mainWithMean.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Course Reccomendation System "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import annotations # To prevent Subscript for class \"list\" will generate runtime exception; enclose type annotation in quotes\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import make_blobs\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import math\n",
    "import scipy\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from Kmeans import * \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_state = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applying Pre-processing steps on data: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data Structures and Algorithms</th>\n",
       "      <th>Computer Architecture\\n</th>\n",
       "      <th>Discrete Mathematics\\n</th>\n",
       "      <th>Economics</th>\n",
       "      <th>Programming-2</th>\n",
       "      <th>Machine Learning\\n</th>\n",
       "      <th>Mathematics For Machine Learning\\n</th>\n",
       "      <th>Visual Recognition\\n</th>\n",
       "      <th>Natural Language Processing\\n</th>\n",
       "      <th>Reinforcement Learning</th>\n",
       "      <th>...</th>\n",
       "      <th>Digital CMOS VLSI Design</th>\n",
       "      <th>System design with FPGA</th>\n",
       "      <th>ASIC design</th>\n",
       "      <th>VLSI Architecture Design\\n</th>\n",
       "      <th>High level synthesis and optimization of Digital Circuits</th>\n",
       "      <th>Digital Sociology</th>\n",
       "      <th>Privacy in the Digital Age</th>\n",
       "      <th>Technology Ethics and AI</th>\n",
       "      <th>Techno-economics of networks</th>\n",
       "      <th>The Web and the Mind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.620098</td>\n",
       "      <td>0.572638</td>\n",
       "      <td>0.603495</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251538</td>\n",
       "      <td>0.246316</td>\n",
       "      <td>0.214805</td>\n",
       "      <td>0.234868</td>\n",
       "      <td>0.168056</td>\n",
       "      <td>0.318243</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.423286</td>\n",
       "      <td>0.396622</td>\n",
       "      <td>0.510897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.572638</td>\n",
       "      <td>0.603495</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251538</td>\n",
       "      <td>0.246316</td>\n",
       "      <td>0.214805</td>\n",
       "      <td>0.234868</td>\n",
       "      <td>0.168056</td>\n",
       "      <td>0.318243</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.423286</td>\n",
       "      <td>0.396622</td>\n",
       "      <td>0.510897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251538</td>\n",
       "      <td>0.246316</td>\n",
       "      <td>0.214805</td>\n",
       "      <td>0.234868</td>\n",
       "      <td>0.168056</td>\n",
       "      <td>0.318243</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.423286</td>\n",
       "      <td>0.396622</td>\n",
       "      <td>0.510897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.65</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.572638</td>\n",
       "      <td>0.603495</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251538</td>\n",
       "      <td>0.246316</td>\n",
       "      <td>0.214805</td>\n",
       "      <td>0.234868</td>\n",
       "      <td>0.168056</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.452708</td>\n",
       "      <td>0.423286</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.510897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.572638</td>\n",
       "      <td>0.603495</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251538</td>\n",
       "      <td>0.246316</td>\n",
       "      <td>0.214805</td>\n",
       "      <td>0.234868</td>\n",
       "      <td>0.168056</td>\n",
       "      <td>0.318243</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.423286</td>\n",
       "      <td>0.396622</td>\n",
       "      <td>0.510897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.572638</td>\n",
       "      <td>0.603495</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251538</td>\n",
       "      <td>0.246316</td>\n",
       "      <td>0.214805</td>\n",
       "      <td>0.234868</td>\n",
       "      <td>0.168056</td>\n",
       "      <td>0.318243</td>\n",
       "      <td>0.452708</td>\n",
       "      <td>0.423286</td>\n",
       "      <td>0.396622</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>0.60</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251538</td>\n",
       "      <td>0.246316</td>\n",
       "      <td>0.214805</td>\n",
       "      <td>0.234868</td>\n",
       "      <td>0.168056</td>\n",
       "      <td>0.318243</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.423286</td>\n",
       "      <td>0.396622</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.620098</td>\n",
       "      <td>0.572638</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251538</td>\n",
       "      <td>0.246316</td>\n",
       "      <td>0.214805</td>\n",
       "      <td>0.234868</td>\n",
       "      <td>0.168056</td>\n",
       "      <td>0.318243</td>\n",
       "      <td>0.452708</td>\n",
       "      <td>0.423286</td>\n",
       "      <td>0.396622</td>\n",
       "      <td>0.510897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.620098</td>\n",
       "      <td>0.572638</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251538</td>\n",
       "      <td>0.246316</td>\n",
       "      <td>0.214805</td>\n",
       "      <td>0.234868</td>\n",
       "      <td>0.168056</td>\n",
       "      <td>0.318243</td>\n",
       "      <td>0.452708</td>\n",
       "      <td>0.423286</td>\n",
       "      <td>0.396622</td>\n",
       "      <td>0.510897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>162 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Data Structures and Algorithms  Computer Architecture\\n  \\\n",
       "0                              0.30                     0.85   \n",
       "1                              0.70                     0.80   \n",
       "2                              0.70                     0.00   \n",
       "3                              0.65                     0.70   \n",
       "4                              0.70                     0.60   \n",
       "..                              ...                      ...   \n",
       "157                            0.00                     1.00   \n",
       "158                            1.00                     0.00   \n",
       "159                            0.60                     0.30   \n",
       "160                            0.80                     0.60   \n",
       "161                            0.40                     0.70   \n",
       "\n",
       "     Discrete Mathematics\\n  Economics  Programming-2  Machine Learning\\n  \\\n",
       "0                      0.85       0.90           0.70                0.70   \n",
       "1                      0.20       0.80           0.80                0.50   \n",
       "2                      1.00       1.00           1.00                1.00   \n",
       "3                      0.20       0.75           0.75                0.75   \n",
       "4                      0.20       0.00           0.80                0.50   \n",
       "..                      ...        ...            ...                 ...   \n",
       "157                    0.50       0.70           0.60                0.50   \n",
       "158                    0.00       0.70           0.70                0.00   \n",
       "159                    0.40       0.90           0.80                0.70   \n",
       "160                    0.20       0.40           0.70                0.70   \n",
       "161                    0.90       0.40           0.40                0.70   \n",
       "\n",
       "     Mathematics For Machine Learning\\n  Visual Recognition\\n  \\\n",
       "0                                  0.80              0.620098   \n",
       "1                                  0.10              0.200000   \n",
       "2                                  1.00              0.800000   \n",
       "3                                  0.75              0.750000   \n",
       "4                                  0.60              0.400000   \n",
       "..                                  ...                   ...   \n",
       "157                                0.30              0.700000   \n",
       "158                                0.00              0.000000   \n",
       "159                                0.40              0.500000   \n",
       "160                                0.80              0.620098   \n",
       "161                                0.90              0.620098   \n",
       "\n",
       "     Natural Language Processing\\n  Reinforcement Learning  ...  \\\n",
       "0                         0.572638                0.603495  ...   \n",
       "1                         0.572638                0.603495  ...   \n",
       "2                         0.900000                1.000000  ...   \n",
       "3                         0.572638                0.603495  ...   \n",
       "4                         0.400000                0.200000  ...   \n",
       "..                             ...                     ...  ...   \n",
       "157                       0.572638                0.603495  ...   \n",
       "158                       0.572638                0.603495  ...   \n",
       "159                       0.400000                0.200000  ...   \n",
       "160                       0.572638                0.700000  ...   \n",
       "161                       0.572638                0.800000  ...   \n",
       "\n",
       "     Digital CMOS VLSI Design   System design with FPGA   ASIC design   \\\n",
       "0                     0.251538                  0.246316      0.214805   \n",
       "1                     0.251538                  0.246316      0.214805   \n",
       "2                     0.251538                  0.246316      0.214805   \n",
       "3                     0.251538                  0.246316      0.214805   \n",
       "4                     0.000000                  0.000000      0.000000   \n",
       "..                         ...                       ...           ...   \n",
       "157                   0.251538                  0.246316      0.214805   \n",
       "158                   0.251538                  0.246316      0.214805   \n",
       "159                   0.251538                  0.246316      0.214805   \n",
       "160                   0.251538                  0.246316      0.214805   \n",
       "161                   0.251538                  0.246316      0.214805   \n",
       "\n",
       "     VLSI Architecture Design\\n  \\\n",
       "0                      0.234868   \n",
       "1                      0.234868   \n",
       "2                      0.234868   \n",
       "3                      0.234868   \n",
       "4                      0.000000   \n",
       "..                          ...   \n",
       "157                    0.234868   \n",
       "158                    0.234868   \n",
       "159                    0.234868   \n",
       "160                    0.234868   \n",
       "161                    0.234868   \n",
       "\n",
       "     High level synthesis and optimization of Digital Circuits   \\\n",
       "0                                             0.168056            \n",
       "1                                             0.168056            \n",
       "2                                             0.168056            \n",
       "3                                             0.168056            \n",
       "4                                             0.000000            \n",
       "..                                                 ...            \n",
       "157                                           0.168056            \n",
       "158                                           0.168056            \n",
       "159                                           0.168056            \n",
       "160                                           0.168056            \n",
       "161                                           0.168056            \n",
       "\n",
       "     Digital Sociology   Privacy in the Digital Age  \\\n",
       "0              0.318243                    0.600000   \n",
       "1              0.318243                    0.500000   \n",
       "2              0.318243                    0.400000   \n",
       "3              0.800000                    0.452708   \n",
       "4              0.000000                    0.000000   \n",
       "..                  ...                         ...   \n",
       "157            0.318243                    0.000000   \n",
       "158            0.318243                    0.452708   \n",
       "159            0.318243                    1.000000   \n",
       "160            0.318243                    0.452708   \n",
       "161            0.318243                    0.452708   \n",
       "\n",
       "     Technology Ethics and AI   Techno-economics of networks   \\\n",
       "0                     0.423286                       0.396622   \n",
       "1                     0.423286                       0.396622   \n",
       "2                     0.423286                       0.396622   \n",
       "3                     0.423286                       0.800000   \n",
       "4                     0.000000                       0.000000   \n",
       "..                         ...                            ...   \n",
       "157                   0.423286                       0.396622   \n",
       "158                   0.423286                       0.396622   \n",
       "159                   0.423286                       0.396622   \n",
       "160                   0.423286                       0.396622   \n",
       "161                   0.423286                       0.396622   \n",
       "\n",
       "     The Web and the Mind   \n",
       "0                 0.510897  \n",
       "1                 0.510897  \n",
       "2                 0.510897  \n",
       "3                 0.510897  \n",
       "4                 0.000000  \n",
       "..                     ...  \n",
       "157               0.510897  \n",
       "158               0.200000  \n",
       "159               1.000000  \n",
       "160               0.510897  \n",
       "161               0.510897  \n",
       "\n",
       "[162 rows x 25 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_excel('Course Recommendation System.xlsx')\n",
    "df=df.drop(['ID','Start time','Completion time','Email','Name','Name(not mandatory)\\n' ], axis=1)\n",
    "df['mean'] = df.mean(axis=0,skipna=True,numeric_only=True)\n",
    "df = df.apply(lambda x : x.fillna(x.mean(skipna=True)),axis=0)\n",
    "df.drop(axis=\"columns\",labels=[\"mean\"],inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data Structures and Algorithms                                1.0\n",
       "Computer Architecture\\n                                       1.0\n",
       "Discrete Mathematics\\n                                        1.0\n",
       "Economics                                                     0.0\n",
       "Programming-2                                                 1.0\n",
       "Machine Learning\\n                                            1.0\n",
       "Mathematics For Machine Learning\\n                            1.0\n",
       "Visual Recognition\\n                                          1.0\n",
       "Natural Language Processing\\n                                 1.0\n",
       "Reinforcement Learning                                        1.0\n",
       "Software Production Engineering\\n                             1.0\n",
       "Computer Graphics\\n                                           1.0\n",
       "Data Visualization\\n                                          1.0\n",
       "Programming Languages\\n                                       1.0\n",
       "Cloud Computing\\n                                             1.0\n",
       "Digital CMOS VLSI Design                                      0.0\n",
       "System design with FPGA                                       0.0\n",
       "ASIC design                                                   0.0\n",
       "VLSI Architecture Design\\n                                    0.0\n",
       "High level synthesis and optimization of Digital Circuits     0.0\n",
       "Digital Sociology                                             0.0\n",
       "Privacy in the Digital Age                                    1.0\n",
       "Technology Ethics and AI                                      1.0\n",
       "Techno-economics of networks                                  1.0\n",
       "The Web and the Mind                                          1.0\n",
       "Name: 90, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[90]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wm = df['The Web and the Mind ']\n",
    "df_sorted = df_wm.sort_values( ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sorted = np.array(df_sorted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sorted[26]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Data Structures and Algorithms', 'Computer Architecture\\n',\n",
       "       'Discrete Mathematics\\n', 'Economics', 'Programming-2',\n",
       "       'Machine Learning\\n', 'Mathematics For Machine Learning\\n',\n",
       "       'Visual Recognition\\n', 'Natural Language Processing\\n',\n",
       "       'Reinforcement Learning', 'Software Production Engineering\\n',\n",
       "       'Computer Graphics\\n', 'Data Visualization\\n',\n",
       "       'Programming Languages\\n', 'Cloud Computing\\n',\n",
       "       'Digital CMOS VLSI Design ', 'System design with FPGA ', 'ASIC design ',\n",
       "       'VLSI Architecture Design\\n',\n",
       "       'High level synthesis and optimization of Digital Circuits ',\n",
       "       'Digital Sociology ', 'Privacy in the Digital Age',\n",
       "       'Technology Ethics and AI ', 'Techno-economics of networks ',\n",
       "       'The Web and the Mind '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cluster_to_matrix(X_train,y_train,num_cluster):\n",
    "    k=Kmeans(K=num_cluster,iter=150,p=2,random_state=random_state)\n",
    "    y_pred,centroids=k.predict(np.array(X_train),choice=0)\n",
    "    k.centroids=np.array(centroids)\n",
    "    empty_cluster = []\n",
    "    for idx,i in enumerate(k.clusters):\n",
    "        if len(i)==0:\n",
    "            empty_cluster.append(idx)\n",
    "\n",
    "    X_train2 = pd.concat([X_train,y_train,pd.DataFrame(y_pred,columns=[\"label\"],dtype='int')],axis=\"columns\")\n",
    "    df_cluster_rep = X_train2.groupby(['label'], as_index=False).mean()\n",
    "    df_cluster_rep.drop(labels=\"label\",axis=\"columns\",inplace=True)\n",
    "    matrix = df_cluster_rep.to_numpy()\n",
    "    final_matrix = []\n",
    "    j = 0 \n",
    "    for i in range(num_cluster):\n",
    "        if i in empty_cluster:\n",
    "            final_matrix.append(np.zeros(25))\n",
    "        else:\n",
    "            final_matrix.append(matrix[j])\n",
    "            j+=1\n",
    "\n",
    "    return np.array(final_matrix),k "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performing the task on dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[[df.columns[0],df.columns[1],df.columns[2],df.columns[3],df.columns[4]]]\n",
    "y = df.drop(labels=[\"Data Structures and Algorithms\",\"Computer Architecture\\n\",\"Discrete Mathematics\\n\",\"Economics\",\"Programming-2\"],axis=\"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Data Structures and Algorithms</th>\n",
       "      <th>Computer Architecture\\n</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.70</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.80</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>1.00</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.65</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.70</td>\n",
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       "      <td>0.80</td>\n",
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       "      <td>0.60</td>\n",
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       "      <th>158</th>\n",
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       "      <td>0.40</td>\n",
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       "    <tr>\n",
       "      <th>161</th>\n",
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       "      <td>0.70</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
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       "<p>162 rows × 5 columns</p>\n",
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       "     Data Structures and Algorithms  Computer Architecture\\n  \\\n",
       "0                              0.30                     0.85   \n",
       "1                              0.70                     0.80   \n",
       "2                              0.70                     0.00   \n",
       "3                              0.65                     0.70   \n",
       "4                              0.70                     0.60   \n",
       "..                              ...                      ...   \n",
       "157                            0.00                     1.00   \n",
       "158                            1.00                     0.00   \n",
       "159                            0.60                     0.30   \n",
       "160                            0.80                     0.60   \n",
       "161                            0.40                     0.70   \n",
       "\n",
       "     Discrete Mathematics\\n  Economics  Programming-2  \n",
       "0                      0.85       0.90           0.70  \n",
       "1                      0.20       0.80           0.80  \n",
       "2                      1.00       1.00           1.00  \n",
       "3                      0.20       0.75           0.75  \n",
       "4                      0.20       0.00           0.80  \n",
       "..                      ...        ...            ...  \n",
       "157                    0.50       0.70           0.60  \n",
       "158                    0.00       0.70           0.70  \n",
       "159                    0.40       0.90           0.80  \n",
       "160                    0.20       0.40           0.70  \n",
       "161                    0.90       0.40           0.40  \n",
       "\n",
       "[162 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.58841</td>\n",
       "      <td>0.543363</td>\n",
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       "      <td>0.251538</td>\n",
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       "      <td>0.234868</td>\n",
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       "      <td>0.603495</td>\n",
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       "      <td>0.58841</td>\n",
       "      <td>0.543363</td>\n",
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       "      <td>0.246316</td>\n",
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       "      <td>0.234868</td>\n",
       "      <td>0.168056</td>\n",
       "      <td>0.318243</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.246316</td>\n",
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       "      <td>0.318243</td>\n",
       "      <td>0.400000</td>\n",
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       "      <td>0.572638</td>\n",
       "      <td>0.603495</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.200000</td>\n",
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       "      <td>0.600000</td>\n",
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       "      <td>0.168056</td>\n",
       "      <td>0.800000</td>\n",
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       "      <td>0.200000</td>\n",
       "      <td>0.20000</td>\n",
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       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.423286</td>\n",
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       "      <th>158</th>\n",
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       "      <td>1.00000</td>\n",
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       "      <td>0.452708</td>\n",
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       "      <td>0.400000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.58841</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.572587</td>\n",
       "      <td>0.251538</td>\n",
       "      <td>0.246316</td>\n",
       "      <td>0.214805</td>\n",
       "      <td>0.234868</td>\n",
       "      <td>0.168056</td>\n",
       "      <td>0.318243</td>\n",
       "      <td>0.452708</td>\n",
       "      <td>0.423286</td>\n",
       "      <td>0.396622</td>\n",
       "      <td>0.510897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.620098</td>\n",
       "      <td>0.572638</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.675943</td>\n",
       "      <td>0.456217</td>\n",
       "      <td>0.58841</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.572587</td>\n",
       "      <td>0.251538</td>\n",
       "      <td>0.246316</td>\n",
       "      <td>0.214805</td>\n",
       "      <td>0.234868</td>\n",
       "      <td>0.168056</td>\n",
       "      <td>0.318243</td>\n",
       "      <td>0.452708</td>\n",
       "      <td>0.423286</td>\n",
       "      <td>0.396622</td>\n",
       "      <td>0.510897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>162 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Machine Learning\\n  Mathematics For Machine Learning\\n  \\\n",
       "0                  0.70                                0.80   \n",
       "1                  0.50                                0.10   \n",
       "2                  1.00                                1.00   \n",
       "3                  0.75                                0.75   \n",
       "4                  0.50                                0.60   \n",
       "..                  ...                                 ...   \n",
       "157                0.50                                0.30   \n",
       "158                0.00                                0.00   \n",
       "159                0.70                                0.40   \n",
       "160                0.70                                0.80   \n",
       "161                0.70                                0.90   \n",
       "\n",
       "     Visual Recognition\\n  Natural Language Processing\\n  \\\n",
       "0                0.620098                       0.572638   \n",
       "1                0.200000                       0.572638   \n",
       "2                0.800000                       0.900000   \n",
       "3                0.750000                       0.572638   \n",
       "4                0.400000                       0.400000   \n",
       "..                    ...                            ...   \n",
       "157              0.700000                       0.572638   \n",
       "158              0.000000                       0.572638   \n",
       "159              0.500000                       0.400000   \n",
       "160              0.620098                       0.572638   \n",
       "161              0.620098                       0.572638   \n",
       "\n",
       "     Reinforcement Learning  Software Production Engineering\\n  \\\n",
       "0                  0.603495                           0.675943   \n",
       "1                  0.603495                           0.675943   \n",
       "2                  1.000000                           0.000000   \n",
       "3                  0.603495                           0.300000   \n",
       "4                  0.200000                           0.700000   \n",
       "..                      ...                                ...   \n",
       "157                0.603495                           0.675943   \n",
       "158                0.603495                           0.500000   \n",
       "159                0.200000                           0.700000   \n",
       "160                0.700000                           0.400000   \n",
       "161                0.800000                           0.675943   \n",
       "\n",
       "     Computer Graphics\\n  Data Visualization\\n  Programming Languages\\n  \\\n",
       "0               0.456217               0.58841                 0.543363   \n",
       "1               0.800000               0.58841                 0.543363   \n",
       "2               0.600000               0.00000                 0.000000   \n",
       "3               0.200000               0.58841                 0.600000   \n",
       "4               0.200000               0.20000                 0.500000   \n",
       "..                   ...                   ...                      ...   \n",
       "157             0.000000               0.58841                 0.543363   \n",
       "158             1.000000               1.00000                 0.000000   \n",
       "159             0.700000               0.70000                 0.700000   \n",
       "160             0.400000               0.58841                 0.600000   \n",
       "161             0.456217               0.58841                 0.400000   \n",
       "\n",
       "     Cloud Computing\\n  Digital CMOS VLSI Design   System design with FPGA   \\\n",
       "0             0.572587                   0.251538                  0.246316   \n",
       "1             0.572587                   0.251538                  0.246316   \n",
       "2             0.000000                   0.251538                  0.246316   \n",
       "3             0.572587                   0.251538                  0.246316   \n",
       "4             0.300000                   0.000000                  0.000000   \n",
       "..                 ...                        ...                       ...   \n",
       "157           0.572587                   0.251538                  0.246316   \n",
       "158           0.572587                   0.251538                  0.246316   \n",
       "159           0.800000                   0.251538                  0.246316   \n",
       "160           0.572587                   0.251538                  0.246316   \n",
       "161           0.572587                   0.251538                  0.246316   \n",
       "\n",
       "     ASIC design   VLSI Architecture Design\\n  \\\n",
       "0        0.214805                    0.234868   \n",
       "1        0.214805                    0.234868   \n",
       "2        0.214805                    0.234868   \n",
       "3        0.214805                    0.234868   \n",
       "4        0.000000                    0.000000   \n",
       "..            ...                         ...   \n",
       "157      0.214805                    0.234868   \n",
       "158      0.214805                    0.234868   \n",
       "159      0.214805                    0.234868   \n",
       "160      0.214805                    0.234868   \n",
       "161      0.214805                    0.234868   \n",
       "\n",
       "     High level synthesis and optimization of Digital Circuits   \\\n",
       "0                                             0.168056            \n",
       "1                                             0.168056            \n",
       "2                                             0.168056            \n",
       "3                                             0.168056            \n",
       "4                                             0.000000            \n",
       "..                                                 ...            \n",
       "157                                           0.168056            \n",
       "158                                           0.168056            \n",
       "159                                           0.168056            \n",
       "160                                           0.168056            \n",
       "161                                           0.168056            \n",
       "\n",
       "     Digital Sociology   Privacy in the Digital Age  \\\n",
       "0              0.318243                    0.600000   \n",
       "1              0.318243                    0.500000   \n",
       "2              0.318243                    0.400000   \n",
       "3              0.800000                    0.452708   \n",
       "4              0.000000                    0.000000   \n",
       "..                  ...                         ...   \n",
       "157            0.318243                    0.000000   \n",
       "158            0.318243                    0.452708   \n",
       "159            0.318243                    1.000000   \n",
       "160            0.318243                    0.452708   \n",
       "161            0.318243                    0.452708   \n",
       "\n",
       "     Technology Ethics and AI   Techno-economics of networks   \\\n",
       "0                     0.423286                       0.396622   \n",
       "1                     0.423286                       0.396622   \n",
       "2                     0.423286                       0.396622   \n",
       "3                     0.423286                       0.800000   \n",
       "4                     0.000000                       0.000000   \n",
       "..                         ...                            ...   \n",
       "157                   0.423286                       0.396622   \n",
       "158                   0.423286                       0.396622   \n",
       "159                   0.423286                       0.396622   \n",
       "160                   0.423286                       0.396622   \n",
       "161                   0.423286                       0.396622   \n",
       "\n",
       "     The Web and the Mind   \n",
       "0                 0.510897  \n",
       "1                 0.510897  \n",
       "2                 0.510897  \n",
       "3                 0.510897  \n",
       "4                 0.000000  \n",
       "..                     ...  \n",
       "157               0.510897  \n",
       "158               0.200000  \n",
       "159               1.000000  \n",
       "160               0.510897  \n",
       "161               0.510897  \n",
       "\n",
       "[162 rows x 20 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.05, shuffle=True,random_state=random_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix,kmeans_model = cluster_to_matrix(X_train,y_train,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.77743947, 0.6895    , 0.59953923, 0.59320881, 0.86579796,\n",
       "        0.80915302, 0.73684492, 0.65039914, 0.59657487, 0.65739057,\n",
       "        0.64830975, 0.49684393, 0.64835402, 0.60851032, 0.61463668,\n",
       "        0.30615385, 0.28003509, 0.2268961 , 0.29001096, 0.19636574,\n",
       "        0.30011261, 0.48297561, 0.41474762, 0.40636261, 0.53237179],\n",
       "       [0.86666667, 0.85833333, 0.87083333, 0.70833333, 0.8375    ,\n",
       "        0.79388707, 0.7616958 , 0.48169918, 0.51965241, 0.60203901,\n",
       "        0.62963836, 0.48175706, 0.6201708 , 0.51334808, 0.47191125,\n",
       "        0.16769231, 0.18473684, 0.1611039 , 0.17615132, 0.12604167,\n",
       "        0.33716216, 0.40180553, 0.42385714, 0.34746622, 0.54059829],\n",
       "       [0.72      , 0.54      , 0.54      , 0.58      , 0.82      ,\n",
       "        0.64133224, 0.46451748, 0.49401951, 0.44905526, 0.36139817,\n",
       "        0.67037736, 0.37124339, 0.45536397, 0.68867257, 0.6490348 ,\n",
       "        0.20061538, 0.19852632, 0.14592208, 0.15394737, 0.16722222,\n",
       "        0.2272973 , 0.41054166, 0.40931429, 0.39864865, 0.40435897],\n",
       "       [0.82142857, 0.63095238, 0.47142857, 0.53333333, 0.80952381,\n",
       "        0.81015821, 0.65583749, 0.55905691, 0.5257406 , 0.49147489,\n",
       "        0.62398922, 0.47975561, 0.55969713, 0.48984408, 0.54585405,\n",
       "        0.27186813, 0.27258145, 0.27089672, 0.25758145, 0.20992063,\n",
       "        0.36972329, 0.55778769, 0.5195034 , 0.47014157, 0.61791819],\n",
       "       [0.70833333, 0.54583333, 0.56583333, 0.64166667, 0.89166667,\n",
       "        0.82083333, 0.66666667, 0.58002438, 0.60903892, 0.66841439,\n",
       "        0.65664308, 0.33727514, 0.53587163, 0.36806785, 0.52796017,\n",
       "        0.25102564, 0.24754386, 0.14320346, 0.15657895, 0.11203704,\n",
       "        0.39397523, 0.57151041, 0.36358333, 0.38941441, 0.5480235 ],\n",
       "       [0.86470588, 0.68529412, 0.71176471, 0.49117647, 0.87352941,\n",
       "        0.78274478, 0.71309338, 0.63765853, 0.45504876, 0.5598516 ,\n",
       "        0.80915649, 0.35513539, 0.55925173, 0.58001041, 0.69886447,\n",
       "        0.1479638 , 0.14489164, 0.126356  , 0.13815789, 0.09885621,\n",
       "        0.33505564, 0.39507352, 0.4535042 , 0.38644674, 0.39272247],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ],\n",
       "       [0.86111111, 0.765     , 0.67222222, 0.63055556, 0.89444444,\n",
       "        0.89277687, 0.82598679, 0.70334959, 0.73880372, 0.69524823,\n",
       "        0.69053983, 0.51283804, 0.54827053, 0.51204523, 0.48378383,\n",
       "        0.27863248, 0.28684211, 0.33600289, 0.2749269 , 0.20825617,\n",
       "        0.32301051, 0.31271989, 0.42571429, 0.45108859, 0.51716524]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 25)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from SVD import * \n",
    "u,s,sigma,V_trans = ReducedSVD(matrix,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix_clean = u@sigma@V_trans\n",
    "matrix_clean = matrix_clean.astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(datapoint,kmeans,matrix_clean):\n",
    "    ''' \n",
    "        @input: Takes in a datapoint, kmeans object, and information matrix \n",
    "        @output: Returns: rating sorted with index for column name mapping\n",
    "    '''\n",
    "    y_pred = kmeans.predictPoint(datapoint)\n",
    "    inferred_full = matrix_clean[y_pred]\n",
    "    inferred = inferred_full[5:]\n",
    "    sorted_ratings = []\n",
    "    for idx,element in enumerate(inferred):\n",
    "        sorted_ratings.append([element,idx+5])\n",
    "    sorted_ratings.sort()\n",
    "    sorted_ratings.reverse()\n",
    "    return sorted_ratings "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 5)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Machine Learning\\n</th>\n",
       "      <th>Mathematics For Machine Learning\\n</th>\n",
       "      <th>Visual Recognition\\n</th>\n",
       "      <th>Natural Language Processing\\n</th>\n",
       "      <th>Reinforcement Learning</th>\n",
       "      <th>Software Production Engineering\\n</th>\n",
       "      <th>Computer Graphics\\n</th>\n",
       "      <th>Data Visualization\\n</th>\n",
       "      <th>Programming Languages\\n</th>\n",
       "      <th>Cloud Computing\\n</th>\n",
       "      <th>Digital CMOS VLSI Design</th>\n",
       "      <th>System design with FPGA</th>\n",
       "      <th>ASIC design</th>\n",
       "      <th>VLSI Architecture Design\\n</th>\n",
       "      <th>High level synthesis and optimization of Digital Circuits</th>\n",
       "      <th>Digital Sociology</th>\n",
       "      <th>Privacy in the Digital Age</th>\n",
       "      <th>Technology Ethics and AI</th>\n",
       "      <th>Techno-economics of networks</th>\n",
       "      <th>The Web and the Mind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.572638</td>\n",
       "      <td>0.603495</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.572587</td>\n",
       "      <td>0.251538</td>\n",
       "      <td>0.246316</td>\n",
       "      <td>0.214805</td>\n",
       "      <td>0.234868</td>\n",
       "      <td>0.168056</td>\n",
       "      <td>0.318243</td>\n",
       "      <td>0.452708</td>\n",
       "      <td>0.423286</td>\n",
       "      <td>0.396622</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.620098</td>\n",
       "      <td>0.572638</td>\n",
       "      <td>0.603495</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.456217</td>\n",
       "      <td>0.58841</td>\n",
       "      <td>0.543363</td>\n",
       "      <td>0.572587</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.168056</td>\n",
       "      <td>0.318243</td>\n",
       "      <td>0.452708</td>\n",
       "      <td>0.423286</td>\n",
       "      <td>0.396622</td>\n",
       "      <td>0.510897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>0.60</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.70000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.251538</td>\n",
       "      <td>0.246316</td>\n",
       "      <td>0.214805</td>\n",
       "      <td>0.234868</td>\n",
       "      <td>0.168056</td>\n",
       "      <td>0.318243</td>\n",
       "      <td>0.452708</td>\n",
       "      <td>0.423286</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.510897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.572638</td>\n",
       "      <td>0.603495</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.70000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.90000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Machine Learning\\n  Mathematics For Machine Learning\\n  \\\n",
       "158                0.00                                 0.0   \n",
       "109                0.75                                 0.8   \n",
       "131                0.60                                 0.7   \n",
       "55                 1.00                                 1.0   \n",
       "94                 1.00                                 1.0   \n",
       "29                 1.00                                 1.0   \n",
       "101                1.00                                 1.0   \n",
       "51                 1.00                                 1.0   \n",
       "100                1.00                                 1.0   \n",
       "\n",
       "     Visual Recognition\\n  Natural Language Processing\\n  \\\n",
       "158              0.000000                       0.572638   \n",
       "109              0.620098                       0.572638   \n",
       "131              0.700000                       0.500000   \n",
       "55               1.000000                       0.572638   \n",
       "94               0.600000                       0.800000   \n",
       "29               0.000000                       0.000000   \n",
       "101              1.000000                       0.500000   \n",
       "51               1.000000                       1.000000   \n",
       "100              1.000000                       0.000000   \n",
       "\n",
       "     Reinforcement Learning  Software Production Engineering\\n  \\\n",
       "158                0.603495                                0.5   \n",
       "109                0.603495                                1.0   \n",
       "131                0.600000                                0.7   \n",
       "55                 0.603495                                1.0   \n",
       "94                 1.000000                                1.0   \n",
       "29                 0.000000                                1.0   \n",
       "101                0.500000                                0.8   \n",
       "51                 1.000000                                0.5   \n",
       "100                0.000000                                1.0   \n",
       "\n",
       "     Computer Graphics\\n  Data Visualization\\n  Programming Languages\\n  \\\n",
       "158             1.000000               1.00000                 0.000000   \n",
       "109             0.456217               0.58841                 0.543363   \n",
       "131             0.700000               0.70000                 0.800000   \n",
       "55              0.000000               0.00000                 0.000000   \n",
       "94              0.200000               0.70000                 0.500000   \n",
       "29              0.000000               0.00000                 0.000000   \n",
       "101             0.300000               0.90000                 0.800000   \n",
       "51              0.700000               0.50000                 1.000000   \n",
       "100             0.000000               0.00000                 0.000000   \n",
       "\n",
       "     Cloud Computing\\n  Digital CMOS VLSI Design   System design with FPGA   \\\n",
       "158           0.572587                   0.251538                  0.246316   \n",
       "109           0.572587                   0.500000                  1.000000   \n",
       "131           0.750000                   0.251538                  0.246316   \n",
       "55            0.000000                   0.000000                  0.000000   \n",
       "94            0.600000                   0.000000                  0.000000   \n",
       "29            0.000000                   0.000000                  0.000000   \n",
       "101           0.900000                   0.000000                  0.000000   \n",
       "51            0.500000                   0.500000                  0.500000   \n",
       "100           0.000000                   0.000000                  0.000000   \n",
       "\n",
       "     ASIC design   VLSI Architecture Design\\n  \\\n",
       "158      0.214805                    0.234868   \n",
       "109      0.700000                    0.500000   \n",
       "131      0.214805                    0.234868   \n",
       "55       0.000000                    0.000000   \n",
       "94       0.000000                    0.000000   \n",
       "29       0.000000                    0.000000   \n",
       "101      0.000000                    0.000000   \n",
       "51       0.400000                    0.200000   \n",
       "100      0.000000                    0.000000   \n",
       "\n",
       "     High level synthesis and optimization of Digital Circuits   \\\n",
       "158                                           0.168056            \n",
       "109                                           0.168056            \n",
       "131                                           0.168056            \n",
       "55                                            0.000000            \n",
       "94                                            0.000000            \n",
       "29                                            0.000000            \n",
       "101                                           0.000000            \n",
       "51                                            0.300000            \n",
       "100                                           0.000000            \n",
       "\n",
       "     Digital Sociology   Privacy in the Digital Age  \\\n",
       "158            0.318243                    0.452708   \n",
       "109            0.318243                    0.452708   \n",
       "131            0.318243                    0.452708   \n",
       "55             1.000000                    0.000000   \n",
       "94             0.000000                    0.500000   \n",
       "29             0.000000                    0.000000   \n",
       "101            0.000000                    0.600000   \n",
       "51             0.400000                    0.500000   \n",
       "100            0.000000                    0.000000   \n",
       "\n",
       "     Technology Ethics and AI   Techno-economics of networks   \\\n",
       "158                   0.423286                       0.396622   \n",
       "109                   0.423286                       0.396622   \n",
       "131                   0.423286                       0.900000   \n",
       "55                    0.000000                       0.000000   \n",
       "94                    0.500000                       0.000000   \n",
       "29                    0.000000                       0.000000   \n",
       "101                   0.000000                       0.000000   \n",
       "51                    1.000000                       0.300000   \n",
       "100                   0.000000                       0.000000   \n",
       "\n",
       "     The Web and the Mind   \n",
       "158               0.200000  \n",
       "109               0.510897  \n",
       "131               0.510897  \n",
       "55                0.000000  \n",
       "94                0.600000  \n",
       "29                0.000000  \n",
       "101               0.300000  \n",
       "51                1.000000  \n",
       "100               0.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trying Prediction on a sample data point from test set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1. , 0. , 0. , 0.7, 0.7])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.to_numpy()[0][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_ratings = predict(X_test.to_numpy()[0][:5],kmeans_model,matrix_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 PREDICTED recommended electives are:\n",
      "Machine Learning\n",
      "\n",
      "Mathematics For Machine Learning\n",
      "\n",
      "Software Production Engineering\n",
      "\n",
      "Visual Recognition\n",
      "\n",
      "Reinforcement Learning\n",
      "\n"
     ]
    }
   ],
   "source": [
    "s=\"Top 5 PREDICTED recommended electives are:\\n\"\n",
    "for i in range(5):\n",
    "    s+=df.columns[sorted_ratings[i][1]]+\"\\n\"\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing sample with the actual top 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 ACTUAL recommended electives are:\n",
      "System design with FPGA \n",
      "Software Production Engineering\n",
      "\n",
      "Mathematics For Machine Learning\n",
      "\n",
      "Machine Learning\n",
      "\n",
      "ASIC design \n",
      "\n"
     ]
    }
   ],
   "source": [
    "sorted_ratings = []\n",
    "inferred = y_test.to_numpy()[1]\n",
    "for idx,element in enumerate(inferred):\n",
    "    sorted_ratings.append([element,idx+5])\n",
    "sorted_ratings.sort()\n",
    "sorted_ratings.reverse()\n",
    "\n",
    "s=\"Top 5 ACTUAL recommended electives are:\\n\"\n",
    "for i in range(5):\n",
    "    s+=df.columns[sorted_ratings[i][1]]+\"\\n\"\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 5)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions for whole test series application "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_for_dataset(X_test,kmeans_instance,matrix_clean):\n",
    "    ''' Runs out inference and return predicted ratings of subjects \n",
    "        @input: X_test,kmeans object, clean_matrix after SVD\n",
    "    '''\n",
    "    X_test = X_test.to_numpy()\n",
    "    y_pred = []\n",
    "    for i in range(X_test.shape[0]):\n",
    "        datapoint =X_test[i][:5]\n",
    "        y_pred.append(predict(datapoint,kmeans_instance,matrix_clean))\n",
    "    return np.array(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = predict_for_dataset(X_test,kmeans_model,matrix_clean) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 20, 2)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(y_pred,y_test,n=5):\n",
    "    ''' Finds out % common subject in top 5 rated subjects (predicted and actual) \n",
    "        @input: predicted values and test dataset values \n",
    "        @output: accuracy value \n",
    "    '''\n",
    "    y_test = y_test.to_numpy()\n",
    "    sorted_ratings_actual = []\n",
    "    for i in range(y_test.shape[0]):\n",
    "        inferred = y_test[i]\n",
    "        t = []\n",
    "        for idx,element in enumerate(inferred):\n",
    "            t.append([element,idx+5])\n",
    "        t.sort()\n",
    "        t.reverse()\n",
    "        sorted_ratings_actual.append(np.array(t))\n",
    "    sorted_ratings_actual = np.array(sorted_ratings_actual)\n",
    "    \n",
    "    total = 0 \n",
    "    correct = 0 \n",
    "\n",
    "    \n",
    "    for i in range(len(sorted_ratings_actual)):\n",
    "        total+=5\n",
    "        temp = sorted_ratings_actual[i][:n]\n",
    "        # if i ==1:\n",
    "        #     print(temp)\n",
    "        for j in range(n):\n",
    "            for k in range(len(temp)):\n",
    "                if(y_pred[i][j][1]==temp[k][1]):correct+=1\n",
    "\n",
    "    perecent_correct_for_datapoint = []\n",
    "\n",
    "    for i in range(len(sorted_ratings_actual)):\n",
    "        per_correct = 0 \n",
    "        temp = sorted_ratings_actual[i][:n]\n",
    "        # if i ==1:\n",
    "        #     print(temp)\n",
    "        for j in range(n):\n",
    "            for k in range(len(temp)):\n",
    "                if(y_pred[i][j][1]==temp[k][1]):per_correct+=1\n",
    "        perecent_correct_for_datapoint.append(per_correct/n)\n",
    "    print(\"Total Entries received: \", len(sorted_ratings_actual)*n,\" Total Correct: \",correct)\n",
    "    return 100*(correct/total),perecent_correct_for_datapoint,sorted_ratings_actual\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Entries received:  45  Total Correct:  23\n"
     ]
    }
   ],
   "source": [
    "overall_val,per_point_val,y_sorted_actual = accuracy(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51.11111111111111"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overall_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.2, 0.6, 0.0, 0.8, 0.8, 0.6, 0.6, 0.2, 0.8]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_point_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_subject(sorted_ratings,names,num_recs=5):\n",
    "    '''\n",
    "        @input: sorted_ratings list \n",
    "        @output: Names of subject corresponding\n",
    "    '''\n",
    "    s=\"\"\n",
    "    for i in range(num_recs):\n",
    "        s+=names[int(sorted_ratings[i][1])]+\"\\n\"\n",
    "    return s "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Machine Learning\n",
      "\n",
      "Mathematics For Machine Learning\n",
      "\n",
      "Software Production Engineering\n",
      "\n",
      "Visual Recognition\n",
      "\n",
      "Reinforcement Learning\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(display_subject(y_pred[1],df.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System design with FPGA \n",
      "Software Production Engineering\n",
      "\n",
      "Mathematics For Machine Learning\n",
      "\n",
      "Machine Learning\n",
      "\n",
      "ASIC design \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(display_subject(y_sorted_actual[1],df.columns))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy Vs K "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Entries received:  765  Total Correct:  399\n",
      "Total Entries received:  765  Total Correct:  378\n",
      "Total Entries received:  765  Total Correct:  367\n",
      "Total Entries received:  765  Total Correct:  356\n",
      "Total Entries received:  765  Total Correct:  351\n",
      "Total Entries received:  765  Total Correct:  348\n",
      "Total Entries received:  765  Total Correct:  348\n",
      "Total Entries received:  765  Total Correct:  361\n",
      "Total Entries received:  765  Total Correct:  354\n",
      "Total Entries received:  765  Total Correct:  379\n",
      "Total Entries received:  765  Total Correct:  361\n",
      "Total Entries received:  765  Total Correct:  371\n",
      "Total Entries received:  765  Total Correct:  336\n",
      "Total Entries received:  765  Total Correct:  321\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_list = []\n",
    "k = range(1,15)\n",
    "y_ticks = range(0,60)\n",
    "for i in range(1,15):\n",
    "    matrix,kmeans_object=cluster_to_matrix(X_train,y_train,i)\n",
    "    u,s,sigma,V_trans = ReducedSVD(matrix,to_remove=0)\n",
    "    matrix_clean = u@sigma@V_trans\n",
    "    matrix_clean = matrix_clean.astype('float')\n",
    "    y_pred = predict_for_dataset(X_train,kmeans_object,matrix_clean)\n",
    "    overall_val,per_point_val,y_sorted_actual = accuracy(y_pred,y_train)\n",
    "    accuracy_list.append(overall_val)\n",
    "plt.plot(k,accuracy_list)\n",
    "plt.xlabel('Values of K')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xticks(np.arange(min(k), max(k)+1, 1.0))\n",
    "plt.yticks(np.arange(min(y_ticks),max(y_ticks)+1,5.0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_df = pd.DataFrame(list(k),columns=[\"Values of K\"])\n",
    "acc_df = pd.DataFrame(accuracy_list,columns=[\"Accuracy\"])\n",
    "to_show = pd.concat([list_df,acc_df],axis=\"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Values of K</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>52.156863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>49.411765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>47.973856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>46.535948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>45.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>45.490196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>45.490196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>47.189542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>46.274510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>49.542484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>47.189542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>48.496732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>43.921569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>41.960784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Values of K   Accuracy\n",
       "0             1  52.156863\n",
       "1             2  49.411765\n",
       "2             3  47.973856\n",
       "3             4  46.535948\n",
       "4             5  45.882353\n",
       "5             6  45.490196\n",
       "6             7  45.490196\n",
       "7             8  47.189542\n",
       "8             9  46.274510\n",
       "9            10  49.542484\n",
       "10           11  47.189542\n",
       "11           12  48.496732\n",
       "12           13  43.921569\n",
       "13           14  41.960784"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_show"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reduced SVD Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Entries received:  765  Total Correct:  379\n",
      "Total Entries received:  765  Total Correct:  378\n",
      "Total Entries received:  765  Total Correct:  377\n",
      "Total Entries received:  765  Total Correct:  379\n",
      "Total Entries received:  765  Total Correct:  380\n",
      "Total Entries received:  765  Total Correct:  379\n",
      "Total Entries received:  765  Total Correct:  393\n",
      "Total Entries received:  765  Total Correct:  399\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_list = []\n",
    "k = range(0,8)\n",
    "y_ticks = range(0,60)\n",
    "for i in range(0,8):\n",
    "    matrix,kmeans_object=cluster_to_matrix(X_train,y_train,10)\n",
    "    u,s,sigma,V_trans = ReducedSVD(matrix,to_remove=i)\n",
    "    matrix_clean = u@sigma@V_trans\n",
    "    matrix_clean = matrix_clean.astype('float')\n",
    "    y_pred = predict_for_dataset(X_train,kmeans_object,matrix_clean)\n",
    "    overall_val,per_point_val,y_sorted_actual = accuracy(y_pred,y_train)\n",
    "    accuracy_list.append(overall_val)\n",
    "plt.plot(k,accuracy_list)\n",
    "plt.xlabel('Singular Values Removed for best k')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xticks(np.arange(min(k), max(k)+1, 1.0))\n",
    "plt.yticks(np.arange(min(y_ticks),max(y_ticks)+1,5.0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[49.54248366013072,\n",
       " 49.411764705882355,\n",
       " 49.28104575163399,\n",
       " 49.54248366013072,\n",
       " 49.673202614379086,\n",
       " 49.54248366013072,\n",
       " 51.37254901960784,\n",
       " 52.156862745098046]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Removed values</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>49.542484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>49.411765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>49.281046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>49.542484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>49.673203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>49.542484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>51.372549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>52.156863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Removed values   Accuracy\n",
       "0               0  49.542484\n",
       "1               1  49.411765\n",
       "2               2  49.281046\n",
       "3               3  49.542484\n",
       "4               4  49.673203\n",
       "5               5  49.542484\n",
       "6               6  51.372549\n",
       "7               7  52.156863"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_df = pd.DataFrame(list(k),columns=[\"Removed values\"])\n",
    "acc_df = pd.DataFrame(accuracy_list,columns=[\"Accuracy\"])\n",
    "to_show = pd.concat([list_df,acc_df],axis=\"columns\")\n",
    "to_show"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mean Absolute Error Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_unsorted(datapoint,kmeans,matrix_clean):\n",
    "    ''' \n",
    "        @input: Takes in a datapoint, kmeans object, and information matrix \n",
    "        @output: Returns: rating not-sorted i.e in order of subjects and not ratings \n",
    "    '''\n",
    "    y_pred = kmeans.predictPoint(datapoint)\n",
    "    inferred_full = matrix_clean[y_pred]\n",
    "    inferred = inferred_full[5:]\n",
    "    sorted_ratings = []\n",
    "    for idx,element in enumerate(inferred):\n",
    "        sorted_ratings.append(element)\n",
    "    # sorted_ratings.sort()\n",
    "    # sorted_ratings.reverse()\n",
    "    return sorted_ratings "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_for_dataset_unsorted(X_test,kmeans_instance,matrix_clean):\n",
    "    ''' Runs out inference and return predicted ratings of subjects \n",
    "        @input: X_test,kmeans object, clean_matrix after SVD\n",
    "    '''\n",
    "    X_test = X_test.to_numpy()\n",
    "    y_pred = []\n",
    "    for i in range(X_test.shape[0]):\n",
    "        datapoint =X_test[i][:5]\n",
    "        y_pred.append(predict_unsorted(datapoint,kmeans_instance,matrix_clean))\n",
    "    return np.array(y_pred)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "trial on K=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.42679298237560986\n"
     ]
    }
   ],
   "source": [
    "matrix,kmeans_object=cluster_to_matrix(X_train,y_train,10)\n",
    "u,s,sigma,V_trans = ReducedSVD(matrix,to_remove=i)\n",
    "matrix_clean = u@sigma@V_trans\n",
    "matrix_clean = matrix_clean.astype('float')\n",
    "y_pred = predict_for_dataset_unsorted(X_train,kmeans_object,matrix_clean)\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "mae = mean_absolute_error(y_train, y_pred)\n",
    "print(mae)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MAE VS K "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mae_list = []\n",
    "k = range(1,15)\n",
    "# y_ticks = range(0,60)\n",
    "for i in range(1,15):\n",
    "    matrix,kmeans_object=cluster_to_matrix(X_train,y_train,i)\n",
    "    u,s,sigma,V_trans = ReducedSVD(matrix,to_remove=0)\n",
    "    matrix_clean = u@sigma@V_trans\n",
    "    matrix_clean = matrix_clean.astype('float')\n",
    "    y_pred = predict_for_dataset_unsorted(X_train,kmeans_object,matrix_clean)\n",
    "    from sklearn.metrics import mean_absolute_error\n",
    "    mae = mean_absolute_error(y_train, y_pred)\n",
    "    mae_list.append(mae)\n",
    "plt.plot(k,mae_list)\n",
    "plt.xlabel('Values of K')\n",
    "plt.ylabel('Mean Absolute Error')\n",
    "plt.xticks(np.arange(min(k), max(k)+1, 1.0))\n",
    "# plt.yticks(np.arange(min(y_ticks),max(y_ticks)+1,5.0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Values of K</th>\n",
       "      <th>Mean Absolute Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.183020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.190758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.190090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.195289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.198413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.198957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.199071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.201933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.202508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.435482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>0.292483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>0.418309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0.214732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0.321667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Values of K  Mean Absolute Error\n",
       "0             1             0.183020\n",
       "1             2             0.190758\n",
       "2             3             0.190090\n",
       "3             4             0.195289\n",
       "4             5             0.198413\n",
       "5             6             0.198957\n",
       "6             7             0.199071\n",
       "7             8             0.201933\n",
       "8             9             0.202508\n",
       "9            10             0.435482\n",
       "10           11             0.292483\n",
       "11           12             0.418309\n",
       "12           13             0.214732\n",
       "13           14             0.321667"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_df = pd.DataFrame(list(k),columns=[\"Values of K\"])\n",
    "acc_df = pd.DataFrame(mae_list,columns=[\"Mean Absolute Error\"])\n",
    "to_show = pd.concat([list_df,acc_df],axis=\"columns\")\n",
    "to_show"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MAE vs Reduced SVD "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mae_list = []\n",
    "k = range(0,8)\n",
    "\n",
    "for i in range(0,8):\n",
    "    matrix,kmeans_object=cluster_to_matrix(X_train,y_train,10)\n",
    "    u,s,sigma,V_trans = ReducedSVD(matrix,to_remove=i)\n",
    "    matrix_clean = u@sigma@V_trans\n",
    "    matrix_clean = matrix_clean.astype('float')\n",
    "    y_pred = predict_for_dataset_unsorted(X_train,kmeans_object,matrix_clean)\n",
    "    from sklearn.metrics import mean_absolute_error\n",
    "    mae = mean_absolute_error(y_train, y_pred)\n",
    "    mae_list.append(mae)\n",
    "\n",
    "plt.plot(k,mae_list)\n",
    "plt.xlabel('Singular Values Removed for k=10')\n",
    "plt.ylabel('Mean Absolute Error')\n",
    "plt.xticks(np.arange(min(k), max(k)+1, 1.0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Singular Values Removed</th>\n",
       "      <th>Mean Absolute Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.435482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.434608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.432514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.431406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.429863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.428380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.427238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.426793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Singular Values Removed  Mean Absolute Error\n",
       "0                        0             0.435482\n",
       "1                        1             0.434608\n",
       "2                        2             0.432514\n",
       "3                        3             0.431406\n",
       "4                        4             0.429863\n",
       "5                        5             0.428380\n",
       "6                        6             0.427238\n",
       "7                        7             0.426793"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_df = pd.DataFrame(list(k),columns=[\"Singular Values Removed\"])\n",
    "acc_df = pd.DataFrame(mae_list,columns=[\"Mean Absolute Error\"])\n",
    "to_show = pd.concat([list_df,acc_df],axis=\"columns\")\n",
    "to_show"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cross Validation\n",
    "We take values of cluster from 4 to 14 and go on removing 0 to k-1 singular values and try to find the best accuracy (cause the recommendations can be explained better with what you recommend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Entries received:  765  Total Correct:  356\n",
      "Total Entries received:  765  Total Correct:  367\n",
      "Total Entries received:  765  Total Correct:  372\n",
      "Total Entries received:  765  Total Correct:  399\n",
      "Total Entries received:  765  Total Correct:  351\n",
      "Total Entries received:  765  Total Correct:  369\n",
      "Total Entries received:  765  Total Correct:  385\n",
      "Total Entries received:  765  Total Correct:  385\n",
      "Total Entries received:  765  Total Correct:  399\n",
      "Total Entries received:  765  Total Correct:  348\n",
      "Total Entries received:  765  Total Correct:  362\n",
      "Total Entries received:  765  Total Correct:  359\n",
      "Total Entries received:  765  Total Correct:  378\n",
      "Total Entries received:  765  Total Correct:  386\n",
      "Total Entries received:  765  Total Correct:  374\n",
      "Total Entries received:  765  Total Correct:  348\n",
      "Total Entries received:  765  Total Correct:  355\n",
      "Total Entries received:  765  Total Correct:  358\n",
      "Total Entries received:  765  Total Correct:  363\n",
      "Total Entries received:  765  Total Correct:  366\n",
      "Total Entries received:  765  Total Correct:  367\n",
      "Total Entries received:  765  Total Correct:  315\n",
      "Total Entries received:  765  Total Correct:  361\n",
      "Total Entries received:  765  Total Correct:  361\n",
      "Total Entries received:  765  Total Correct:  350\n",
      "Total Entries received:  765  Total Correct:  375\n",
      "Total Entries received:  765  Total Correct:  383\n",
      "Total Entries received:  765  Total Correct:  387\n",
      "Total Entries received:  765  Total Correct:  399\n",
      "Total Entries received:  765  Total Correct:  361\n",
      "Total Entries received:  765  Total Correct:  354\n",
      "Total Entries received:  765  Total Correct:  376\n",
      "Total Entries received:  765  Total Correct:  375\n",
      "Total Entries received:  765  Total Correct:  357\n",
      "Total Entries received:  765  Total Correct:  360\n",
      "Total Entries received:  765  Total Correct:  366\n",
      "Total Entries received:  765  Total Correct:  387\n",
      "Total Entries received:  765  Total Correct:  399\n",
      "Total Entries received:  765  Total Correct:  354\n",
      "Total Entries received:  765  Total Correct:  379\n",
      "Total Entries received:  765  Total Correct:  378\n",
      "Total Entries received:  765  Total Correct:  377\n",
      "Total Entries received:  765  Total Correct:  379\n",
      "Total Entries received:  765  Total Correct:  380\n",
      "Total Entries received:  765  Total Correct:  379\n",
      "Total Entries received:  765  Total Correct:  393\n",
      "Total Entries received:  765  Total Correct:  399\n",
      "Total Entries received:  765  Total Correct:  399\n",
      "Total Entries received:  765  Total Correct:  379\n",
      "Total Entries received:  765  Total Correct:  361\n",
      "Total Entries received:  765  Total Correct:  361\n",
      "Total Entries received:  765  Total Correct:  360\n",
      "Total Entries received:  765  Total Correct:  350\n",
      "Total Entries received:  765  Total Correct:  361\n",
      "Total Entries received:  765  Total Correct:  362\n",
      "Total Entries received:  765  Total Correct:  363\n",
      "Total Entries received:  765  Total Correct:  364\n",
      "Total Entries received:  765  Total Correct:  375\n",
      "Total Entries received:  765  Total Correct:  399\n",
      "Total Entries received:  765  Total Correct:  361\n",
      "Total Entries received:  765  Total Correct:  371\n",
      "Total Entries received:  765  Total Correct:  379\n",
      "Total Entries received:  765  Total Correct:  377\n",
      "Total Entries received:  765  Total Correct:  377\n",
      "Total Entries received:  765  Total Correct:  363\n",
      "Total Entries received:  765  Total Correct:  366\n",
      "Total Entries received:  765  Total Correct:  365\n",
      "Total Entries received:  765  Total Correct:  368\n",
      "Total Entries received:  765  Total Correct:  392\n",
      "Total Entries received:  765  Total Correct:  399\n",
      "Total Entries received:  765  Total Correct:  399\n",
      "Total Entries received:  765  Total Correct:  371\n",
      "Total Entries received:  765  Total Correct:  336\n",
      "Total Entries received:  765  Total Correct:  337\n",
      "Total Entries received:  765  Total Correct:  349\n",
      "Total Entries received:  765  Total Correct:  353\n",
      "Total Entries received:  765  Total Correct:  349\n",
      "Total Entries received:  765  Total Correct:  356\n",
      "Total Entries received:  765  Total Correct:  358\n",
      "Total Entries received:  765  Total Correct:  362\n",
      "Total Entries received:  765  Total Correct:  355\n",
      "Total Entries received:  765  Total Correct:  367\n",
      "Total Entries received:  765  Total Correct:  392\n",
      "Total Entries received:  765  Total Correct:  399\n",
      "Total Entries received:  765  Total Correct:  336\n",
      "Total Entries received:  765  Total Correct:  321\n",
      "Total Entries received:  765  Total Correct:  325\n",
      "Total Entries received:  765  Total Correct:  325\n",
      "Total Entries received:  765  Total Correct:  324\n",
      "Total Entries received:  765  Total Correct:  346\n",
      "Total Entries received:  765  Total Correct:  344\n",
      "Total Entries received:  765  Total Correct:  348\n",
      "Total Entries received:  765  Total Correct:  354\n",
      "Total Entries received:  765  Total Correct:  354\n",
      "Total Entries received:  765  Total Correct:  348\n",
      "Total Entries received:  765  Total Correct:  360\n",
      "Total Entries received:  765  Total Correct:  377\n",
      "Total Entries received:  765  Total Correct:  399\n",
      "Total Entries received:  765  Total Correct:  321\n"
     ]
    }
   ],
   "source": [
    "accuracy_list_gs = []\n",
    "def grid_search(X_train,y_train):\n",
    "    for i in range(4,15):\n",
    "        for j in range(0,i):\n",
    "            try:\n",
    "                matrix,kmeans_object=cluster_to_matrix(X_train,y_train,i)\n",
    "                u,s,sigma,V_trans = ReducedSVD(matrix,to_remove=j)\n",
    "                matrix_clean = u@sigma@V_trans\n",
    "                matrix_clean = matrix_clean.astype('float')\n",
    "                y_pred = predict_for_dataset(X_train,kmeans_object,matrix_clean)\n",
    "                overall_val,per_point_val,y_sorted_actual = accuracy(y_pred,y_train)\n",
    "                accuracy_list_gs.append([overall_val,('Number of cluster',i),('SV removed',j)])\n",
    "            except: \n",
    "                print(i,j,\"index_error\")\n",
    "    accuracy_list_gs.sort(reverse=True)\n",
    "grid_search(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[52.156862745098046, ('Number of cluster', 14), ('SV removed', 12)],\n",
       " [52.156862745098046, ('Number of cluster', 13), ('SV removed', 11)],\n",
       " [52.156862745098046, ('Number of cluster', 12), ('SV removed', 10)],\n",
       " [52.156862745098046, ('Number of cluster', 12), ('SV removed', 9)],\n",
       " [52.156862745098046, ('Number of cluster', 11), ('SV removed', 9)],\n",
       " [52.156862745098046, ('Number of cluster', 10), ('SV removed', 8)],\n",
       " [52.156862745098046, ('Number of cluster', 10), ('SV removed', 7)],\n",
       " [52.156862745098046, ('Number of cluster', 9), ('SV removed', 7)],\n",
       " [52.156862745098046, ('Number of cluster', 8), ('SV removed', 6)],\n",
       " [52.156862745098046, ('Number of cluster', 5), ('SV removed', 4)],\n",
       " [52.156862745098046, ('Number of cluster', 4), ('SV removed', 3)],\n",
       " [51.37254901960784, ('Number of cluster', 10), ('SV removed', 6)],\n",
       " [51.24183006535947, ('Number of cluster', 13), ('SV removed', 10)],\n",
       " [51.24183006535947, ('Number of cluster', 12), ('SV removed', 8)],\n",
       " [50.588235294117645, ('Number of cluster', 9), ('SV removed', 6)],\n",
       " [50.588235294117645, ('Number of cluster', 8), ('SV removed', 5)],\n",
       " [50.45751633986928, ('Number of cluster', 6), ('SV removed', 4)],\n",
       " [50.326797385620914, ('Number of cluster', 5), ('SV removed', 3)],\n",
       " [50.326797385620914, ('Number of cluster', 5), ('SV removed', 2)],\n",
       " [50.06535947712418, ('Number of cluster', 8), ('SV removed', 4)],\n",
       " [49.673202614379086, ('Number of cluster', 10), ('SV removed', 4)],\n",
       " [49.54248366013072, ('Number of cluster', 12), ('SV removed', 1)],\n",
       " [49.54248366013072, ('Number of cluster', 10), ('SV removed', 9)],\n",
       " [49.54248366013072, ('Number of cluster', 10), ('SV removed', 5)],\n",
       " [49.54248366013072, ('Number of cluster', 10), ('SV removed', 3)],\n",
       " [49.54248366013072, ('Number of cluster', 10), ('SV removed', 0)],\n",
       " [49.411764705882355, ('Number of cluster', 10), ('SV removed', 1)],\n",
       " [49.411764705882355, ('Number of cluster', 6), ('SV removed', 3)],\n",
       " [49.28104575163399, ('Number of cluster', 14), ('SV removed', 11)],\n",
       " [49.28104575163399, ('Number of cluster', 12), ('SV removed', 3)],\n",
       " [49.28104575163399, ('Number of cluster', 12), ('SV removed', 2)],\n",
       " [49.28104575163399, ('Number of cluster', 10), ('SV removed', 2)],\n",
       " [49.15032679738562, ('Number of cluster', 9), ('SV removed', 1)],\n",
       " [49.01960784313725, ('Number of cluster', 11), ('SV removed', 8)],\n",
       " [49.01960784313725, ('Number of cluster', 9), ('SV removed', 2)],\n",
       " [49.01960784313725, ('Number of cluster', 8), ('SV removed', 3)],\n",
       " [48.888888888888886, ('Number of cluster', 6), ('SV removed', 5)],\n",
       " [48.627450980392155, ('Number of cluster', 4), ('SV removed', 2)],\n",
       " [48.49673202614379, ('Number of cluster', 12), ('SV removed', 11)],\n",
       " [48.49673202614379, ('Number of cluster', 12), ('SV removed', 0)],\n",
       " [48.23529411764706, ('Number of cluster', 5), ('SV removed', 1)],\n",
       " [48.10457516339869, ('Number of cluster', 12), ('SV removed', 7)],\n",
       " [47.97385620915033, ('Number of cluster', 13), ('SV removed', 9)],\n",
       " [47.97385620915033, ('Number of cluster', 7), ('SV removed', 5)],\n",
       " [47.97385620915033, ('Number of cluster', 4), ('SV removed', 1)],\n",
       " [47.84313725490196, ('Number of cluster', 12), ('SV removed', 5)],\n",
       " [47.84313725490196, ('Number of cluster', 9), ('SV removed', 5)],\n",
       " [47.84313725490196, ('Number of cluster', 7), ('SV removed', 4)],\n",
       " [47.712418300653596, ('Number of cluster', 12), ('SV removed', 6)],\n",
       " [47.58169934640522, ('Number of cluster', 11), ('SV removed', 7)],\n",
       " [47.45098039215686, ('Number of cluster', 12), ('SV removed', 4)],\n",
       " [47.45098039215686, ('Number of cluster', 11), ('SV removed', 6)],\n",
       " [47.45098039215686, ('Number of cluster', 7), ('SV removed', 3)],\n",
       " [47.32026143790849, ('Number of cluster', 13), ('SV removed', 7)],\n",
       " [47.32026143790849, ('Number of cluster', 11), ('SV removed', 5)],\n",
       " [47.32026143790849, ('Number of cluster', 6), ('SV removed', 1)],\n",
       " [47.18954248366013, ('Number of cluster', 11), ('SV removed', 10)],\n",
       " [47.18954248366013, ('Number of cluster', 11), ('SV removed', 4)],\n",
       " [47.18954248366013, ('Number of cluster', 11), ('SV removed', 1)],\n",
       " [47.18954248366013, ('Number of cluster', 11), ('SV removed', 0)],\n",
       " [47.18954248366013, ('Number of cluster', 8), ('SV removed', 7)],\n",
       " [47.18954248366013, ('Number of cluster', 8), ('SV removed', 1)],\n",
       " [47.18954248366013, ('Number of cluster', 8), ('SV removed', 0)],\n",
       " [47.05882352941176, ('Number of cluster', 14), ('SV removed', 10)],\n",
       " [47.05882352941176, ('Number of cluster', 11), ('SV removed', 2)],\n",
       " [47.05882352941176, ('Number of cluster', 9), ('SV removed', 4)],\n",
       " [46.928104575163395, ('Number of cluster', 6), ('SV removed', 2)],\n",
       " [46.79738562091503, ('Number of cluster', 13), ('SV removed', 6)],\n",
       " [46.79738562091503, ('Number of cluster', 7), ('SV removed', 2)],\n",
       " [46.666666666666664, ('Number of cluster', 9), ('SV removed', 3)],\n",
       " [46.5359477124183, ('Number of cluster', 13), ('SV removed', 5)],\n",
       " [46.5359477124183, ('Number of cluster', 4), ('SV removed', 0)],\n",
       " [46.40522875816993, ('Number of cluster', 13), ('SV removed', 8)],\n",
       " [46.40522875816993, ('Number of cluster', 7), ('SV removed', 1)],\n",
       " [46.27450980392157, ('Number of cluster', 14), ('SV removed', 8)],\n",
       " [46.27450980392157, ('Number of cluster', 14), ('SV removed', 7)],\n",
       " [46.27450980392157, ('Number of cluster', 9), ('SV removed', 8)],\n",
       " [46.27450980392157, ('Number of cluster', 9), ('SV removed', 0)],\n",
       " [46.1437908496732, ('Number of cluster', 13), ('SV removed', 3)],\n",
       " [45.88235294117647, ('Number of cluster', 5), ('SV removed', 0)],\n",
       " [45.751633986928105, ('Number of cluster', 11), ('SV removed', 3)],\n",
       " [45.751633986928105, ('Number of cluster', 8), ('SV removed', 2)],\n",
       " [45.62091503267974, ('Number of cluster', 13), ('SV removed', 4)],\n",
       " [45.62091503267974, ('Number of cluster', 13), ('SV removed', 2)],\n",
       " [45.490196078431374, ('Number of cluster', 14), ('SV removed', 9)],\n",
       " [45.490196078431374, ('Number of cluster', 14), ('SV removed', 6)],\n",
       " [45.490196078431374, ('Number of cluster', 7), ('SV removed', 0)],\n",
       " [45.490196078431374, ('Number of cluster', 6), ('SV removed', 0)],\n",
       " [45.22875816993464, ('Number of cluster', 14), ('SV removed', 4)],\n",
       " [44.96732026143791, ('Number of cluster', 14), ('SV removed', 5)],\n",
       " [44.052287581699346, ('Number of cluster', 13), ('SV removed', 1)],\n",
       " [43.92156862745098, ('Number of cluster', 13), ('SV removed', 12)],\n",
       " [43.92156862745098, ('Number of cluster', 13), ('SV removed', 0)],\n",
       " [42.48366013071895, ('Number of cluster', 14), ('SV removed', 2)],\n",
       " [42.48366013071895, ('Number of cluster', 14), ('SV removed', 1)],\n",
       " [42.35294117647059, ('Number of cluster', 14), ('SV removed', 3)],\n",
       " [41.96078431372549, ('Number of cluster', 14), ('SV removed', 13)],\n",
       " [41.96078431372549, ('Number of cluster', 14), ('SV removed', 0)],\n",
       " [41.17647058823529, ('Number of cluster', 7), ('SV removed', 6)]]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_list_gs"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA for visualisation\n",
    "Keeping k = 10 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# colors = ['red','blue','green','purple','yellow','orange','pink','gray','brown','cyan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = {0: 'red', 1: 'blue', 2:'green' ,3:'purple',4:'yellow',5:'orange',6:'magenta',7:'gray',8:'brown',9:'cyan'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>index</th>\n",
       "      <th>Data Structures and Algorithms</th>\n",
       "      <th>Computer Architecture\\n</th>\n",
       "      <th>Discrete Mathematics\\n</th>\n",
       "      <th>Economics</th>\n",
       "      <th>Programming-2</th>\n",
       "      <th>Machine Learning\\n</th>\n",
       "      <th>Mathematics For Machine Learning\\n</th>\n",
       "      <th>Visual Recognition\\n</th>\n",
       "      <th>Natural Language Processing\\n</th>\n",
       "      <th>...</th>\n",
       "      <th>ASIC design</th>\n",
       "      <th>VLSI Architecture Design\\n</th>\n",
       "      <th>High level synthesis and optimization of Digital Circuits</th>\n",
       "      <th>Digital Sociology</th>\n",
       "      <th>Privacy in the Digital Age</th>\n",
       "      <th>Technology Ethics and AI</th>\n",
       "      <th>Techno-economics of networks</th>\n",
       "      <th>The Web and the Mind</th>\n",
       "      <th>index</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>144</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.620098</td>\n",
       "      <td>0.572638</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214805</td>\n",
       "      <td>0.234868</td>\n",
       "      <td>0.168056</td>\n",
       "      <td>0.318243</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.423286</td>\n",
       "      <td>0.396622</td>\n",
       "      <td>0.510897</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.318243</td>\n",
       "      <td>0.452708</td>\n",
       "      <td>0.423286</td>\n",
       "      <td>0.396622</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.572638</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214805</td>\n",
       "      <td>0.234868</td>\n",
       "      <td>0.168056</td>\n",
       "      <td>0.318243</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.423286</td>\n",
       "      <td>0.396622</td>\n",
       "      <td>0.510897</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>66</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.318243</td>\n",
       "      <td>0.452708</td>\n",
       "      <td>0.423286</td>\n",
       "      <td>0.396622</td>\n",
       "      <td>0.510897</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>71</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.572638</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214805</td>\n",
       "      <td>0.234868</td>\n",
       "      <td>0.168056</td>\n",
       "      <td>0.318243</td>\n",
       "      <td>0.452708</td>\n",
       "      <td>0.423286</td>\n",
       "      <td>0.396622</td>\n",
       "      <td>0.510897</td>\n",
       "      <td>148</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>106</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>14</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.572638</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214805</td>\n",
       "      <td>0.234868</td>\n",
       "      <td>0.168056</td>\n",
       "      <td>0.318243</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.423286</td>\n",
       "      <td>0.396622</td>\n",
       "      <td>0.510897</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>92</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.572638</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214805</td>\n",
       "      <td>0.234868</td>\n",
       "      <td>0.168056</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.452708</td>\n",
       "      <td>0.423286</td>\n",
       "      <td>0.396622</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>151</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>102</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>152</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>153 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  Data Structures and Algorithms  Computer Architecture\\n  \\\n",
       "0      144                             1.0                      0.7   \n",
       "1       19                             0.8                      0.8   \n",
       "2       84                             1.0                      0.5   \n",
       "3       15                             0.7                      0.9   \n",
       "4       66                             1.0                      1.0   \n",
       "..     ...                             ...                      ...   \n",
       "148     71                             0.5                      0.5   \n",
       "149    106                             0.5                      0.8   \n",
       "150     14                             0.8                      0.6   \n",
       "151     92                             1.0                      1.0   \n",
       "152    102                             0.8                      0.2   \n",
       "\n",
       "     Discrete Mathematics\\n  Economics  Programming-2  Machine Learning\\n  \\\n",
       "0                       1.0        0.5            1.0                 1.0   \n",
       "1                       0.8        0.5            0.8                 0.9   \n",
       "2                       0.0        0.0            1.0                 1.0   \n",
       "3                       0.2        0.6            1.0                 1.0   \n",
       "4                       1.0        0.0            1.0                 0.0   \n",
       "..                      ...        ...            ...                 ...   \n",
       "148                     0.0        1.0            0.6                 0.7   \n",
       "149                     0.4        0.9            0.7                 1.0   \n",
       "150                     0.9        0.9            0.7                 0.8   \n",
       "151                     1.0        0.7            1.0                 0.7   \n",
       "152                     0.2        0.9            1.0                 0.4   \n",
       "\n",
       "     Mathematics For Machine Learning\\n  Visual Recognition\\n  \\\n",
       "0                                   1.0              1.000000   \n",
       "1                                   0.9              0.620098   \n",
       "2                                   1.0              0.500000   \n",
       "3                                   0.8              0.700000   \n",
       "4                                   0.0              0.000000   \n",
       "..                                  ...                   ...   \n",
       "148                                 0.6              0.700000   \n",
       "149                                 0.8              0.900000   \n",
       "150                                 0.5              0.750000   \n",
       "151                                 0.9              0.500000   \n",
       "152                                 0.2              0.200000   \n",
       "\n",
       "     Natural Language Processing\\n  ...  ASIC design   \\\n",
       "0                         0.000000  ...      0.500000   \n",
       "1                         0.572638  ...      0.214805   \n",
       "2                         0.000000  ...      0.000000   \n",
       "3                         0.572638  ...      0.214805   \n",
       "4                         0.000000  ...      1.000000   \n",
       "..                             ...  ...           ...   \n",
       "148                       0.572638  ...      0.214805   \n",
       "149                       0.600000  ...      0.000000   \n",
       "150                       0.572638  ...      0.214805   \n",
       "151                       0.572638  ...      0.214805   \n",
       "152                       0.200000  ...      0.000000   \n",
       "\n",
       "     VLSI Architecture Design\\n  \\\n",
       "0                      0.800000   \n",
       "1                      0.234868   \n",
       "2                      0.000000   \n",
       "3                      0.234868   \n",
       "4                      1.000000   \n",
       "..                          ...   \n",
       "148                    0.234868   \n",
       "149                    0.000000   \n",
       "150                    0.234868   \n",
       "151                    0.234868   \n",
       "152                    0.000000   \n",
       "\n",
       "     High level synthesis and optimization of Digital Circuits   \\\n",
       "0                                             1.000000            \n",
       "1                                             0.168056            \n",
       "2                                             0.000000            \n",
       "3                                             0.168056            \n",
       "4                                             0.000000            \n",
       "..                                                 ...            \n",
       "148                                           0.168056            \n",
       "149                                           0.000000            \n",
       "150                                           0.168056            \n",
       "151                                           0.168056            \n",
       "152                                           0.000000            \n",
       "\n",
       "     Digital Sociology   Privacy in the Digital Age  \\\n",
       "0              0.500000                    0.000000   \n",
       "1              0.318243                    0.300000   \n",
       "2              0.318243                    0.452708   \n",
       "3              0.318243                    0.600000   \n",
       "4              0.318243                    0.452708   \n",
       "..                  ...                         ...   \n",
       "148            0.318243                    0.452708   \n",
       "149            0.000000                    0.500000   \n",
       "150            0.318243                    0.400000   \n",
       "151            0.500000                    0.452708   \n",
       "152            0.500000                    0.000000   \n",
       "\n",
       "     Technology Ethics and AI   Techno-economics of networks   \\\n",
       "0                     0.000000                       0.300000   \n",
       "1                     0.423286                       0.396622   \n",
       "2                     0.423286                       0.396622   \n",
       "3                     0.423286                       0.396622   \n",
       "4                     0.423286                       0.396622   \n",
       "..                         ...                            ...   \n",
       "148                   0.423286                       0.396622   \n",
       "149                   0.500000                       0.500000   \n",
       "150                   0.423286                       0.396622   \n",
       "151                   0.423286                       0.396622   \n",
       "152                   0.600000                       0.100000   \n",
       "\n",
       "     The Web and the Mind   index  label  \n",
       "0                 0.000000      0      0  \n",
       "1                 0.510897      1      0  \n",
       "2                 1.000000      2      7  \n",
       "3                 0.510897      3      9  \n",
       "4                 0.510897      4      5  \n",
       "..                     ...    ...    ...  \n",
       "148               0.510897    148      8  \n",
       "149               0.700000    149      1  \n",
       "150               0.510897    150      0  \n",
       "151               0.700000    151      0  \n",
       "152               0.600000    152      8  \n",
       "\n",
       "[153 rows x 28 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "k=Kmeans(K=10,iter=150,p=2,random_state=42)\n",
    "y_pred,_=k.predict(np.array(X_train),choice=0)\n",
    "k.centroids=np.array(k.centroids, dtype='int')\n",
    "X_train2 = pd.concat([X_train,y_train],axis=\"columns\")\n",
    "labels = pd.DataFrame(y_pred,columns=[\"label\"],dtype='int')\n",
    "X_train2.reset_index(inplace=True)\n",
    "labels.reset_index(inplace=True)\n",
    "X_train2=pd.concat([X_train2,labels],axis=1)\n",
    "X_train2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = X_train2['label']\n",
    "features = X_train2.drop(['label','index'], axis=\"columns\")\n",
    "pca = PCA(n_components=3)\n",
    "principal_components = pca.fit_transform(features)\n",
    "\n",
    "fig = plt.figure(figsize=(12,12))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "for label in labels.unique():\n",
    "    indices = labels == label\n",
    "    x = principal_components[indices, 0]\n",
    "    y = principal_components[indices, 1]\n",
    "    z = principal_components[indices, 2]\n",
    "    c = colors[label]\n",
    "\n",
    "    ax.scatter(x, y, z, c=c, label=label)\n",
    "\n",
    "sorted_labels = sorted(labels.unique(), key=lambda x: colors[x])\n",
    "\n",
    "handles = [plt.plot([], [], color=colors[label], marker='o', ls='', label=label)[0] for label in colors.keys()]\n",
    "ax.legend(handles=handles)\n",
    "\n",
    "ax.set_xlabel('PC1')\n",
    "ax.set_ylabel('PC2')\n",
    "ax.set_zlabel('PC3')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = X_train2['label']\n",
    "features = X_train2.drop(['label','index'], axis=\"columns\")\n",
    "pca = PCA(n_components=2)\n",
    "principal_components = pca.fit_transform(features)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "for label in labels.unique():\n",
    "    indices = labels == label\n",
    "    x = principal_components[indices, 0]\n",
    "    y = principal_components[indices, 1]\n",
    "    c = colors[label]\n",
    "    ax.scatter(x, y, c=c, label=label)\n",
    "\n",
    "sorted_labels = sorted(labels.unique(), key=lambda x: colors[x])\n",
    "\n",
    "handles = [plt.plot([], [], color=colors[label], marker='o', ls='', label=label)[0] for label in colors.keys()]\n",
    "ax.legend(handles=handles)\n",
    "ax.set_xlabel('PC1')\n",
    "ax.set_ylabel('PC2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/d3/cp13q0jn14z64vh0k61msh680000gn/T/ipykernel_15115/2765835443.py:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  cl = np.array(k.clusters)\n"
     ]
    },
    {
     "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>Cluster Number</th>\n",
       "      <th>Number of points in it</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cluster Number  Number of points in it\n",
       "0               0                      52\n",
       "1               1                      10\n",
       "2               2                       5\n",
       "3               3                       6\n",
       "4               4                       5\n",
       "5               5                      14\n",
       "6               6                       0\n",
       "7               7                      13\n",
       "8               8                      21\n",
       "9               9                      27"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl = np.array(k.clusters)\n",
    "count_arr = []\n",
    "for idx,i in enumerate(cl):\n",
    "    count_arr.append([idx,len(i)])\n",
    "count_arr\n",
    "df_count = pd.DataFrame(count_arr,columns=[\"Cluster Number\",\"Number of points in it\"])\n",
    "df_count "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top Subjects for Each Cluster "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "subjects = [\"Data Structures and Algorithms\",\"Computer Architecture\",\"Discrete Mathematics\",\"Economics\",\"Programming-2\",\n",
    "       'Machine Learning', 'Mathematics For Machine Learning',\n",
    "       'Visual Recognition', 'Natural Language Processing',\n",
    "       'Reinforcement Learning', 'Software Production Engineering',\n",
    "       'Computer Graphics', 'Data Visualization',\n",
    "       'Programming Languages', 'Cloud Computing',\n",
    "       'Digital CMOS VLSI Design ', 'System design with FPGA ', 'ASIC design ',\n",
    "       'VLSI Architecture Design',\n",
    "       'High level synthesis and optimization of Digital Circuits',\n",
    "       'Digital Sociology ', 'Privacy in the Digital Age',\n",
    "       'Technology Ethics and AI ', 'Techno-economics of networks ',\n",
    "       'The Web and the Mind']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "Machine Learning\n",
      "Mathematics For Machine Learning\n",
      "Software Production Engineering\n",
      "Visual Recognition\n",
      "Reinforcement Learning\n",
      "\n",
      "1\n",
      "Machine Learning\n",
      "Mathematics For Machine Learning\n",
      "Software Production Engineering\n",
      "Reinforcement Learning\n",
      "Natural Language Processing\n",
      "\n",
      "2\n",
      "Machine Learning\n",
      "Software Production Engineering\n",
      "Mathematics For Machine Learning\n",
      "Cloud Computing\n",
      "Programming Languages\n",
      "\n",
      "3\n",
      "Machine Learning\n",
      "Mathematics For Machine Learning\n",
      "Privacy in the Digital Age\n",
      "The Web and the Mind\n",
      "Data Visualization\n",
      "\n",
      "4\n",
      "Machine Learning\n",
      "Mathematics For Machine Learning\n",
      "Software Production Engineering\n",
      "Reinforcement Learning\n",
      "Natural Language Processing\n",
      "\n",
      "5\n",
      "Machine Learning\n",
      "Software Production Engineering\n",
      "Mathematics For Machine Learning\n",
      "Cloud Computing\n",
      "Visual Recognition\n",
      "\n",
      "6\n",
      "The Web and the Mind\n",
      "Techno-economics of networks \n",
      "Technology Ethics and AI \n",
      "Privacy in the Digital Age\n",
      "Digital Sociology \n",
      "\n",
      "7\n",
      "Machine Learning\n",
      "Mathematics For Machine Learning\n",
      "Reinforcement Learning\n",
      "Natural Language Processing\n",
      "Software Production Engineering\n",
      "\n",
      "8\n",
      "Machine Learning\n",
      "Mathematics For Machine Learning\n",
      "Software Production Engineering\n",
      "Visual Recognition\n",
      "Cloud Computing\n",
      "\n",
      "9\n",
      "Machine Learning\n",
      "Mathematics For Machine Learning\n",
      "Software Production Engineering\n",
      "Visual Recognition\n",
      "Reinforcement Learning\n",
      "\n"
     ]
    }
   ],
   "source": [
    "matrix,kmeans_model=cluster_to_matrix(X_train,y_train,10)\n",
    "u,s,sigma,V_trans = ReducedSVD(matrix,to_remove=5)\n",
    "matrix_clean = u@sigma@V_trans\n",
    "matrix_clean = matrix_clean.astype('float')\n",
    "vals = []\n",
    "for i in range(matrix_clean.shape[0]):\n",
    "    mat = matrix_clean[i][5:]\n",
    "    sorted_ratings = []\n",
    "    for idx,element in enumerate(mat):\n",
    "        sorted_ratings.append([element,idx+5])\n",
    "    sorted_ratings.sort()\n",
    "    sorted_ratings.reverse()\n",
    "    vals.append(sorted_ratings)\n",
    "for i in range(len(vals)):\n",
    "    print(i)\n",
    "    print(display_subject(vals[i],subjects,5) )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### UI for easy testing and prediction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "!pip install gradio\n",
    "import gradio as gr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = [\"Data Structures and Algorithms\",\"Computer Architecture\",\"Discrete Mathematics\",\"Economics\",\"Programming-2\",\n",
    "       'Machine Learning', 'Mathematics For Machine Learning',\n",
    "       'Visual Recognition', 'Natural Language Processing',\n",
    "       'Reinforcement Learning', 'Software Production Engineering',\n",
    "       'Computer Graphics', 'Data Visualization',\n",
    "       'Programming Languages', 'Cloud Computing',\n",
    "       'Digital CMOS VLSI Design ', 'System design with FPGA ', 'ASIC design ',\n",
    "       'VLSI Architecture Design',\n",
    "       'High level synthesis and optimization of Digital Circuits',\n",
    "       'Digital Sociology ', 'Privacy in the Digital Age',\n",
    "       'Technology Ethics and AI ', 'Techno-economics of networks ',\n",
    "       'The Web and the Mind ']\n",
    "def ui(name,num_recs,c1,c2,c3,c4,c5):\n",
    "    core = np.array([c1,c2,c3,c4,c5])\n",
    "    matrix,kmeans_model=cluster_to_matrix(X_train,y_train,10)\n",
    "    print(\"Allocated Cluster:\" ,kmeans_model.predictPoint(core))\n",
    "    u,s,sigma,V_trans = ReducedSVD(matrix,to_remove=5)\n",
    "    matrix_clean = u@sigma@V_trans\n",
    "    matrix_clean = matrix_clean.astype('float')\n",
    "    subjects=predict(core,kmeans_model,matrix_clean)\n",
    "    return display_subject(subjects,text,num_recs)\n",
    "    \n",
    "    \n",
    "    \n",
    "demo = gr.Interface(fn=ui, inputs=[gr.Text(label=\"Enter your Name (Optional)\"),\n",
    "                                      gr.Slider(int(1),int(20),step=1,label=\"Number of subjects you want us to recommend?\"),\n",
    "                                      gr.Slider(0,1,label=\"Data Structures and Algorithms\",info=\"Enter a value between 0 and 1\"),\n",
    "                                      gr.Slider(0,1,label=\"Computer Architecture\",info=\"Enter a value between 0 and 1\"),\n",
    "                                      gr.Slider(0,1,label=\"Discrete Mathematics\",info=\"Enter a value between 0 and 1\"),\n",
    "                                      gr.Slider(0,1,label=\"Economics\",info=\"Enter a value between 0 and 1\"),\n",
    "                                      gr.Slider(0,1,label=\"Programming-2\",info=\"Enter a value between 0 and 1\")], \n",
    "                                    outputs=gr.Text(label=\"Here are your recommendations:(in decreasing order/ preference) \"))\n",
    "\n",
    "demo.launch(share=True) "
   ]
  }
 ],
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