SVM_Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM Classifier using 100% of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For data management\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "from sklearn import svm\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# For plotting\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_in = open(\"X.pickle\", \"rb\")\n",
    "X = pickle.load(pickle_in)\n",
    "pickle_in = open(\"y.pickle\", \"rb\")\n",
    "y = pickle.load(pickle_in)\n",
    "\n",
    "#X an y for 75%, 50%, and 25% of the dataset\n",
    "X_75, X_extra, y_75, y_extra = train_test_split(X, y, train_size=.75)\n",
    "X_50, X_extra, y_50, y_extra = train_test_split(X, y, train_size=.50)\n",
    "X_25, X_extra, y_25, y_extra = train_test_split(X, y, train_size=.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split 100 % of the dataset into Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building and fitting SVM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_100 = svm.SVC()\n",
    "model_100.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate performance for using 100% of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.961556\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9615558570782451"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 1. Get and print a baseline accuracy score.\n",
    "y_pred = model_100.predict(X_test)\n",
    "accuracy = model_100.score(X_test, y_test)\n",
    "print(\"Accuracy %f\" % accuracy)\n",
    "metrics.accuracy_score(y_true=y_test, y_pred=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.96      0.96      1489\n",
      "           1       0.99      0.98      0.99      1466\n",
      "           2       0.94      0.94      0.94      1467\n",
      "\n",
      "    accuracy                           0.96      4422\n",
      "   macro avg       0.96      0.96      0.96      4422\n",
      "weighted avg       0.96      0.96      0.96      4422\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM Classifier using 75% of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_75, y_75, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_75 = svm.SVC()\n",
    "model_75.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate performance for using 75% of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.951161\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9511606873681037"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 1. Get and print a baseline accuracy score.\n",
    "y_pred = model_75.predict(X_test)\n",
    "accuracy = model_75.score(X_test, y_test)\n",
    "print(\"Accuracy %f\" % accuracy)\n",
    "metrics.accuracy_score(y_true=y_test, y_pred=y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM Classifier using 50% of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_50, y_50, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_50 = svm.SVC()\n",
    "model_50.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate performance for using 50% of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.939846\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.939846223428313"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 1. Get and print a baseline accuracy score.\n",
    "y_pred = model_50.predict(X_test)\n",
    "accuracy = model_50.score(X_test, y_test)\n",
    "print(\"Accuracy %f\" % accuracy)\n",
    "metrics.accuracy_score(y_true=y_test, y_pred=y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM Classifier using 25% of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_25, y_25, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_25 = svm.SVC()\n",
    "model_25.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate performance for using 25% of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.933996\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.933996383363472"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 1. Get and print a baseline accuracy score.\n",
    "y_pred = model_25.predict(X_test)\n",
    "accuracy = model_25.score(X_test, y_test)\n",
    "print(\"Accuracy %f\" % accuracy)\n",
    "metrics.accuracy_score(y_true=y_test, y_pred=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17687, 4096)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
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