NadeemChaudhary_SVM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn import svm\n",
    "from sklearn.linear_model import Perceptron, LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import datasets\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This turns all the axes white in all the matplotlib plots. Comment this out if you dont want that\n",
    "COLOR = 'white'\n",
    "matplotlib.rcParams['text.color'] = COLOR\n",
    "matplotlib.rcParams['axes.labelcolor'] = COLOR\n",
    "matplotlib.rcParams['xtick.color'] = COLOR\n",
    "matplotlib.rcParams['ytick.color'] = COLOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>TransactionID</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>TransactionDT</th>\n",
       "      <th>TransactionAmt</th>\n",
       "      <th>ProductCD</th>\n",
       "      <th>card1</th>\n",
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       "      <td>...</td>\n",
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       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>102.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>visa</td>\n",
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       "      <td>...</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0</td>\n",
       "      <td>86499</td>\n",
       "      <td>50.0</td>\n",
       "      <td>W</td>\n",
       "      <td>18132</td>\n",
       "      <td>567.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>117.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
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       "<p>5 rows × 394 columns</p>\n",
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      ],
      "text/plain": [
       "   TransactionID  isFraud  TransactionDT  TransactionAmt ProductCD  card1  \\\n",
       "0        2987000        0          86400            68.5         W  13926   \n",
       "1        2987001        0          86401            29.0         W   2755   \n",
       "2        2987002        0          86469            59.0         W   4663   \n",
       "3        2987003        0          86499            50.0         W  18132   \n",
       "4        2987004        0          86506            50.0         H   4497   \n",
       "\n",
       "   card2  card3       card4  card5  ... V330  V331  V332  V333  V334 V335  \\\n",
       "0    NaN  150.0    discover  142.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
       "1  404.0  150.0  mastercard  102.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
       "2  490.0  150.0        visa  166.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
       "3  567.0  150.0  mastercard  117.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
       "4  514.0  150.0  mastercard  102.0  ...  0.0   0.0   0.0   0.0   0.0  0.0   \n",
       "\n",
       "  V336  V337  V338  V339  \n",
       "0  NaN   NaN   NaN   NaN  \n",
       "1  NaN   NaN   NaN   NaN  \n",
       "2  NaN   NaN   NaN   NaN  \n",
       "3  NaN   NaN   NaN   NaN  \n",
       "4  0.0   0.0   0.0   0.0  \n",
       "\n",
       "[5 rows x 394 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/Users/nadeem/datasets/ieee-fraud-detection/train_transaction.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In the case of a SVM, we will try to split the cloud of 0's and 1's.\n",
    "\n",
    "features = ['isFraud', 'TransactionDT',\n",
    "            'TransactionAmt','ProductCD', 'P_emaildomain','R_emaildomain']\n",
    "df = df[features]\n",
    "df.head()\n",
    "\n",
    "target = 'isFraud'\n",
    "cat = ['TransactionDT','ProductCD', 'P_emaildomain','R_emaildomain']\n",
    "num = ['TransactionAmt']\n",
    "\n",
    "df = df.dropna()\n",
    "y = df[target].values\n",
    "\n",
    "x_cat = df.filter(items = cat).values\n",
    "x_num = df.filter(items = num).values\n",
    "\n",
    "labelencoder_X = LabelEncoder()\n",
    "for i in range(len(cat)): \n",
    "    x_cat[:, i] = labelencoder_X.fit_transform(x_cat[:, i])\n",
    "    \n",
    "X = np.concatenate((x_cat, x_num), axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0, stratify = y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature scaling - prepares training and test data for ML by standardizing features to have similar scales\n",
    "sc = StandardScaler()\n",
    "sc.fit(X_train)\n",
    "X_train_std = sc.transform(X_train)\n",
    "X_test_std = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating instance of Support Vector Classification\n",
    "# C compensates between the width of the margins and classification error.\n",
    "# Large values of C create result in narrow margins and more strict classification rules.\n",
    "# C and random_state are hyperparamters\n",
    "svc_linear = SVC(C=1.0, random_state=1, kernel='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear', random_state=1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It takes so long to train these models\n",
    "svc_linear.fit(X_train_std, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict_linear = svc_linear.predict(X_test_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score 0.916\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy score %.3f\" %metrics.accuracy_score(y_test, y_predict_linear))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[23131     0]\n",
      " [ 2115     0]]\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_predict_linear)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# These results are very bad. \n",
    "# The general structure of a confusion matrix by rows is [[TN FP]\n",
    "#                                                         [FN TP]]\n",
    "# All the negative are correctly identified and the all positives are falsely identified. \n",
    "# Our model is very biased towards the negative class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# other kernels take too long - over 8 minutes and still not done!\n",
    "svc_rbf = SVC(C=1.0, random_state=1, kernel='rbf', gamma = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_rbf.fit(X_train_std, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict_poly = svc_rbf.predict(X_test_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Accuracy score %.3f\" %metrics.accuracy_score(y_test, y_predict_poly))"
   ]
  }
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