├── Assignment1 ├── Housing Price data set.csv └── q5.ipynb ├── Assignment2 ├── Housing Price data set.csv ├── q1.ipynb └── q2.ipynb ├── Assignment3 ├── SCO Assignment 3.pdf ├── examination.csv ├── microchip.csv ├── q1.ipynb └── q2.ipynb ├── Assignment4 ├── examination.csv ├── microchip.csv ├── q1.ipynb └── q2.ipynb ├── Assignment5 ├── NonRiverPoints.txt ├── RiverPoints.txt ├── images │ ├── 1.png │ ├── 2.png │ ├── 3.png │ └── 4.png ├── q1.ipynb ├── sco_5_q2.ipynb └── spam.csv ├── Assignment6 ├── dataset │ ├── Luv │ │ ├── Luv_0.jpg │ │ ├── Luv_1.jpg │ │ ├── Luv_2.jpg │ │ ├── Luv_3.jpg │ │ ├── Luv_4.jpg │ │ ├── Luve_1.jpg │ │ └── Luvv_0.jpg │ ├── Parag │ │ ├── parag_0.jpg │ │ ├── parag_1.jpg │ │ ├── parag_2.jpg │ │ ├── parag_3.jpg │ │ ├── parag_4.jpg │ │ └── parag_5.jpg │ ├── Pasha │ │ ├── pasha_0.jpg │ │ ├── pasha_1.jpg │ │ ├── pasha_2.jpg │ │ ├── pasha_3.jpg │ │ └── pasha_4.jpg │ ├── Pranav │ │ ├── pranav_0.jpg │ │ ├── pranav_1.jpg │ │ ├── pranav_2.jpg │ │ ├── pranav_3.jpg │ │ └── pranav_4.jpg │ ├── Pravin │ │ ├── praveen_0.jpg │ │ ├── praveen_1.jpg │ │ ├── praveen_2.jpg │ │ ├── praveen_3.jpg │ │ └── praveen_4.jpg │ ├── Ravi │ │ ├── IMG_20190404_155931.jpg │ │ ├── IMG_20190404_155943.jpg │ │ ├── IMG_20190404_155946.jpg │ │ ├── IMG_20190404_155948.jpg │ │ ├── IMG_20190404_155950.jpg │ │ ├── IMG_20190404_155952.jpg │ │ ├── IMG_20190404_155953.jpg │ │ ├── ravi_0.jpg │ │ ├── ravi_1.jpg │ │ ├── ravi_2.jpg │ │ ├── ravi_3.jpg │ │ └── ravi_4.jpg │ ├── Sidharth │ │ ├── sidharth_0.jpg │ │ ├── sidharth_1.jpg │ │ ├── sidharth_2.jpg │ │ ├── sidharth_3.jpg │ │ └── sidharth_4.jpg │ ├── Varun │ │ ├── IMG_20190404_160019.jpg │ │ ├── IMG_20190404_160020.jpg │ │ ├── IMG_20190404_160021.jpg │ │ ├── varun_0.jpg │ │ ├── varun_1.jpg │ │ ├── varun_2.jpg │ │ ├── varun_3.jpg │ │ └── varun_4.jpg │ ├── amit │ │ ├── IMG_20190404_155812.jpg │ │ ├── IMG_20190404_155814.jpg │ │ ├── amit_0.jpg │ │ ├── amit_1.jpg │ │ ├── amit_2.jpg │ │ ├── amit_3.jpg │ │ ├── amit_4.jpg │ │ └── amit_5.jpg │ ├── aswin │ │ ├── aswin_0.jpg │ │ ├── aswin_1.jpg │ │ ├── aswin_2.jpg │ │ ├── aswin_3.jpg │ │ └── aswin_4.jpg │ └── balaji │ │ ├── IMG_20190404155811.jpg │ │ ├── balaji_0.jpg │ │ ├── balaji_1.jpg │ │ ├── balaji_2.jpg │ │ ├── balaji_3.jpg │ │ └── balaji_4.jpg ├── face_dataset │ ├── s1 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s10 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s11 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s12 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s13 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s14 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s15 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s16 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s17 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s18 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s19 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s2 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s20 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s21 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s22 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s23 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s24 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s25 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s26 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s27 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s28 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s29 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s3 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s30 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s31 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s32 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s33 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s34 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s35 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s36 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s37 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s38 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s39 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s4 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s40 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s5 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s6 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s7 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ ├── s8 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm │ └── s9 │ │ ├── 1.pgm │ │ ├── 10.pgm │ │ ├── 2.pgm │ │ ├── 3.pgm │ │ ├── 4.pgm │ │ ├── 5.pgm │ │ ├── 6.pgm │ │ ├── 7.pgm │ │ ├── 8.pgm │ │ └── 9.pgm ├── sco_6_q1.ipynb └── sco_6_q2.ipynb ├── Assignment7 ├── sco_7_q1.ipynb ├── train-images.idx3-ubyte └── train-labels.idx1-ubyte ├── Assignment8 ├── sco_8_q1.ipynb ├── sco_8_q2.ipynb └── sco_8_q3.ipynb └── README.md /Assignment1/q5.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 5, 6 | "metadata": { 7 | "colab": {}, 8 | "colab_type": "code", 9 | "id": "CHxrIZH_QuDO" 10 | }, 11 | "outputs": [], 12 | "source": [ 13 | "import numpy as np\n", 14 | "import pandas as pd\n", 15 | "import matplotlib.pyplot as plt\n", 16 | "from mpl_toolkits.mplot3d import Axes3D" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 49, 22 | "metadata": { 23 | "colab": { 24 | "base_uri": "https://localhost:8080/", 25 | "height": 195 26 | }, 27 | "colab_type": "code", 28 | "id": "B_ORs-0xer0n", 29 | "outputId": "dac0af30-bbea-4d4d-8f1c-a1a20a92e0e3" 30 | }, 31 | "outputs": [ 32 | { 33 | "data": { 34 | "text/html": [ 35 | "
\n", 36 | "\n", 49 | "\n", 50 | " \n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | "
pricelotsizebedroomsbathrmsstoriesgaragepl
0-0.9782390.3227320.047192-0.5689730.2215010.357239
1-1.109312-0.530526-1.308947-0.568973-0.930304-0.803788
2-0.697368-0.9640740.047192-0.568973-0.930304-0.803788
3-0.2854250.6917090.047192-0.5689730.221501-0.803788
4-0.2667000.557955-1.308947-0.568973-0.930304-0.803788
\n", 109 | "
" 110 | ], 111 | "text/plain": [ 112 | " price lotsize bedrooms bathrms stories garagepl\n", 113 | "0 -0.978239 0.322732 0.047192 -0.568973 0.221501 0.357239\n", 114 | "1 -1.109312 -0.530526 -1.308947 -0.568973 -0.930304 -0.803788\n", 115 | "2 -0.697368 -0.964074 0.047192 -0.568973 -0.930304 -0.803788\n", 116 | "3 -0.285425 0.691709 0.047192 -0.568973 0.221501 -0.803788\n", 117 | "4 -0.266700 0.557955 -1.308947 -0.568973 -0.930304 -0.803788" 118 | ] 119 | }, 120 | "execution_count": 49, 121 | "metadata": {}, 122 | "output_type": "execute_result" 123 | } 124 | ], 125 | "source": [ 126 | "# Fetching data\n", 127 | "data = pd.read_csv('Housing Price data set.csv')\n", 128 | "data=data.drop(['Unnamed: 0','driveway','recroom','fullbase','airco','prefarea', 'gashw'],axis=1)\n", 129 | "# di = {'no': 0, 'yes': 1}\n", 130 | "# data=data.replace({'driveway':di,'recroom':di,'fullbase':di,'gashw':di,'airco':di,'prefarea':di})\n", 131 | "mean = data.mean()[0]\n", 132 | "stddev = data.std()[0]\n", 133 | "data = (data - data.mean())/data.std()\n", 134 | "data.head()" 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": 51, 140 | "metadata": { 141 | "colab": {}, 142 | "colab_type": "code", 143 | "id": "sFceNVEyQuDj" 144 | }, 145 | "outputs": [], 146 | "source": [ 147 | "# Separate out the Feature and Target matrices\n", 148 | "data=np.asarray(data)\n", 149 | "Y=data[:,0:1]\n", 150 | "X=data[:,1:]\n", 151 | "one = np.ones(len(X))\n", 152 | "one = [one]\n", 153 | "one = np.asarray(one)\n", 154 | "X = np.concatenate((one.T,X),axis=1)\n", 155 | "split = int(.7 * X.shape[0])\n", 156 | "X_test = X[split+1:,:]\n", 157 | "X = X[:split+1, :]\n", 158 | "Y_test = Y[split+1:,:]\n", 159 | "Y = Y[:split+1, :]" 160 | ] 161 | }, 162 | { 163 | "cell_type": "code", 164 | "execution_count": 76, 165 | "metadata": { 166 | "colab": {}, 167 | "colab_type": "code", 168 | "id": "7r4JwRSsQuDm" 169 | }, 170 | "outputs": [], 171 | "source": [ 172 | "def computeCost(X,y,theta):\n", 173 | " tobesummed = np.power(((X.dot(theta.T))-y),2)\n", 174 | " return np.sum(tobesummed)/(2 * len(X))\n", 175 | "\n", 176 | "def denormalise_price(price):\n", 177 | " global mean\n", 178 | " global stddev\n", 179 | " ret = price * stddev + mean\n", 180 | " return ret\n", 181 | "\n", 182 | "def computeError(predicted, actual):\n", 183 | " error = 0\n", 184 | " for i in range(len(predicted)):\n", 185 | " error += abs(actual[i] - predicted[i]) / actual[i]\n", 186 | " error /= len(actual)\n", 187 | " return error[0]*100" 188 | ] 189 | }, 190 | { 191 | "cell_type": "code", 192 | "execution_count": 111, 193 | "metadata": { 194 | "colab": {}, 195 | "colab_type": "code", 196 | "id": "JHGN_9F_QuDu" 197 | }, 198 | "outputs": [], 199 | "source": [ 200 | "# Gradient Descent\n", 201 | "def gradientDescent(X,y,theta,iters,alpha):\n", 202 | " cost = []\n", 203 | " for i in range(iters):\n", 204 | " theta = theta - (alpha/len(X)) * np.sum(X * (X @ theta.T - y), axis=0)\n", 205 | " cost.append(computeCost(X, y, theta)) \n", 206 | " return theta,cost\n" 207 | ] 208 | }, 209 | { 210 | "cell_type": "code", 211 | "execution_count": 115, 212 | "metadata": { 213 | "colab": { 214 | "base_uri": "https://localhost:8080/", 215 | "height": 235 216 | }, 217 | "colab_type": "code", 218 | "id": "Dd8fiOYBQuDw", 219 | "outputId": "46943a23-d363-4918-b99d-a2c6b560144b" 220 | }, 221 | "outputs": [ 222 | { 223 | "name": "stdout", 224 | "output_type": "stream", 225 | "text": [ 226 | "(1, 6)\n", 227 | "After 1000 iterations and learning rate = 0.5 we get the following Co-efficient matrix : \n", 228 | "[[-0.03768711 0.43348361 0.05074058 0.28641966 0.26934991 0.19757631]]\n", 229 | "(1, 6)\n", 230 | "Cost after training : 0.2326158585544259\n", 231 | "16.990232558666996\n" 232 | ] 233 | } 234 | ], 235 | "source": [ 236 | "# Set initial values for hyper-parameters\n", 237 | "theta = np.zeros([1,X.shape[1]])\n", 238 | "alpha = 0.5\n", 239 | "iters = 1000\n", 240 | "print('After ',iters,' iterations and learning rate = ',alpha,' we get the following Co-efficient matrix : ')\n", 241 | "# Running the Gradient Descent Function\n", 242 | "\n", 243 | "g,cost = gradientDescent(X,Y,theta,iters,alpha)\n", 244 | "print(g)\n", 245 | "print(g.shape)\n", 246 | "# Calculating cost after tuning of hyper Parameters\n", 247 | "finalCost = computeCost(X,Y,g)\n", 248 | "error = 0\n", 249 | "print('Cost after training : ',finalCost)\n", 250 | "Y_pred = X_test.dot(g.T)\n", 251 | "error = computeError(denormalise_price(Y_pred),denormalise_price(Y_test))\n", 252 | "print(error)" 253 | ] 254 | }, 255 | { 256 | "cell_type": "code", 257 | "execution_count": 84, 258 | "metadata": { 259 | "colab": { 260 | "base_uri": "https://localhost:8080/", 261 | "height": 310 262 | }, 263 | "colab_type": "code", 264 | "id": "yqu9KZO_QuD0", 265 | "outputId": "3cf2dd09-bd42-4343-b142-dc1b65ac20e9" 266 | }, 267 | "outputs": [ 268 | { 269 | "data": { 270 | "text/plain": [ 271 | "Text(0.5, 1.0, 'Cost vs. Training Epoch')" 272 | ] 273 | }, 274 | "execution_count": 84, 275 | "metadata": {}, 276 | "output_type": "execute_result" 277 | }, 278 | { 279 | "data": { 280 | "image/png": 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\n", 281 | "text/plain": [ 282 | "
" 283 | ] 284 | }, 285 | "metadata": { 286 | "needs_background": "light" 287 | }, 288 | "output_type": "display_data" 289 | } 290 | ], 291 | "source": [ 292 | "# Iteration vs Error(Cost) plot\n", 293 | "fig, ax = plt.subplots() \n", 294 | "ax.plot(np.arange(iters), cost, 'r') \n", 295 | "ax.set_xlabel('Iterations') \n", 296 | "ax.set_ylabel('Cost') \n", 297 | "ax.set_title('Cost vs. Training Epoch') " 298 | ] 299 | }, 300 | { 301 | "cell_type": "code", 302 | "execution_count": 78, 303 | "metadata": { 304 | "colab": { 305 | "base_uri": "https://localhost:8080/", 306 | "height": 229 307 | }, 308 | "colab_type": "code", 309 | "id": "9weddsqCQuD4", 310 | "outputId": "9b376078-a557-443d-9692-c15e702b5a79" 311 | }, 312 | "outputs": [ 313 | { 314 | "name": "stdout", 315 | "output_type": "stream", 316 | "text": [ 317 | "Co-efficient matrix after applying Normal Equation : \n", 318 | "[[-0.03768711 0.43348361 0.05074058 0.28641966 0.26934991 0.19757631]]\n", 319 | "Cost after applying Normal Equation : 0.23261585855442593\n", 320 | "16.99023255866699\n" 321 | ] 322 | } 323 | ], 324 | "source": [ 325 | "# Normal Equation\n", 326 | "theta_norm = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(Y)\n", 327 | "print(\"Co-efficient matrix after applying Normal Equation : \")\n", 328 | "print(theta_norm.T)\n", 329 | "\n", 330 | "finalCost_norm = computeCost(X,Y,theta_norm.T)\n", 331 | "print(\"Cost after applying Normal Equation : \",finalCost_norm)\n", 332 | "\n", 333 | "Y_pred = X_test.dot(theta_norm)\n", 334 | "error = computeError(denormalise_price(Y_pred),denormalise_price(Y_test))\n", 335 | "print(error)" 336 | ] 337 | }, 338 | { 339 | "cell_type": "code", 340 | "execution_count": 71, 341 | "metadata": { 342 | "colab": { 343 | "base_uri": "https://localhost:8080/", 344 | "height": 50 345 | }, 346 | "colab_type": "code", 347 | "id": "WHUiXxEcQuD8", 348 | "outputId": "2162aa06-8b40-4883-dd6b-594bfbb1a107" 349 | }, 350 | "outputs": [ 351 | { 352 | "name": "stdout", 353 | "output_type": "stream", 354 | "text": [ 355 | "Difference in Final Cost for Normal Equation and Gradient Descent(learning rate= 0.5 ,iterations= 1000 ) : \n", 356 | "2.7755575615628914e-17\n" 357 | ] 358 | } 359 | ], 360 | "source": [ 361 | "print(\"Difference in Final Cost for Normal Equation and Gradient Descent(learning rate=\",alpha,\",iterations=\",iters,\") : \")\n", 362 | "print(abs(finalCost_norm-finalCost))" 363 | ] 364 | } 365 | ], 366 | "metadata": { 367 | "colab": { 368 | "collapsed_sections": [], 369 | "name": "Assgn1_ques5.ipynb", 370 | "provenance": [], 371 | "version": "0.3.2" 372 | }, 373 | "kernelspec": { 374 | "display_name": "Python 3", 375 | "language": "python", 376 | "name": "python3" 377 | }, 378 | "language_info": { 379 | "codemirror_mode": { 380 | "name": "ipython", 381 | "version": 3 382 | }, 383 | "file_extension": ".py", 384 | "mimetype": "text/x-python", 385 | "name": "python", 386 | "nbconvert_exporter": "python", 387 | "pygments_lexer": "ipython3", 388 | "version": "3.6.5" 389 | } 390 | }, 391 | "nbformat": 4, 392 | "nbformat_minor": 1 393 | } 394 | -------------------------------------------------------------------------------- /Assignment3/SCO Assignment 3.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/luvk1412/MachineLearning/424e6d745fde45eaf0d210e3d83d72f0344e2b3a/Assignment3/SCO Assignment 3.pdf -------------------------------------------------------------------------------- /Assignment3/examination.csv: -------------------------------------------------------------------------------- 1 | exam1,exam2,accept 2 | 34.62365962451697,78.0246928153624,0 3 | 30.28671076822607,43.89499752400101,0 4 | 35.84740876993872,72.90219802708364,0 5 | 60.18259938620976,86.30855209546826,1 6 | 79.0327360507101,75.3443764369103,1 7 | 45.08327747668339,56.3163717815305,0 8 | 61.10666453684766,96.51142588489624,1 9 | 75.02474556738889,46.55401354116538,1 10 | 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-------------------------------------------------------------------------------- /Assignment3/microchip.csv: -------------------------------------------------------------------------------- 1 | result1,result2,accepted 2 | 0.051267,0.69956,1 3 | -0.092742,0.68494,1 4 | -0.21371,0.69225,1 5 | -0.375,0.50219,1 6 | -0.51325,0.46564,1 7 | -0.52477,0.2098,1 8 | -0.39804,0.034357,1 9 | -0.30588,-0.19225,1 10 | 0.016705,-0.40424,1 11 | 0.13191,-0.51389,1 12 | 0.38537,-0.56506,1 13 | 0.52938,-0.5212,1 14 | 0.63882,-0.24342,1 15 | 0.73675,-0.18494,1 16 | 0.54666,0.48757,1 17 | 0.322,0.5826,1 18 | 0.16647,0.53874,1 19 | -0.046659,0.81652,1 20 | -0.17339,0.69956,1 21 | -0.47869,0.63377,1 22 | -0.60541,0.59722,1 23 | -0.62846,0.33406,1 24 | -0.59389,0.005117,1 25 | -0.42108,-0.27266,1 26 | -0.11578,-0.39693,1 27 | 0.20104,-0.60161,1 28 | 0.46601,-0.53582,1 29 | 0.67339,-0.53582,1 30 | -0.13882,0.54605,1 31 | -0.29435,0.77997,1 32 | -0.26555,0.96272,1 33 | -0.16187,0.8019,1 34 | -0.17339,0.64839,1 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"source": [ 9 | "import numpy as np\n", 10 | "import pandas as pd\n", 11 | "import math\n", 12 | "import matplotlib.pyplot as plt\n", 13 | "from mpl_toolkits.mplot3d import Axes3D\n", 14 | "from sklearn.utils import shuffle" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 2, 20 | "metadata": {}, 21 | "outputs": [ 22 | { 23 | "data": { 24 | "text/html": [ 25 | "
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exam1exam2accept
034.62366078.0246930
130.28671143.8949980
235.84740972.9021980
360.18259986.3085521
479.03273675.3443761
\n", 81 | "
" 82 | ], 83 | "text/plain": [ 84 | " exam1 exam2 accept\n", 85 | "0 34.623660 78.024693 0\n", 86 | "1 30.286711 43.894998 0\n", 87 | "2 35.847409 72.902198 0\n", 88 | "3 60.182599 86.308552 1\n", 89 | "4 79.032736 75.344376 1" 90 | ] 91 | }, 92 | "execution_count": 2, 93 | "metadata": {}, 94 | "output_type": "execute_result" 95 | } 96 | ], 97 | "source": [ 98 | "# Fetching data\n", 99 | "data = pd.read_csv('examination.csv')\n", 100 | "mean = data.mean()\n", 101 | "stddev = data.std()\n", 102 | "# data = shuffle(data)\n", 103 | "data.head()\n", 104 | "# print(data)" 105 | ] 106 | }, 107 | { 108 | "cell_type": "code", 109 | "execution_count": 3, 110 | "metadata": {}, 111 | "outputs": [ 112 | { 113 | "name": "stdout", 114 | "output_type": "stream", 115 | "text": [ 116 | "(29, 3) (71, 3)\n" 117 | ] 118 | } 119 | ], 120 | "source": [ 121 | "# Separate out the Feature and Target matrices\n", 122 | "data=np.asarray(data)\n", 123 | "np.random.shuffle(data)\n", 124 | "Y=data[:,-1:]\n", 125 | "X=data[:,:-1]\n", 126 | "\n", 127 | "# Nature of Data\n", 128 | "\n", 129 | "# fig, ax = plt.subplots() \n", 130 | "# for i in range(len(X)):\n", 131 | "# if Y[i] == 1:\n", 132 | "# ax.plot(X[i][0], X[i][1], marker='o',color='b')\n", 133 | "# else:\n", 134 | "# ax.plot(X[i][0], X[i][1], marker='o',color='r')\n", 135 | "\n", 136 | "one = np.ones((len(X),1))\n", 137 | "X = np.concatenate((one,X),axis=1)\n", 138 | "split_ratio = 0.7\n", 139 | "split = int(split_ratio * X.shape[0])\n", 140 | "\n", 141 | "X_train = X[:split+1, :]\n", 142 | "Y_train = Y[:split+1, :]\n", 143 | "\n", 144 | "X_test = X[split+1:,:]\n", 145 | "Y_test = Y[split+1:,:]\n", 146 | "\n", 147 | "# Extra features can be added for more accuracy\n", 148 | "\n", 149 | "# X_train = np.concatenate((X_train,X_train[:,1:]**2), axis=1)\n", 150 | "# X_test = np.concatenate((X_test,X_test[:,1:]**2), axis=1)\n", 151 | "\n", 152 | "\n", 153 | "print(X_test.shape, X_train.shape)" 154 | ] 155 | }, 156 | { 157 | "cell_type": "code", 158 | "execution_count": 4, 159 | "metadata": {}, 160 | "outputs": [], 161 | "source": [ 162 | "def computeError(predicted, actual):\n", 163 | " error = 0\n", 164 | " for i in range(len(predicted)):\n", 165 | " if(actual[i] != predicted[i]):\n", 166 | " error+=1\n", 167 | " error /= len(actual)\n", 168 | " return error*100\n", 169 | "\n", 170 | "def getHessian(X, gx):\n", 171 | " identity = np.identity(gx.shape[0])\n", 172 | " identity = (gx*(1-gx))*identity\n", 173 | " return X.T@identity@X\n", 174 | "\n", 175 | "\n", 176 | "def getJacobian(X,gx,y):\n", 177 | " return X.T@(gx - y)\n", 178 | " \n", 179 | "def getG(thetax):\n", 180 | "# print(\"hi\",thetax)\n", 181 | " return (1 / (1 + np.exp(-1 * thetax)))" 182 | ] 183 | }, 184 | { 185 | "cell_type": "code", 186 | "execution_count": 5, 187 | "metadata": {}, 188 | "outputs": [], 189 | "source": [ 190 | "# Newton's Method using deltalearning\n", 191 | "def NewtonMethod(X,y,theta,iters):\n", 192 | " cost = []\n", 193 | " for i in range(iters):\n", 194 | " gx = getG(X @ theta)\n", 195 | " iden = np.identity(gx.shape[0])\n", 196 | " iden = (gx*(1-gx))*iden\n", 197 | "# print(iden)\n", 198 | " theta = theta - (1 / len(X))* np.linalg.inv(getHessian(X,gx))@(getJacobian(X,gx,y))\n", 199 | " return theta,cost\n" 200 | ] 201 | }, 202 | { 203 | "cell_type": "code", 204 | "execution_count": 6, 205 | "metadata": {}, 206 | "outputs": [ 207 | { 208 | "name": "stdout", 209 | "output_type": "stream", 210 | "text": [ 211 | "Accuracy : 86.20689655172414\n", 212 | "Error : 13.793103448275861\n" 213 | ] 214 | } 215 | ], 216 | "source": [ 217 | "theta = np.zeros([X_train.shape[1],1])\n", 218 | "iters = 20\n", 219 | "# print(computeCost(X_train, Y_train, theta).shape)\n", 220 | "g,cost = NewtonMethod(X_train,Y_train,theta,iters)\n", 221 | "# print(g)\n", 222 | "Y_pred = getG(X_test.dot(g))\n", 223 | "\n", 224 | "for i in range(len(Y_pred)):\n", 225 | " if(Y_pred[i] >= 0.5):\n", 226 | " Y_pred[i] = 1\n", 227 | " else:\n", 228 | " Y_pred[i] = 0\n", 229 | "# print(Y_pred,Y_test)\n", 230 | "error = computeError(Y_pred,Y_test)\n", 231 | "print(\"Accuracy : \", 100 - error)\n", 232 | "print(\"Error : \", error)" 233 | ] 234 | }, 235 | { 236 | "cell_type": "code", 237 | "execution_count": 7, 238 | "metadata": {}, 239 | "outputs": [ 240 | { 241 | "data": { 242 | "text/plain": [ 243 | "[]" 244 | ] 245 | }, 246 | "execution_count": 7, 247 | "metadata": {}, 248 | "output_type": "execute_result" 249 | }, 250 | { 251 | "data": { 252 | "image/png": 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\n", 253 | "text/plain": [ 254 | "
" 255 | ] 256 | }, 257 | "metadata": { 258 | "needs_background": "light" 259 | }, 260 | "output_type": "display_data" 261 | } 262 | ], 263 | "source": [ 264 | "fig1,ax1 = plt.subplots()\n", 265 | "X_tmp = X[:,1:]\n", 266 | "Y_tmp = Y\n", 267 | "Y_out = []\n", 268 | "\n", 269 | "# Plotting for straight line Features\n", 270 | "\n", 271 | "for i in range(len(X_tmp)):\n", 272 | " Y_out.append((-1*g[0]-g[1]*X_tmp[i][0])/g[2])\n", 273 | " if Y_tmp[i] == 1:\n", 274 | " ax1.plot(X_tmp[i][0], X_tmp[i][1], marker='o',color='b')\n", 275 | " else:\n", 276 | " ax1.plot(X_tmp[i][0], X_tmp[i][1], marker='o',color='r')\n", 277 | "Y_out = np.asarray(Y_out)\n", 278 | "ax1.plot(X_tmp[:,:1],Y_out)\n", 279 | "\n", 280 | "\n", 281 | "#PLotting linear regression if extra features added\n", 282 | "\n", 283 | "# Y_out = []\n", 284 | "# X_out = []\n", 285 | "# for i in range(len(X_tmp)):\n", 286 | "# X_out.append(X_tmp[i][0])\n", 287 | "# X_out.sort()\n", 288 | "# X__out = []\n", 289 | "# for i in range(len(X_tmp)):\n", 290 | "# if(g[2]**2 - 4 * g[4] * (g[0]+g[1]*X_out[i]+g[3]*X_out[i]**2) > 0):\n", 291 | "# X__out.append(X_out[i])\n", 292 | "# Y_out.append((-g[2] + math.sqrt(g[2]**2 - 4 * g[4] * (g[0]+g[1]*X_out[i]+g[3]*X_out[i]**2))) / (2 * g[4]))\n", 293 | "# if Y_tmp[i] == 1:\n", 294 | "# ax1.plot(X_tmp[i][0], X_tmp[i][1], marker='o',color='b')\n", 295 | "# else:\n", 296 | "# ax1.plot(X_tmp[i][0], X_tmp[i][1], marker='o',color='r')\n", 297 | "# l = len(X__out)\n", 298 | "# for i in range(l-1, -1, -1):\n", 299 | "# X__out.append(X__out[i])\n", 300 | "# Y_out.append((-g[2] - math.sqrt(g[2]**2 - 4 * g[4] * (g[0]+g[1]*X__out[i]+g[3]*X__out[i]**2))) / (2 * g[4]))\n", 301 | " \n", 302 | "# Y_out = np.asarray(Y_out)\n", 303 | "# ax1.plot(X__out,Y_out,color='y')" 304 | ] 305 | } 306 | ], 307 | "metadata": { 308 | "kernelspec": { 309 | "display_name": "Python 3", 310 | "language": "python", 311 | "name": "python3" 312 | }, 313 | "language_info": { 314 | "codemirror_mode": { 315 | "name": "ipython", 316 | "version": 3 317 | }, 318 | "file_extension": ".py", 319 | "mimetype": "text/x-python", 320 | "name": "python", 321 | "nbconvert_exporter": "python", 322 | "pygments_lexer": "ipython3", 323 | "version": "3.6.5" 324 | } 325 | }, 326 | "nbformat": 4, 327 | "nbformat_minor": 2 328 | } 329 | -------------------------------------------------------------------------------- /Assignment5/NonRiverPoints.txt: -------------------------------------------------------------------------------- 1 | 427 383 2 | 133 40 3 | 39 60 4 | 235 43 5 | 312 166 6 | 142 370 7 | 128 0 8 | 509 332 9 | 369 190 10 | 112 384 11 | 311 467 12 | 474 281 13 | 141 202 14 | 236 124 15 | 459 109 16 | 117 398 17 | 221 304 18 | 305 456 19 | 300 76 20 | 82 395 21 | 290 118 22 | 366 157 23 | 15 14 24 | 50 338 25 | 478 84 26 | 160 116 27 | 454 481 28 | 107 416 29 | 2 416 30 | 269 473 31 | 119 162 32 | 98 508 33 | 233 185 34 | 183 227 35 | 246 339 36 | 467 107 37 | 385 160 38 | 53 205 39 | 125 410 40 | 359 42 41 | 470 384 42 | 56 410 43 | 431 54 44 | 215 297 45 | 22 111 46 | 84 297 47 | 298 401 48 | 196 298 49 | 447 229 50 | 392 310 51 | 347 208 52 | 78 398 53 | 191 200 54 | 447 20 55 | 468 226 56 | 76 149 57 | 428 402 58 | 60 112 59 | 505 498 60 | 368 423 61 | 426 218 62 | 504 235 63 | 53 211 64 | 231 24 65 | 253 477 66 | 33 358 67 | 59 323 68 | 154 336 69 | 36 483 70 | 131 178 71 | 467 507 72 | 43 416 73 | 365 51 74 | 445 107 75 | 223 286 76 | 70 510 77 | 157 502 78 | 262 136 79 | 466 500 80 | 29 442 81 | 163 386 82 | 44 131 83 | 425 205 84 | 495 56 85 | 49 377 86 | 472 506 87 | 7 165 88 | 507 176 89 | 298 81 90 | 178 112 91 | 44 453 92 | 32 325 93 | 290 416 94 | 107 363 95 | 161 398 96 | 278 452 97 | 264 164 98 | 171 329 99 | 392 17 100 | 47 199 101 | -------------------------------------------------------------------------------- /Assignment5/RiverPoints.txt: -------------------------------------------------------------------------------- 1 | 280 201 2 | 158 165 3 | 291 212 4 | 105 168 5 | 54 170 6 | 318 222 7 | 128 0 8 | 126 171 9 | 369 190 10 | 178 167 11 | 223 172 12 | 177 165 13 | 192 172 14 | 478 167 15 | 178 163 16 | 291 218 17 | 344 215 18 | 250 193 19 | 223 187 20 | 82 395 21 | 331 215 22 | 298 215 23 | 70 171 24 | 488 154 25 | 74 181 26 | 113 177 27 | 458 173 28 | 167 167 29 | 2 416 30 | 256 198 31 | 119 162 32 | 299 219 33 | 233 185 34 | 157 159 35 | 22 155 36 | 314 221 37 | 385 160 38 | 288 204 39 | 480 155 40 | 359 42 41 | 28 156 42 | 465 166 43 | 431 54 44 | 141 171 45 | 7 150 46 | 290 209 47 | 118 184 48 | 86 172 49 | 300 216 50 | 196 162 51 | -------------------------------------------------------------------------------- /Assignment5/images/1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/luvk1412/MachineLearning/424e6d745fde45eaf0d210e3d83d72f0344e2b3a/Assignment5/images/1.png -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 71, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import numpy as np\n", 10 | "import pandas as pd\n", 11 | "import matplotlib.pyplot as plt\n", 12 | "from nltk.tokenize import word_tokenize\n", 13 | "from nltk.corpus import stopwords\n", 14 | "from nltk.stem import PorterStemmer" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 82, 20 | "metadata": {}, 21 | "outputs": [ 22 | { 23 | "name": "stdout", 24 | "output_type": "stream", 25 | "text": [ 26 | "5572\n" 27 | ] 28 | } 29 | ], 30 | "source": [ 31 | "# Taking input and creating train test splits\n", 32 | "data = pd.read_csv('spam.csv',encoding = 'latin-1')\n", 33 | "data = data.drop(['Unnamed: 2','Unnamed: 3','Unnamed: 4'], axis=1)\n", 34 | "data = np.asarray(data)\n", 35 | "split_ratio = 0.7\n", 36 | "split = int(split_ratio * data.shape[0])\n", 37 | "data_train = data[:split+1, :]\n", 38 | "data_test = data[split+1:,:]\n", 39 | "print(len(data))" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 73, 45 | "metadata": {}, 46 | "outputs": [], 47 | "source": [ 48 | "#Data Preprocessing\n", 49 | "def Preprocessing(mail):\n", 50 | " mail = mail.lower()\n", 51 | " mail = word_tokenize(mail)\n", 52 | " stopword = stopwords.words('english')\n", 53 | " words = []\n", 54 | " for word in mail:\n", 55 | " if word not in stopword and len(word) > 2: \n", 56 | " words.append(word)\n", 57 | " stemmer = PorterStemmer()\n", 58 | " words = [stemmer.stem(word) for word in words]\n", 59 | " return words" 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": 81, 65 | "metadata": {}, 66 | "outputs": [ 67 | { 68 | "name": "stdout", 69 | "output_type": "stream", 70 | "text": [ 71 | "6674\n", 72 | "2176\n", 73 | "5171\n", 74 | "519 3382 3901\n" 75 | ] 76 | } 77 | ], 78 | "source": [ 79 | "#String counts for spam and ham words\n", 80 | "message_train = []\n", 81 | "for data in data_train:\n", 82 | " words = Preprocessing(data[1])\n", 83 | " message_train.append(words)\n", 84 | "spam = {}\n", 85 | "ham = {}\n", 86 | "all_words = {}\n", 87 | "spam_word_ct = 0\n", 88 | "ham_word_ct = 0\n", 89 | "total_words = 0\n", 90 | "spam_ct = 0\n", 91 | "ham_ct = 0\n", 92 | "\n", 93 | "for i,message in enumerate(message_train):\n", 94 | " if data_train[i][0] == 'spam':\n", 95 | " spam_ct += 1\n", 96 | " for word in message:\n", 97 | " if word not in all_words:\n", 98 | " all_words[word] = 1\n", 99 | " else:\n", 100 | " all_words[word] += 1\n", 101 | " total_words += 1\n", 102 | " spam_word_ct += 1\n", 103 | " if word not in spam:\n", 104 | " spam[word] = 1\n", 105 | " else:\n", 106 | " spam[word] += 1\n", 107 | " if data_train[i][0] == 'ham':\n", 108 | " ham_ct += 1\n", 109 | " for word in message:\n", 110 | " if word not in all_words:\n", 111 | " all_words[word] = 1\n", 112 | " else:\n", 113 | " all_words[word] += 1\n", 114 | " total_words += 1\n", 115 | " ham_word_ct += 1\n", 116 | " if word not in ham:\n", 117 | " ham[word] = 1\n", 118 | " else:\n", 119 | " ham[word] += 1\n", 120 | " \n", 121 | "print(len(all_words))\n", 122 | "print(len(spam))\n", 123 | "print(len(ham))\n", 124 | "print(spam_ct, ham_ct, spam_ct+ham_ct)" 125 | ] 126 | }, 127 | { 128 | "cell_type": "code", 129 | "execution_count": 68, 130 | "metadata": {}, 131 | "outputs": [], 132 | "source": [ 133 | "def Predict(mail):\n", 134 | " probaility_spam = spam_ct / len(data_train)\n", 135 | " probaility_ham = ham_ct / len(data_train)\n", 136 | " words = Preprocessing(mail)\n", 137 | " for word in words:\n", 138 | " if word in spam:\n", 139 | " probaility_spam *= (spam[word] / spam_word_ct) / (all_words[word] / total_words)\n", 140 | " else:\n", 141 | " #Laplacian smoothening with alpha = 1\n", 142 | " probaility_spam *= (1 / (spam_word_ct+len(spam)))\n", 143 | " if word in all_words:\n", 144 | " probaility_spam /= (all_words[word] / total_words)\n", 145 | " else:\n", 146 | " probaility_spam /= (1 / (total_words+len(all_words)))\n", 147 | " if word in ham:\n", 148 | " probaility_ham *= (ham[word] / ham_word_ct) / (all_words[word] / total_words)\n", 149 | " else:\n", 150 | " probaility_ham *= (1 / (ham_word_ct+len(ham)))\n", 151 | " if word in all_words:\n", 152 | " probaility_ham /= (all_words[word] / total_words)\n", 153 | " else:\n", 154 | " probaility_ham /= (1 / (total_words+len(all_words)))\n", 155 | " if probaility_spam >= probaility_ham:\n", 156 | " return 'spam'\n", 157 | " else:\n", 158 | " return 'ham'" 159 | ] 160 | }, 161 | { 162 | "cell_type": "code", 163 | "execution_count": 69, 164 | "metadata": {}, 165 | "outputs": [ 166 | { 167 | "name": "stdout", 168 | "output_type": "stream", 169 | "text": [ 170 | "Confusion Matrix\n", 171 | "[[1350. 93.]\n", 172 | " [ 4. 224.]]\n", 173 | "Accuracy Percentage : 94.19509275882704\n" 174 | ] 175 | } 176 | ], 177 | "source": [ 178 | "# Testing\n", 179 | "confusion_matrix = np.zeros((2,2))\n", 180 | "for data in data_test:\n", 181 | " predictions.append(Predict(data[1]))\n", 182 | " if data[0] == 'ham':\n", 183 | " if Predict(data[1]) == 'ham':\n", 184 | " confusion_matrix[0][0] += 1\n", 185 | " else:\n", 186 | " confusion_matrix[0][1] += 1\n", 187 | " else:\n", 188 | " if Predict(data[1]) == 'spam':\n", 189 | " confusion_matrix[1][1] += 1\n", 190 | " else:\n", 191 | " confusion_matrix[1][0] += 1\n", 192 | "print('Confusion Matrix')\n", 193 | "print(confusion_matrix)\n", 194 | "print('Accuracy Percentage : ', (confusion_matrix[0][0]+ confusion_matrix[1][1]) / len(data_test) * 100)" 195 | ] 196 | } 197 | ], 198 | "metadata": { 199 | "kernelspec": { 200 | "display_name": "Python 3", 201 | "language": "python", 202 | "name": "python3" 203 | }, 204 | "language_info": { 205 | "codemirror_mode": { 206 | "name": "ipython", 207 | "version": 3 208 | }, 209 | "file_extension": ".py", 210 | "mimetype": "text/x-python", 211 | "name": "python", 212 | "nbconvert_exporter": "python", 213 | "pygments_lexer": "ipython3", 214 | "version": "3.6.5" 215 | } 216 | }, 217 | "nbformat": 4, 218 | "nbformat_minor": 2 219 | } 220 | -------------------------------------------------------------------------------- /Assignment5/spam.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/luvk1412/MachineLearning/424e6d745fde45eaf0d210e3d83d72f0344e2b3a/Assignment5/spam.csv -------------------------------------------------------------------------------- /Assignment6/dataset/Luv/Luv_0.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/luvk1412/MachineLearning/424e6d745fde45eaf0d210e3d83d72f0344e2b3a/Assignment6/dataset/Luv/Luv_0.jpg -------------------------------------------------------------------------------- /Assignment6/dataset/Luv/Luv_1.jpg: 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baysian method 19 | 20 | Assignment 6 : 21 | - Q1 : Face Detection using PCA 22 | - Q2 : Face Detection using LDA 23 | 24 | Assignment 7 : SVM from scratch using Simplified SMO algorithm. Source : http://cs229.stanford.edu/materials/smo.pdf 25 | 26 | Assignment 8 : 27 | - Q1 : Fuzzy expert system for house mortgage using scikit fuzzy 28 | - Q2 : Fuzzy expert system for Universty prediction using scikit fuzzy 29 | - Q3 : Fuzzy addition and function evaluation from scratch 30 | --------------------------------------------------------------------------------