
       "906          7              100.000000  \n",
       "898          8              100.000000  \n",
       "484        540                5.185185  \n",
       "830        533                4.127580  \n",
       "184        536                4.104478  \n",
       "165        508                3.543307  \n",
       "917        504                2.976190  \n",
       "391        545                2.935780  \n",
       "197        514                2.918288  \n",
       "514        533                2.626642  \n",
       "357        518                2.509653  \n",
       "155        506                2.371542  \n",
       "543       1066                2.345216  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#constructing the zip-transaction count distribution\n",
    "job = fraud_df[['job','trans_num']].groupby(['job']).count().reset_index()\n",
    "job.columns = ['job', 'job_count']\n",
    "\n",
    "#creating the zip-fraud distribution\n",
    "fraud_job = fraud_df[['job', 'trans_num', 'is_fraud']].groupby(['job','is_fraud']).count().reset_index()\n",
    "fraud_job.columns = ['job', 'is_fraud', 'Transaction count']\n",
    "\n",
    "fraud_job = fraud_job.merge(job[['job', 'job_count']], how='inner', on='job')\n",
    "\n",
    "fraud_job['Transaction percentage'] = (fraud_job['Transaction count']/fraud_job['job_count'])*100\n",
    "\n",
    "#viewing the top 30 jobs with high fraudulent transaction volumes\n",
    "fraud_job[fraud_job['is_fraud'] == 1].sort_values(by = ['Transaction percentage'], ascending=False).head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/emilychen/Library/Python/3.8/lib/python/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the fraudulent transactions percentage by binning\n",
    "job_plot = sns.countplot(pd.cut(fraud_job[fraud_job.is_fraud == 1]['Transaction percentage'], bins = 2))\n",
    "job_plot.set_xticklabels(job_plot.get_xticklabels(), rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2                  Accountant, chartered\n",
       "39                Air traffic controller\n",
       "64        Armed forces technical officer\n",
       "94                  Broadcast journalist\n",
       "111                      Careers adviser\n",
       "198           Contracting civil engineer\n",
       "218                               Dancer\n",
       "318                       Engineer, site\n",
       "374              Forest/woodland manager\n",
       "421                            Homeopath\n",
       "450                     Industrial buyer\n",
       "453                  Information officer\n",
       "493                      Legal secretary\n",
       "619                    Personnel officer\n",
       "758    Sales promotion account executive\n",
       "787                          Ship broker\n",
       "795                            Solicitor\n",
       "898                   Veterinary surgeon\n",
       "906                    Warehouse manager\n",
       "Name: job, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#job with more than one percent fraudulent transactions\n",
    "fraud_job.loc[(fraud_job.is_fraud == 1) & (fraud_job['Transaction percentage'] >= 50)].job"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above are the jobs where in high transaction frequencies have been noted.\n",
    "In the above dataframe, the transactions containing the job feature value as-Armed forces technical officer; Engineer, site; Information officer; Legal secretary; Industrial buyer; Careers adviser; Homeopath; Broadcast journalist; Personnel officer; Forest/woodland manager; Sales promotion account executive; Air traffic controller; Contracting civil engineer; Ship broker; Solicitor;2 Accountant, chartered; Dancer; Warehouse manager; Veterinary surgeon have completely fraudulent transactions\n",
    "\n",
    "The people in the jobs with high number of fraudulent transactions can be alerted about the credit card transaction frauds so that they can be more carefull while using their credit cards.\n",
    "the jobs roles in which almost all transactions are fraudulent might mean that there is some fault with the datapoint. That is, the person representing the specific job might be at fault since it is highly unlikely that all the transactions made by a person from a spceific job are fraudulent. Hence, doing some kind of background check upon the credit card user might help in this case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Pre-Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('fraudTrain.csv')\n",
    "df_test = pd.read_csv('fraudTest.csv')\n",
    "\n",
    "def datetime_column(df, col_name: str, hour: bool =False, new_col_prefix: str =''):\n",
    "    df[col_name] = pd.to_datetime(df[col_name])\n",
    "\n",
    "    if hour:\n",
    "        new_col = new_col_prefix + '_hour'\n",
    "        df[new_col] = df[col_name].dt.hour\n",
    "    df[new_col_prefix + '_weekday'] = df[col_name].dt.weekday\n",
    "    df[new_col_prefix + '_month'] = df[col_name].dt.strftime(\"%m\")\n",
    "    df[new_col_prefix + '_year'] = df[col_name].dt.year\n",
    "\n",
    "# deriving additonal columns from 'trans_date_trans_time' and 'dob' columns\n",
    "datetime_column(df_train, 'trans_date_trans_time', True, 'trans')\n",
    "datetime_column(df_test, 'trans_date_trans_time', True, 'trans')\n",
    "datetime_column(df_train, 'dob', new_col_prefix='dob')\n",
    "datetime_column(df_test, 'dob', new_col_prefix='dob')\n",
    "\n",
    "\n",
    "# dropping irrelevant columns\n",
    "df_train.drop(['gender','city','state','job','unix_time','Unnamed: 0','cc_num','merchant', 'first', 'last','street','zip', 'dob', 'trans_num', 'trans_date_trans_time'], axis=1, inplace=True)\n",
    "df_test.drop(['gender','city','state','job','unix_time','Unnamed: 0','cc_num','merchant', 'first', 'last','street','zip', 'dob', 'trans_num', 'trans_date_trans_time'], axis=1, inplace=True)\n",
    "\n",
    "# Convert categorical columns\n",
    "categorical_column_names = ['category']\n",
    "for cat_name in categorical_column_names:\n",
    "    df_train[cat_name] = pd.factorize(df_train[cat_name])[0]\n",
    "    df_test[cat_name] = pd.factorize(df_test[cat_name])[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Resampling methods "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resampling(df):\n",
    "    class_count_0, class_count_1 = df['is_fraud'].value_counts()\n",
    "\n",
    "    class_0 = df[df['is_fraud'] == 0]\n",
    "    class_1 = df[df['is_fraud'] == 1]\n",
    "\n",
    "    class_0_under = class_0.sample(class_count_1)\n",
    "    df_undersampling = pd.concat([class_0_under, class_1], axis=0)\n",
    "\n",
    "    class_1_over = class_1.sample(class_count_0, replace=True)\n",
    "    df_oversampling = pd.concat([class_1_over, class_0], axis=0)\n",
    "    return df_undersampling, df_oversampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Selection and Data Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_scale_features(model, n_features, df_train, df_test):\n",
    "    X_train = df_train.drop('is_fraud', axis=1).astype(int)\n",
    "    y_train = df_train['is_fraud'].astype(int)\n",
    "    X_test = df_test.drop('is_fraud', axis=1).astype(int)\n",
    "    y_test = df_test['is_fraud'].astype(int)\n",
    "    \n",
    "    # Normalize and scale the data\n",
    "    mean = np.mean(X_train)\n",
    "    std = np.std(X_train)\n",
    "    X_train = (X_train - mean) / std\n",
    "    X_test = (X_test - mean) / std\n",
    "    \n",
    "    if model != None:\n",
    "        selector = RFE(model, n_features_to_select=n_features)\n",
    "        selector.fit(X_train, y_train)\n",
    "        X_train = X_train[X_train.columns[selector.support_]]\n",
    "        X_test = X_test[X_test.columns[selector.support_]]\n",
    "        \n",
    "\n",
    "    return X_train, y_train, X_test, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resampling the training data\n",
    "df_undersampling, df_oversampling = resampling(df_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def logistic_regression(X_train, y_train, X_test, y_test):\n",
    "    clf = LogisticRegression().fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print('accuracy:', metrics.accuracy_score(y_test, y_pred))\n",
    "    print(metrics.classification_report(y_test,\n",
    "                                    y_pred))\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "\n",
      "UNDERSAMPLING DATA:\n",
      "accuracy: 0.9732598669471442\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.99    553574\n",
      "           1       0.10      0.70      0.17      2145\n",
      "\n",
      "    accuracy                           0.97    555719\n",
      "   macro avg       0.55      0.84      0.58    555719\n",
      "weighted avg       1.00      0.97      0.98    555719\n",
      "\n",
      "OVERSAMPLING DATA:\n",
      "accuracy: 0.9727596141215255\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.99    553574\n",
      "           1       0.09      0.69      0.16      2145\n",
      "\n",
      "    accuracy                           0.97    555719\n",
      "   macro avg       0.55      0.83      0.58    555719\n",
      "weighted avg       1.00      0.97      0.98    555719\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Logistic Regression\\n')\n",
    "\n",
    "print('UNDERSAMPLING DATA:')\n",
    "X_train, y_train, X_test, y_test = select_scale_features(LogisticRegression(), 10, df_undersampling, df_test)\n",
    "y_pred = logistic_regression(X_train, y_train, X_test, y_test)\n",
    "\n",
    "print('OVERSAMPLING DATA:')\n",
    "X_train, y_train, X_test, y_test = select_scale_features(LogisticRegression(), 10, df_oversampling, df_test)\n",
    "y_pred = logistic_regression(X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Trees & Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decision_tree(X_train, y_train, X_test, y_test, criterion, max_depth, min_samples_split):\n",
    "    clf = DecisionTreeClassifier(criterion = criterion, max_depth = max_depth,\\\n",
    "                                 min_samples_split = min_samples_split)\n",
    "    clf.fit(X_train, y_train)\n",
    "    \n",
    "    y_pred = clf.predict(X_test)\n",
    "    print('accuracy:', metrics.accuracy_score(y_test, y_pred))\n",
    "    print(metrics.classification_report(y_test,\n",
    "                                    y_pred))\n",
    "    \n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Trees\n",
      "\n",
      "UNDERSAMPLING DATA:\n",
      "accuracy: 0.923551291210126\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.93      0.96    553574\n",
      "           1       0.01      0.11      0.01      2145\n",
      "\n",
      "    accuracy                           0.92    555719\n",
      "   macro avg       0.50      0.52      0.49    555719\n",
      "weighted avg       0.99      0.92      0.96    555719\n",
      "\n",
      "OVERSAMPLING DATA:\n",
      "accuracy: 0.9563358459941085\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.96      0.98    553574\n",
      "           1       0.01      0.07      0.01      2145\n",
      "\n",
      "    accuracy                           0.96    555719\n",
      "   macro avg       0.50      0.52      0.50    555719\n",
      "weighted avg       0.99      0.96      0.97    555719\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Decision Trees\\n')\n",
    "\n",
    "print('UNDERSAMPLING DATA:')\n",
    "X_train, y_train, X_test, y_test = select_scale_features(DecisionTreeClassifier(), 10, df_undersampling, df_test)\n",
    "y_pred = decision_tree(X_train, y_train, X_test, y_test, 'gini', 20, 5)\n",
    "\n",
    "print('OVERSAMPLING DATA:')\n",
    "X_train, y_train, X_test, y_test = select_scale_features(DecisionTreeClassifier(), 10, df_oversampling, df_test)\n",
    "y_pred = decision_tree(X_train, y_train, X_test, y_test, 'gini', 20, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feedforward Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ffnn(X_train, y_train, X_test,epochs, batch_size, weighted, optimizer, loss, metrics):\n",
    "    model = Sequential()\n",
    "    # hidden layers\n",
    "    model.add(Dense(128, activation='relu', input_shape = (X_train.shape[1],)))\n",
    "    model.add(Dropout(.2))\n",
    "    model.add(Dense(64, activation='relu'))\n",
    "    model.add(Dropout(.2))\n",
    "    model.add(Dense(32, activation='relu'))\n",
    "    model.add(Dropout(.2))\n",
    "    # output layer\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "              \n",
    "    # configure the learning process\n",
    "    model.compile(loss=loss,\n",
    "                  optimizer=optimizer,\n",
    "                  metrics=metrics)\n",
    "    \n",
    "    # Weighted class\n",
    "    if weighted:\n",
    "        weights = compute_class_weight(class_weight=\"balanced\",classes=np.unique(y_train),y=y_train)\n",
    "        class_weights = {0: weights[0], 1: weights[1]}\n",
    "        model.fit(X_train, y_train, \n",
    "                  epochs= epochs, batch_size = batch_size, verbose=1, validation_split = 0.2, class_weight = class_weights)\n",
    "    else:\n",
    "        model.fit(X_train, y_train, \n",
    "                  epochs= epochs, batch_size = batch_size, verbose=1, validation_split = 0.2)\n",
    "        \n",
    "    print(model.output)\n",
    "    \n",
    "    # Make predictions\n",
    "    y_pred = model.predict(X_test)\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 12009 samples, validate on 3003 samples\n",
      "Epoch 1/10\n",
      "12009/12009 [==============================] - 1s 53us/step - loss: 0.4472 - accuracy: 0.8035 - val_loss: 1.8119 - val_accuracy: 0.4998\n",
      "Epoch 2/10\n",
      "12009/12009 [==============================] - 0s 29us/step - loss: 0.3322 - accuracy: 0.8668 - val_loss: 1.8947 - val_accuracy: 0.5162\n",
      "Epoch 3/10\n",
      "12009/12009 [==============================] - 0s 26us/step - loss: 0.3069 - accuracy: 0.8743 - val_loss: 2.2102 - val_accuracy: 0.4945\n",
      "Epoch 4/10\n",
      "12009/12009 [==============================] - 0s 27us/step - loss: 0.2901 - accuracy: 0.8805 - val_loss: 2.5692 - val_accuracy: 0.4835\n",
      "Epoch 5/10\n",
      "12009/12009 [==============================] - 0s 25us/step - loss: 0.2786 - accuracy: 0.8830 - val_loss: 2.5564 - val_accuracy: 0.4562\n",
      "Epoch 6/10\n",
      "12009/12009 [==============================] - 0s 24us/step - loss: 0.2723 - accuracy: 0.8859 - val_loss: 2.9136 - val_accuracy: 0.4389\n",
      "Epoch 7/10\n",
      "12009/12009 [==============================] - 0s 24us/step - loss: 0.2648 - accuracy: 0.8872 - val_loss: 3.0322 - val_accuracy: 0.4246\n",
      "Epoch 8/10\n",
      "12009/12009 [==============================] - 0s 27us/step - loss: 0.2536 - accuracy: 0.8911 - val_loss: 3.2261 - val_accuracy: 0.4166\n",
      "Epoch 9/10\n",
      "12009/12009 [==============================] - 0s 30us/step - loss: 0.2495 - accuracy: 0.8932 - val_loss: 3.8147 - val_accuracy: 0.3164\n",
      "Epoch 10/10\n",
      "12009/12009 [==============================] - 0s 28us/step - loss: 0.2423 - accuracy: 0.9002 - val_loss: 4.0137 - val_accuracy: 0.3024\n",
      "Tensor(\"dense_24/Sigmoid:0\", shape=(None, 1), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "# Undersampled data\n",
    "X_train, y_train, X_test, y_test = select_scale_features(None, 10, df_undersampling, df_test)\n",
    "undersampling_preds = ffnn(X_train, y_train, X_test, 10, 128, False, 'adam', 'binary_crossentropy', ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 2062670 samples, validate on 515668 samples\n",
      "Epoch 1/10\n",
      "2062670/2062670 [==============================] - 58s 28us/step - loss: 0.1321 - accuracy: 0.9462 - val_loss: 30.7666 - val_accuracy: 0.0952\n",
      "Epoch 2/10\n",
      "2062670/2062670 [==============================] - 56s 27us/step - loss: 0.0785 - accuracy: 0.9693 - val_loss: 47.5139 - val_accuracy: 0.0820\n",
      "Epoch 3/10\n",
      "2062670/2062670 [==============================] - 59s 29us/step - loss: 0.0661 - accuracy: 0.9749 - val_loss: 48.5673 - val_accuracy: 0.0950\n",
      "Epoch 4/10\n",
      "2062670/2062670 [==============================] - 61s 29us/step - loss: 0.0597 - accuracy: 0.9777 - val_loss: 62.1235 - val_accuracy: 0.0773\n",
      "Epoch 5/10\n",
      "2062670/2062670 [==============================] - 58s 28us/step - loss: 0.0556 - accuracy: 0.9795 - val_loss: 63.9823 - val_accuracy: 0.0956\n",
      "Epoch 6/10\n",
      "2062670/2062670 [==============================] - 59s 29us/step - loss: 0.0527 - accuracy: 0.9808 - val_loss: 70.2001 - val_accuracy: 0.0931\n",
      "Epoch 7/10\n",
      "2062670/2062670 [==============================] - 59s 29us/step - loss: 0.0502 - accuracy: 0.9818 - val_loss: 59.9401 - val_accuracy: 0.0984\n",
      "Epoch 8/10\n",
      "2062670/2062670 [==============================] - 58s 28us/step - loss: 0.0489 - accuracy: 0.9824 - val_loss: 68.7337 - val_accuracy: 0.0950\n",
      "Epoch 9/10\n",
      "2062670/2062670 [==============================] - 59s 29us/step - loss: 0.0474 - accuracy: 0.9829 - val_loss: 79.8245 - val_accuracy: 0.0925\n",
      "Epoch 10/10\n",
      "2062670/2062670 [==============================] - 58s 28us/step - loss: 0.0463 - accuracy: 0.9834 - val_loss: 76.6316 - val_accuracy: 0.0939\n",
      "Tensor(\"dense_28/Sigmoid:0\", shape=(None, 1), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "# Oversampled data\n",
    "X_train, y_train, X_test, y_test = select_scale_features(None, 10, df_oversampling, df_test)\n",
    "oversampling_preds = ffnn(X_train, y_train, X_test, 10, 128, False, 'adam', 'binary_crossentropy', ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1037340 samples, validate on 259335 samples\n",
      "Epoch 1/10\n",
      "1037340/1037340 [==============================] - 29s 28us/step - loss: 0.3634 - accuracy: 0.9254 - val_loss: 0.2874 - val_accuracy: 0.9346\n",
      "Epoch 2/10\n",
      "1037340/1037340 [==============================] - 29s 28us/step - loss: 0.2779 - accuracy: 0.9176 - val_loss: 0.2526 - val_accuracy: 0.8944\n",
      "Epoch 3/10\n",
      "1037340/1037340 [==============================] - 31s 30us/step - loss: 0.2491 - accuracy: 0.9200 - val_loss: 0.2334 - val_accuracy: 0.9325\n",
      "Epoch 4/10\n",
      "1037340/1037340 [==============================] - 28s 27us/step - loss: 0.2238 - accuracy: 0.9283 - val_loss: 0.2772 - val_accuracy: 0.9587\n",
      "Epoch 5/10\n",
      "1037340/1037340 [==============================] - 28s 27us/step - loss: 0.2150 - accuracy: 0.9300 - val_loss: 0.2306 - val_accuracy: 0.8983\n",
      "Epoch 6/10\n",
      "1037340/1037340 [==============================] - 28s 27us/step - loss: 0.2064 - accuracy: 0.9240 - val_loss: 0.2049 - val_accuracy: 0.9535\n",
      "Epoch 7/10\n",
      "1037340/1037340 [==============================] - 30s 29us/step - loss: 0.1962 - accuracy: 0.9341 - val_loss: 0.2633 - val_accuracy: 0.9572\n",
      "Epoch 8/10\n",
      "1037340/1037340 [==============================] - 28s 27us/step - loss: 0.1898 - accuracy: 0.9365 - val_loss: 0.2670 - val_accuracy: 0.9432\n",
      "Epoch 9/10\n",
      "1037340/1037340 [==============================] - 28s 27us/step - loss: 0.1843 - accuracy: 0.9403 - val_loss: 0.2177 - val_accuracy: 0.9391\n",
      "Epoch 10/10\n",
      "1037340/1037340 [==============================] - 29s 28us/step - loss: 0.1785 - accuracy: 0.9412 - val_loss: 0.3790 - val_accuracy: 0.9537\n",
      "Tensor(\"dense_32/Sigmoid:0\", shape=(None, 1), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "# Weighted class, with original non-sampled data\n",
    "X_train, y_train, X_test, y_test = select_scale_features(None, 10, df_train, df_test)\n",
    "weighted_preds = ffnn(X_train, y_train, X_test, 10, 128, True, 'adam', 'binary_crossentropy', ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feedforward Neural Network\n",
      "\n",
      "UNDERSAMPLING DATA:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    553574\n",
      "           1       0.07      0.03      0.04      2145\n",
      "\n",
      "    accuracy                           0.99    555719\n",
      "   macro avg       0.54      0.51      0.52    555719\n",
      "weighted avg       0.99      0.99      0.99    555719\n",
      "\n",
      "OVERSAMPLING DATA:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.00      0.00    553574\n",
      "           1       0.00      1.00      0.01      2145\n",
      "\n",
      "    accuracy                           0.00    555719\n",
      "   macro avg       0.50      0.50      0.00    555719\n",
      "weighted avg       1.00      0.00      0.00    555719\n",
      "\n",
      "ORIGINAL DATA w/ weighted weights:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99    553574\n",
      "           1       0.05      0.29      0.09      2145\n",
      "\n",
      "    accuracy                           0.98    555719\n",
      "   macro avg       0.53      0.63      0.54    555719\n",
      "weighted avg       0.99      0.98      0.99    555719\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Metrics for FFNN\n",
    "print('Feedforward Neural Network\\n')\n",
    "\n",
    "print('UNDERSAMPLING DATA:')\n",
    "preds = [1 if pred > 0.5 else 0 for pred in undersampling_preds]\n",
    "m = metrics.classification_report(y_test, preds)\n",
    "print(m)\n",
    "\n",
    "print('OVERSAMPLING DATA:')\n",
    "preds = [1 if pred > 0.5 else 0 for pred in oversampling_preds]\n",
    "m = metrics.classification_report(y_test, preds)\n",
    "print(m)\n",
    "\n",
    "print('ORIGINAL DATA w/ weighted weights:')\n",
    "preds = [1 if pred > 0.5 else 0 for pred in weighted_preds]\n",
    "m = metrics.classification_report(y_test, preds)\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyperparameter Tuning for Logistic Regression and Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def param_fit(estimator, params, scoring='accuracy', cv=5):\n",
    "    X_train, y_train, X_test, y_test = select_scale_features(estimator, 10, df_undersampling, df_test)\n",
    "    best_params = GridSearchCV(estimator,\n",
    "                      param_grid=params,\n",
    "                      scoring=scoring,\n",
    "                      cv=cv)\n",
    "    best_params.fit(X_train, y_train)\n",
    "    print('best parameters:', best_params.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/shared/centos7/anaconda3/2021.07-TF/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:1322: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best parameters: {'C': 1, 'penalty': 'l2', 'tol': 0.001}\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "lr_params = {\n",
    "    'penalty': ['l2', 'none'],\n",
    "    'tol': [0.001, 0.0001, 0.00001],\n",
    "    'C': [0.1, 1, 10]\n",
    "}\n",
    "\n",
    "lr = LogisticRegression()\n",
    "param_fit(lr, lr_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best parameters: {'criterion': 'entropy', 'max_depth': 10, 'min_samples_split': 2}\n"
     ]
    }
   ],
   "source": [
    "# Decision Trees\n",
    "dt_params = {\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'max_depth': [None, 10, 20],\n",
    "    'min_samples_split': [2, 6, 10]\n",
    "}\n",
    "dtc = DecisionTreeClassifier()\n",
    "param_fit(dtc, dt_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression with best parameters:\n",
      "\n",
      "accuracy: 0.9739238715969761\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99    553574\n",
      "           1       0.09      0.64      0.16      2145\n",
      "\n",
      "    accuracy                           0.97    555719\n",
      "   macro avg       0.55      0.81      0.57    555719\n",
      "weighted avg       1.00      0.97      0.98    555719\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Logistic Regression with best parameters:\\n\")\n",
    "clf = LogisticRegression(C = 1, penalty = 'l2', tol = 0.001).fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print('accuracy:', metrics.accuracy_score(y_test, y_pred))\n",
    "print(metrics.classification_report(y_test,y_pred)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree with best parameters:\n",
      "\n",
      "accuracy: 0.9250970364518759\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.93      0.96    553574\n",
      "           1       0.01      0.13      0.01      2145\n",
      "\n",
      "    accuracy                           0.93    555719\n",
      "   macro avg       0.50      0.53      0.49    555719\n",
      "weighted avg       0.99      0.93      0.96    555719\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Decision Tree with best parameters:\\n\")\n",
    "clf = DecisionTreeClassifier(criterion = 'entropy', max_depth = 10, min_samples_split = 2).fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print('accuracy:', metrics.accuracy_score(y_test, y_pred))\n",
    "print(metrics.classification_report(y_test,y_pred)) "
   ]
  }
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