knn_svm_Classification_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "637dd35e",
   "metadata": {},
   "source": [
    "Second Personal Project on ML Classification\n",
    "This is a classification project using KNN and SVM Algorithm to\n",
    "Classify Banking CreditCard transactional records as either fraudulent or legitimate\n",
    "Project done without supervision - only pure desire and keeping track record\n",
    "by Blibo Albert @bliboalbert <- github && LinkedIn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ee015ad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this project I will train two powerful machine learning algorithms to classify\n",
    "# creditcard transactions as either legitimate {class 0} or fraudulent {class 1}\n",
    "# K Nearest Neighbor algorithm and Support Vector Machine Algorithm is used to train the models\n",
    "# Datasets used in this project were downloaded from kaggle.com {one powerful data aggregator}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e61a2c1b",
   "metadata": {},
   "source": [
    "Project @2a\n",
    "KNN - K-Nearest Neighbor Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "33bfaf0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# K-Nearest Neighbor Classifier\n",
    "# KNN works on the intuition of predicting the class in which a datapoint will fall when it is near\n",
    "# already predicted class of datapoints\n",
    "# KNN uses either euclidian distance or manhatan distance or cosine distance to determine the class in\n",
    "# which a new datapoint will fall\n",
    "# It is either the new datapoint's distance is very close to a particular neighbor\n",
    "# Or the new datapoint is close to K neighbors of one class than the other\n",
    "# KNN is a lazy learning approach since you always have to query the model before..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71fc85af",
   "metadata": {},
   "source": [
    "Data access, Data Analysis and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9cac7791",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import important sklearn and SciPy libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b0b77636",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6aa34f3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Access dataset for preprocessing and analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a082fedc",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
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      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('creditcard.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "da9b954a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 284807 observations and 31 features in the dataset.\n"
     ]
    }
   ],
   "source": [
    "# some info about dataset\n",
    "observation = len(data.index)\n",
    "features = len(data.columns)\n",
    "print('There are {} observations and {} features in the dataset.'.format(observation, features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "df830062",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The dataset has already been normalize, thus from V1 to V28 we see the normalization of the dataset\n",
    "# We can go ahead and extract features for our training\n",
    "# our target is the column named class, which consist of a class group of 0s and 1s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3592d88e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unbalanced Data: There are 284315 Class 0s and 492 class 1s.\n"
     ]
    }
   ],
   "source": [
    "# how many 0s and 1s are there? thus is our target datapoints balanced for a better training?\n",
    "class_size = data.Class.value_counts().values\n",
    "print('Unbalanced Data: There are {} Class 0s and {} class 1s.'.format(class_size[0], class_size[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8f9923ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data bias visuals with pie chart\n",
    "target = data.Class.unique()\n",
    "size = data.Class.value_counts().values\n",
    "fig, ax = plt.subplots()\n",
    "ax.pie(size,labels=target, autopct='%.2f%%')\n",
    "ax.set_title('Bias Data Representation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9f039d4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extracting feature matrix and response vector or target from data dataframe\n",
    "X = data.iloc[:, 1:-1]\n",
    "y = data.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cc5a384e",
   "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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</th>\n",
       "      <th>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251412</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.069083</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.524980</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208038</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>...</td>\n",
       "      <td>0.408542</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9       V10  ...       V20       V21       V22       V23  \\\n",
       "0  0.098698  0.363787  0.090794  ...  0.251412 -0.018307  0.277838 -0.110474   \n",
       "1  0.085102 -0.255425 -0.166974  ... -0.069083 -0.225775 -0.638672  0.101288   \n",
       "2  0.247676 -1.514654  0.207643  ...  0.524980  0.247998  0.771679  0.909412   \n",
       "3  0.377436 -1.387024 -0.054952  ... -0.208038 -0.108300  0.005274 -0.190321   \n",
       "4 -0.270533  0.817739  0.753074  ...  0.408542 -0.009431  0.798278 -0.137458   \n",
       "\n",
       "        V24       V25       V26       V27       V28  Amount  \n",
       "0  0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62  \n",
       "1 -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69  \n",
       "2 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66  \n",
       "3 -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50  \n",
       "4  0.141267 -0.206010  0.502292  0.219422  0.215153   69.99  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e90ed30e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train / Test datasets\n",
    "X_train,X_test, y_train,y_test = train_test_split(X,y, test_size=.33, random_state=44)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a1ce04a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Care is taken to incomporate the unbalanced dataset in the training/fitting of the model\n",
    "# to ensure the model is biased toward the data class that is more (the 0 class)\n",
    "# in other to combat this, we compute the class weight to normalize the target variable\n",
    "# this will be use in the training of the model to make it bias towards the less datapoints\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "85ce80df",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils.class_weight import compute_sample_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "65bc07e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_w = compute_sample_weight('balanced', y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4777b379",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_jobs=10, n_neighbors=4)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train model, knn_model = KNeighborsClassifier()\n",
    "# with hyperparameter tuning :- weight, n_neighbors=4, n_jobs=10\n",
    "knn_model = KNeighborsClassifier(n_neighbors=4, n_jobs=10, weights='uniform')\n",
    "knn_model.fit(X_train, y_train)\n",
    "knn_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3f5c18c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make inferences and evaluate model\n",
    "knn_train_pred = knn_model.predict(X_train)\n",
    "knn_train_pred[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0cb75c4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 1.00\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print('Train Accuracy: {0:.2f}'.format(accuracy_score(y_train, knn_train_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "047ef0da",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 1.00\n"
     ]
    }
   ],
   "source": [
    "knn_test = knn_model.predict(X_test)\n",
    "print('Test Accuracy: {0:.2f}'.format(accuracy_score(y_test, knn_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5231dcd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for ids, cl in enumerate(knn_test):\n",
    " #   if cl == 0:\n",
    " #       count = 0\n",
    "  #      print('Legitimate Transaction! class is {}.'.format(cl))\n",
    "  ##     if count == 10:\n",
    "          #  break\n",
    "   # else:\n",
    "       # print('Fraudulent Transaction! class is {}.'.format(cl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "55f732dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "cm = confusion_matrix(y_test, knn_test, labels=[0,1])\n",
    "cm_train = confusion_matrix(y_train, knn_train_pred, labels=[0,1])\n",
    "c_report = classification_report(y_test, knn_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ae78c67e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[93822     4]\n",
      " [   69    92]]\n",
      "\n",
      "\n",
      "[[190478     11]\n",
      " [   105    226]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     93826\n",
      "           1       0.96      0.57      0.72       161\n",
      "\n",
      "    accuracy                           1.00     93987\n",
      "   macro avg       0.98      0.79      0.86     93987\n",
      "weighted avg       1.00      1.00      1.00     93987\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(cm)\n",
    "print('\\n')\n",
    "print(cm_train)\n",
    "print(c_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f4724609",
   "metadata": {},
   "outputs": [],
   "source": [
    "# confusion matrix for knn model testing evaluation\n",
    "import itertools\n",
    "\n",
    "def plot_confusion_mat(cm, classes, normalize=False, title='<-Confusion Matrix->', cmap=plt.cm.Spectral):\n",
    "    '''This function plots the confusion as either normalized or not normalized\n",
    "    You can set the parameter normalize to True to plot confusion matrix in normalize mode'''\n",
    "    \n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print('Confusion Matrix Normalized')\n",
    "    else:\n",
    "        print('Confusion Matrix without Normalization')\n",
    "        \n",
    "    print(cm)\n",
    "    \n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    tick_mark = np.arange(len(classes))\n",
    "    plt.xticks(tick_mark, classes, rotation=45)\n",
    "    plt.yticks(tick_mark, classes)\n",
    "    plt.colorbar()\n",
    "    \n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max()/2\n",
    "    \n",
    "    for i,j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j,i, format(cm[i,j], fmt),\n",
    "                 horizontalalignment = 'center',\n",
    "                 color = 'white' if thresh > cm[i,j] else 'black')\n",
    "    plt.tight_layout()\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('True Class')\n",
    "    plt.show()\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "51b80128",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix without Normalization\n",
      "[[93822     4]\n",
      " [   69    92]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix without normalization for test model evaluation\n",
    "plt.figure\n",
    "plot_confusion_mat(cm, classes=['L=0', 'F=1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "333503ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix Normalized\n",
      "[[9.99957368e-01 4.26321062e-05]\n",
      " [4.28571429e-01 5.71428571e-01]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix normalized for test model evaluation\n",
    "plt.figure\n",
    "plot_confusion_mat(cm, normalize=True, classes=['L=0', 'F=1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f3aa54e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix Normalized\n",
      "[[9.99942254e-01 5.77461166e-05]\n",
      " [3.17220544e-01 6.82779456e-01]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix normalized for train model evaluation\n",
    "plt.figure\n",
    "plot_confusion_mat(cm_train, normalize=True, classes=['L=0', 'F=1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6eeccee5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# conclusion on KNN model for accuracy and precision\n",
    "# looking at the confusion matrix, the model is able to predict all negative cases {class = 0}\n",
    "# as 0 but predicted only 57% for test data and 68% for train data\n",
    "# this is bad for our model hence further hyperparameter tuning is required for our model to function\n",
    "# well, thus to prevent overfitting of the model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95823209",
   "metadata": {},
   "source": [
    "Support Vector Machine Algorithm Classification "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b40d0af1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Support Vector Machine employs kerneling; a technique of mapping data to a high-dimensional space\n",
    "# using kernel functions to create a hyperplane as a separator to separate a linearly unseparable data\n",
    "# This hyperplane separates the target into classes which can further be classified by\n",
    "# the decision boundary equation\n",
    "# the output of svm is either a 0 or 1 for a binary classification\n",
    "# or a one-vs-rest approach for multiclass classification\n",
    "# svm does not predict probability for the outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c976cbe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SVM model instant and preprocessing\n",
    "svm_model = LinearSVC(C=0.01, class_weight='balanced', random_state=45, loss='hinge', max_iter=3000)\n",
    "# hyperparameter tuning helps to prevent overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a0cb3da7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# features matrix, response vector and preprocessing\n",
    "X = data.iloc[:, 1:30]\n",
    "y = data.iloc[:,30]\n",
    "X = preprocessing.StandardScaler().fit(X).transform(X)\n",
    "X = preprocessing.normalize(X, norm='l1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8599aa7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train / test dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=.30, random_state=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "29697388",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train model and evaluate\n",
    "class_w = compute_sample_weight('balanced', y_train)\n",
    "svm_model.fit(X_train, y_train)\n",
    "yhat = svm_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "707dcd5b",
   "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>Class</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>158708</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188612</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191155</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203206</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10913</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Class  predict\n",
       "158708      0        0\n",
       "188612      0        0\n",
       "191155      0        0\n",
       "203206      0        0\n",
       "10913       0        0"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.DataFrame(y_train)\n",
    "train_df['predict'] = yhat\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "c1f3020e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluation of train model\n",
    "from sklearn.metrics import log_loss, jaccard_score\n",
    "train_loss = log_loss(y_train, yhat)\n",
    "train_ja = jaccard_score(y_train, yhat, pos_label=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "6a1f21ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8211992326447138 0.9761892801527101\n"
     ]
    }
   ],
   "source": [
    "print(train_loss, train_ja)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "7569e597",
   "metadata": {},
   "outputs": [],
   "source": [
    "## how does class weight computed affect fitting of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "eee39b85",
   "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>Class</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11783</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199531</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226972</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229378</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101019</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Class  predict\n",
       "11783       0        0\n",
       "199531      0        1\n",
       "226972      0        0\n",
       "229378      0        0\n",
       "101019      0        0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluation of test model\n",
    "yhat_test = svm_model.predict(X_test)\n",
    "test_df = pd.DataFrame(y_test)\n",
    "test_df['predict'] = yhat_test\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "0389f9ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Test Evaluation Score]: Logloss 0.84, Jaccard Index 0.84\n"
     ]
    }
   ],
   "source": [
    "test_loss = log_loss(y_test, yhat_test)\n",
    "test_ja = jaccard_score(y_test, yhat_test, pos_label=0)\n",
    "print('[Test Evaluation Score]: Logloss {0:.2f}, Jaccard Index {0:.2f}'.format(test_loss, test_ja))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "5cc97be9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[83224  2054]\n",
      " [   14   151]]\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrix for SVM test model evaluation\n",
    "cm_svm = confusion_matrix(y_test, yhat_test, labels=[0,1])\n",
    "print(cm_svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "bd307314",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix without Normalization\n",
      "[[83224  2054]\n",
      " [   14   151]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix normalized for test model evaluation\n",
    "plt.figure\n",
    "plot_confusion_mat(cm_svm, classes=['L=0', 'F=1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "4380834d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix Normalized\n",
      "[[0.97591407 0.02408593]\n",
      " [0.08484848 0.91515152]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix normalized for test model evaluation\n",
    "plt.figure\n",
    "plot_confusion_mat(cm_svm, normalize=True, classes=['L=0', 'F=1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "8e4539bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conclusion on SVM model\n",
    "# interpreting the confusion matrix:\n",
    "# our svm model did better than knn, and this is because of the standardisation and hyperparameters \n",
    "# applied to the fitting the model\n",
    "# svm_model predicted 98% of class 0, L=0 as true and 92% of class 1, F=1 as true\n",
    "# compare to knn_model's ~1.0% and 68% respectively"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a0a5623",
   "metadata": {},
   "source": [
    "End of Classification Project\n",
    "\n",
    "In this particular project, I obtained creditcard transaction of a certain bank\n",
    "\n",
    "From Kaggle.com to train SVM and KNN models to predict which transactions are legitimate or Fraudulent\n",
    "\n",
    "By Blibo Albert @bliboalbert <- github && LinkedIn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0da1ee2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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