starter notebook fraud prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train data shape:  (10493, 15) \n",
      "test data shape:  (4507, 14)\n"
     ]
    }
   ],
   "source": [
    "train = pd.read_csv('dataset/train.csv')\n",
    "test = pd.read_csv('dataset/test.csv')\n",
    "print('train data shape: ',train.shape,'\\ntest data shape: ',test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The first step is to convert the data type of the column (TRANSACTION TIME) to DateTime. \n",
    "# That can be done using .to_datetime() method in Pandas.\n",
    "train['transaction time']= pd.to_datetime(train['transaction time'])\n",
    "train['hour'] = train['transaction time'].dt.hour #returns the hour of the datetime(range 1 - 24)\n",
    "train['dayofweek_name'] = train['transaction time'].dt.weekday_name #day of week of transaction\n",
    "train['is_weekend'] = np.where(train['dayofweek_name'].isin(['Sunday','Saturday']),1,0) #weekend or nota\n",
    "\n",
    "#Getting the period of the day from the hour feature\n",
    "bins = [0,4,8,12,16,20,24]\n",
    "period = ['Late Night', 'Early Morning','Morning','Noon','Eve','Night']\n",
    "train['period_of_day'] = pd.cut(train['hour'], bins=bins, labels=period)\n",
    "\n",
    "#Getting the recent amount withdrawn or deposited\n",
    "train['prev_tran'] = train['current bank amount'] - train['last bank amount']\n",
    "#credit if the value is positive or debit if otherwise\n",
    "train['credit_or_debit'] = ['credit' if x > 0 else 'debit' for x in train['prev_tran'] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12f4c91128>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['period_of_day'],hue=train['fradulent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12f1b7b470>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['credit_or_debit'],hue=train['fradulent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12f1c26d68>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['fradulent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12f1c766a0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['time taken (seconds)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = train[['current bank amount', 'last bank amount',\n",
    "       'time taken (seconds)', 'most recent bank amount', 'account type',\n",
    "       'age', 'occupation', 'credit card type',\n",
    "       'account source verification', 'transaction source method',\n",
    "       'account destination verification', 'dayofweek_name', 'is_weekend',\n",
    "       'hour','prev_tran','credit_or_debit','period_of_day']]\n",
    "X = pd.get_dummies(columns=['account type',\n",
    "       'occupation', 'credit card type', 'credit_or_debit',\n",
    "       'account source verification', 'transaction source method',\n",
    "       'account destination verification', 'dayofweek_name','period_of_day'], data = features)\n",
    "y = train['fradulent']  #target feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,    test_size=0.2, random_state=42, stratify = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.metrics import auc, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lr: 0.525685 \n",
      "LDA: 0.759489 \n",
      "Knn: 0.496915 \n",
      "Decision_Tree: 0.629698 \n"
     ]
    }
   ],
   "source": [
    "seed = 7\n",
    "models = []\n",
    "models.append(('Lr', LogisticRegression()))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('Knn', KNeighborsClassifier()))\n",
    "models.append(('Decision_Tree', DecisionTreeClassifier()))\n",
    "\n",
    "# evaluate each model in turn\n",
    "results = []\n",
    "names = []\n",
    "for name, model in models:\n",
    "    model.fit(X_train, y_train)\n",
    "    score = roc_auc_score(y_test, model.predict_proba(X_test)[:,1])\n",
    "    results.append(score)\n",
    "    names.append(name)\n",
    "    msg = \"%s: %f \" % (name, score)\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['transaction time']= pd.to_datetime(test['transaction time'])\n",
    "test['hour'] = test['transaction time'].dt.hour #returns the hour of the datetime(range 1 - 24)\n",
    "test['dayofweek_name'] = test['transaction time'].dt.weekday_name #day of week of transaction\n",
    "test['is_weekend'] = np.where(test['dayofweek_name'].isin(['Sunday','Saturday']),1,0) #weekend or not\n",
    "\n",
    "test['period_of_day'] = pd.cut(test['hour'], bins=bins, labels=period)\n",
    "\n",
    "#Getting the recent amount withdrawn or deposited\n",
    "test['prev_tran'] = test['current bank amount'] - test['last bank amount']\n",
    "#credit if the value is positive or debit if otherwise\n",
    "test['credit_or_debit'] = ['credit' if x > 0 else 'debit' for x in test['prev_tran'] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test[features.columns]\n",
    "test = pd.get_dummies(columns=['account type',\n",
    "       'occupation', 'credit card type', 'credit_or_debit',\n",
    "       'account source verification', 'transaction source method',\n",
    "       'account destination verification', 'dayofweek_name','period_of_day'], data = test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearDiscriminantAnalysis()\n",
    "model.fit(X, y)\n",
    "predictions = model.predict_proba(test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>id</th>\n",
       "      <th>fradulent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bcus2993</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bcus2994</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bcus2995</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bcus2996</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bcus2997</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  fradulent\n",
       "0  bcus2993        1.0\n",
       "1  bcus2994        1.0\n",
       "2  bcus2995        1.0\n",
       "3  bcus2996        1.0\n",
       "4  bcus2997        1.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub = pd.read_csv('sample sub.csv')\n",
    "sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub['fradulent'] = predictions\n",
    "sub[['id', 'fradulent']].to_csv('submission.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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