R5_Internal_Lab_PCA_PBRS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0zAbYvv-WCve"
   },
   "source": [
    "# Principal Component Analysis with Cancer Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1waXlt6iWCvh"
   },
   "outputs": [],
   "source": [
    "#Import all the necessary modules\n",
    "#Import all the necessary modules\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6FZ90t1vWCvq"
   },
   "source": [
    "# Q1. Load the Data file ( Breast Cancer CSV) into Python DataFrame and view top 10 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./Data/breast-cancer-wisconsin-data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>ID</th>\n",
       "      <th>ClumpThickness</th>\n",
       "      <th>Cell Size</th>\n",
       "      <th>Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000025</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002945</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1015425</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1016277</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1017023</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  ClumpThickness  Cell Size  Cell Shape  Marginal Adhesion  \\\n",
       "0  1000025               5          1           1                  1   \n",
       "1  1002945               5          4           4                  5   \n",
       "2  1015425               3          1           1                  1   \n",
       "3  1016277               6          8           8                  1   \n",
       "4  1017023               4          1           1                  3   \n",
       "\n",
       "   Single Epithelial Cell Size Bare Nuclei  Normal Nucleoli  Bland Chromatin  \\\n",
       "0                            2           1                3                1   \n",
       "1                            7          10                3                2   \n",
       "2                            2           2                3                1   \n",
       "3                            3           4                3                7   \n",
       "4                            2           1                3                1   \n",
       "\n",
       "   Mitoses  Class  \n",
       "0        1      2  \n",
       "1        1      2  \n",
       "2        1      2  \n",
       "3        1      2  \n",
       "4        1      2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xusO2ruNWCv4"
   },
   "outputs": [],
   "source": [
    "  # Id columns is to identify rows hence can be skipped in analysis\n",
    "  # All columns have numerical values\n",
    "  # Class would be the target variable. Should be removed when PCA is done"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KYTFwE0iWCv_"
   },
   "source": [
    "# Q2 Print the datatypes of each column and the shape of the dataset. Perform descriptive analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "abAqUIqjSM4X"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 699 entries, 0 to 698\n",
      "Data columns (total 11 columns):\n",
      "ID                             699 non-null int64\n",
      "ClumpThickness                 699 non-null int64\n",
      "Cell Size                      699 non-null int64\n",
      "Cell Shape                     699 non-null int64\n",
      "Marginal Adhesion              699 non-null int64\n",
      "Single Epithelial Cell Size    699 non-null int64\n",
      "Bare Nuclei                    699 non-null object\n",
      "Normal Nucleoli                699 non-null int64\n",
      "Bland Chromatin                699 non-null int64\n",
      "Mitoses                        699 non-null int64\n",
      "Class                          699 non-null int64\n",
      "dtypes: int64(10), object(1)\n",
      "memory usage: 60.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(699, 11)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ecghJZJIWCwW"
   },
   "source": [
    "# Q3 Check for missing value check, incorrect data, duplicate data and perform imputation with mean, median, mode as necessary.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "49KjoukMSM4b"
   },
   "outputs": [],
   "source": [
    "# We could see \"?\" values in column, this should be removed from data set\n",
    "\n",
    "# Check for missing value in any other column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(ID                             False\n",
       " ClumpThickness                 False\n",
       " Cell Size                      False\n",
       " Cell Shape                     False\n",
       " Marginal Adhesion              False\n",
       " Single Epithelial Cell Size    False\n",
       " Bare Nuclei                    False\n",
       " Normal Nucleoli                False\n",
       " Bland Chromatin                False\n",
       " Mitoses                        False\n",
       " Class                          False\n",
       " dtype: bool, ID                             False\n",
       " ClumpThickness                 False\n",
       " Cell Size                      False\n",
       " Cell Shape                     False\n",
       " Marginal Adhesion              False\n",
       " Single Epithelial Cell Size    False\n",
       " Bare Nuclei                    False\n",
       " Normal Nucleoli                False\n",
       " Bland Chromatin                False\n",
       " Mitoses                        False\n",
       " Class                          False\n",
       " dtype: bool)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().any(), data.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>ID</th>\n",
       "      <th>ClumpThickness</th>\n",
       "      <th>Cell Size</th>\n",
       "      <th>Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.990000e+02</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.071704e+06</td>\n",
       "      <td>4.417740</td>\n",
       "      <td>3.134478</td>\n",
       "      <td>3.207439</td>\n",
       "      <td>2.806867</td>\n",
       "      <td>3.216023</td>\n",
       "      <td>3.437768</td>\n",
       "      <td>2.866953</td>\n",
       "      <td>1.589413</td>\n",
       "      <td>2.689557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.170957e+05</td>\n",
       "      <td>2.815741</td>\n",
       "      <td>3.051459</td>\n",
       "      <td>2.971913</td>\n",
       "      <td>2.855379</td>\n",
       "      <td>2.214300</td>\n",
       "      <td>2.438364</td>\n",
       "      <td>3.053634</td>\n",
       "      <td>1.715078</td>\n",
       "      <td>0.951273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.163400e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.706885e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.171710e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.238298e+06</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.345435e+07</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID  ClumpThickness   Cell Size  Cell Shape  \\\n",
       "count  6.990000e+02      699.000000  699.000000  699.000000   \n",
       "mean   1.071704e+06        4.417740    3.134478    3.207439   \n",
       "std    6.170957e+05        2.815741    3.051459    2.971913   \n",
       "min    6.163400e+04        1.000000    1.000000    1.000000   \n",
       "25%    8.706885e+05        2.000000    1.000000    1.000000   \n",
       "50%    1.171710e+06        4.000000    1.000000    1.000000   \n",
       "75%    1.238298e+06        6.000000    5.000000    5.000000   \n",
       "max    1.345435e+07       10.000000   10.000000   10.000000   \n",
       "\n",
       "       Marginal Adhesion  Single Epithelial Cell Size  Normal Nucleoli  \\\n",
       "count         699.000000                   699.000000       699.000000   \n",
       "mean            2.806867                     3.216023         3.437768   \n",
       "std             2.855379                     2.214300         2.438364   \n",
       "min             1.000000                     1.000000         1.000000   \n",
       "25%             1.000000                     2.000000         2.000000   \n",
       "50%             1.000000                     2.000000         3.000000   \n",
       "75%             4.000000                     4.000000         5.000000   \n",
       "max            10.000000                    10.000000        10.000000   \n",
       "\n",
       "       Bland Chromatin     Mitoses       Class  \n",
       "count       699.000000  699.000000  699.000000  \n",
       "mean          2.866953    1.589413    2.689557  \n",
       "std           3.053634    1.715078    0.951273  \n",
       "min           1.000000    1.000000    2.000000  \n",
       "25%           1.000000    1.000000    2.000000  \n",
       "50%           1.000000    1.000000    2.000000  \n",
       "75%           4.000000    1.000000    4.000000  \n",
       "max          10.000000   10.000000    4.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 698 entries for all columns; So there are no missing (Null or NA) values.\n",
    "\n",
    "10 out of 11 columns are integer types and all 10 can be computed.\n",
    "\n",
    "Bare Nuclei is of object type. Will check if that contains ? or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "02fqzvZuSM4f"
   },
   "outputs": [],
   "source": [
    "# No missing values found. So let us try to remove ? from bare nuclei column\n",
    "\n",
    "# Get count of rows having ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16, 11)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data['Bare Nuclei'] == '?'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 16 rows with Bare Nuclei = ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jAmWPaXpWCw3"
   },
   "outputs": [],
   "source": [
    "# 16 values are corrupted. We can either delete them as it forms roughly 2% of data.\n",
    "# Here we would like to impute it with suitable values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we will first convert Bare Nuclei column to integer, get the mean of all values and assign this mean where ? is occuring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data['Bare Nuclei'] != '?', 'Bare Nuclei'] = pd.to_numeric(data.loc[data['Bare Nuclei'] != '?', 'Bare Nuclei'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "bareNuclei = data.loc[data['Bare Nuclei'] != '?', 'Bare Nuclei']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c8e1602ac8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(bareNuclei)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bareNuclei.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the countplot and unique values, value of 1 is repeated for 402 times.\n",
    "\n",
    "So, we will assign mode value of 1 to the rows where ? is occuring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data.replace(to_replace='?', value=bareNuclei.mode(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data['Bare Nuclei'] == '?', 'Bare Nuclei'] = pd.to_numeric(bareNuclei.mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>ID</th>\n",
       "      <th>ClumpThickness</th>\n",
       "      <th>Cell Size</th>\n",
       "      <th>Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.990000e+02</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.071704e+06</td>\n",
       "      <td>4.417740</td>\n",
       "      <td>3.134478</td>\n",
       "      <td>3.207439</td>\n",
       "      <td>2.806867</td>\n",
       "      <td>3.216023</td>\n",
       "      <td>3.486409</td>\n",
       "      <td>3.437768</td>\n",
       "      <td>2.866953</td>\n",
       "      <td>1.589413</td>\n",
       "      <td>2.689557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.170957e+05</td>\n",
       "      <td>2.815741</td>\n",
       "      <td>3.051459</td>\n",
       "      <td>2.971913</td>\n",
       "      <td>2.855379</td>\n",
       "      <td>2.214300</td>\n",
       "      <td>3.621929</td>\n",
       "      <td>2.438364</td>\n",
       "      <td>3.053634</td>\n",
       "      <td>1.715078</td>\n",
       "      <td>0.951273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.163400e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.706885e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.171710e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.238298e+06</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.345435e+07</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID  ClumpThickness   Cell Size  Cell Shape  \\\n",
       "count  6.990000e+02      699.000000  699.000000  699.000000   \n",
       "mean   1.071704e+06        4.417740    3.134478    3.207439   \n",
       "std    6.170957e+05        2.815741    3.051459    2.971913   \n",
       "min    6.163400e+04        1.000000    1.000000    1.000000   \n",
       "25%    8.706885e+05        2.000000    1.000000    1.000000   \n",
       "50%    1.171710e+06        4.000000    1.000000    1.000000   \n",
       "75%    1.238298e+06        6.000000    5.000000    5.000000   \n",
       "max    1.345435e+07       10.000000   10.000000   10.000000   \n",
       "\n",
       "       Marginal Adhesion  Single Epithelial Cell Size  Bare Nuclei  \\\n",
       "count         699.000000                   699.000000   699.000000   \n",
       "mean            2.806867                     3.216023     3.486409   \n",
       "std             2.855379                     2.214300     3.621929   \n",
       "min             1.000000                     1.000000     1.000000   \n",
       "25%             1.000000                     2.000000     1.000000   \n",
       "50%             1.000000                     2.000000     1.000000   \n",
       "75%             4.000000                     4.000000     5.000000   \n",
       "max            10.000000                    10.000000    10.000000   \n",
       "\n",
       "       Normal Nucleoli  Bland Chromatin     Mitoses       Class  \n",
       "count       699.000000       699.000000  699.000000  699.000000  \n",
       "mean          3.437768         2.866953    1.589413    2.689557  \n",
       "std           2.438364         3.053634    1.715078    0.951273  \n",
       "min           1.000000         1.000000    1.000000    2.000000  \n",
       "25%           2.000000         1.000000    1.000000    2.000000  \n",
       "50%           3.000000         1.000000    1.000000    2.000000  \n",
       "75%           5.000000         4.000000    1.000000    4.000000  \n",
       "max          10.000000        10.000000   10.000000    4.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if there are any duplicate rows (ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54, 11)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicateRows = data[data.duplicated(['ID'])]\n",
    "duplicateRows.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping these duplicate rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop_duplicates(keep='first', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>ID</th>\n",
       "      <th>ClumpThickness</th>\n",
       "      <th>Cell Size</th>\n",
       "      <th>Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.900000e+02</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.073826e+06</td>\n",
       "      <td>4.428986</td>\n",
       "      <td>3.133333</td>\n",
       "      <td>3.204348</td>\n",
       "      <td>2.827536</td>\n",
       "      <td>3.213043</td>\n",
       "      <td>3.482609</td>\n",
       "      <td>3.436232</td>\n",
       "      <td>2.885507</td>\n",
       "      <td>1.594203</td>\n",
       "      <td>2.689855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.196092e+05</td>\n",
       "      <td>2.817378</td>\n",
       "      <td>3.042451</td>\n",
       "      <td>2.960844</td>\n",
       "      <td>2.867787</td>\n",
       "      <td>2.200964</td>\n",
       "      <td>3.617064</td>\n",
       "      <td>2.444060</td>\n",
       "      <td>3.067682</td>\n",
       "      <td>1.724230</td>\n",
       "      <td>0.951379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.163400e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.744845e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.171752e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.238450e+06</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.345435e+07</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID  ClumpThickness   Cell Size  Cell Shape  \\\n",
       "count  6.900000e+02      690.000000  690.000000  690.000000   \n",
       "mean   1.073826e+06        4.428986    3.133333    3.204348   \n",
       "std    6.196092e+05        2.817378    3.042451    2.960844   \n",
       "min    6.163400e+04        1.000000    1.000000    1.000000   \n",
       "25%    8.744845e+05        2.000000    1.000000    1.000000   \n",
       "50%    1.171752e+06        4.000000    1.000000    1.000000   \n",
       "75%    1.238450e+06        6.000000    5.000000    5.000000   \n",
       "max    1.345435e+07       10.000000   10.000000   10.000000   \n",
       "\n",
       "       Marginal Adhesion  Single Epithelial Cell Size  Bare Nuclei  \\\n",
       "count         690.000000                   690.000000   690.000000   \n",
       "mean            2.827536                     3.213043     3.482609   \n",
       "std             2.867787                     2.200964     3.617064   \n",
       "min             1.000000                     1.000000     1.000000   \n",
       "25%             1.000000                     2.000000     1.000000   \n",
       "50%             1.000000                     2.000000     1.000000   \n",
       "75%             4.000000                     4.000000     5.000000   \n",
       "max            10.000000                    10.000000    10.000000   \n",
       "\n",
       "       Normal Nucleoli  Bland Chromatin     Mitoses       Class  \n",
       "count       690.000000       690.000000  690.000000  690.000000  \n",
       "mean          3.436232         2.885507    1.594203    2.689855  \n",
       "std           2.444060         3.067682    1.724230    0.951379  \n",
       "min           1.000000         1.000000    1.000000    2.000000  \n",
       "25%           2.000000         1.000000    1.000000    2.000000  \n",
       "50%           3.000000         1.000000    1.000000    2.000000  \n",
       "75%           5.000000         4.000000    1.000000    4.000000  \n",
       "max          10.000000        10.000000   10.000000    4.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YAu8NvGaWCxr"
   },
   "source": [
    "# Q4. Perform bi variate analysis including correlation, pairplots and state the inferences. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing ID for further analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns='ID', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nu6J2sWuSM4m"
   },
   "outputs": [],
   "source": [
    "# Check for correlation of variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>ClumpThickness</th>\n",
       "      <th>Cell Size</th>\n",
       "      <th>Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ClumpThickness</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.643173</td>\n",
       "      <td>0.653589</td>\n",
       "      <td>0.487715</td>\n",
       "      <td>0.517253</td>\n",
       "      <td>0.590648</td>\n",
       "      <td>0.561063</td>\n",
       "      <td>0.535507</td>\n",
       "      <td>0.350196</td>\n",
       "      <td>0.716812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell Size</th>\n",
       "      <td>0.643173</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905349</td>\n",
       "      <td>0.712933</td>\n",
       "      <td>0.746982</td>\n",
       "      <td>0.684571</td>\n",
       "      <td>0.759632</td>\n",
       "      <td>0.727073</td>\n",
       "      <td>0.459918</td>\n",
       "      <td>0.817586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell Shape</th>\n",
       "      <td>0.653589</td>\n",
       "      <td>0.905349</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.690782</td>\n",
       "      <td>0.714243</td>\n",
       "      <td>0.704165</td>\n",
       "      <td>0.738576</td>\n",
       "      <td>0.724519</td>\n",
       "      <td>0.440435</td>\n",
       "      <td>0.817551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <td>0.487715</td>\n",
       "      <td>0.712933</td>\n",
       "      <td>0.690782</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.608281</td>\n",
       "      <td>0.672512</td>\n",
       "      <td>0.669860</td>\n",
       "      <td>0.602228</td>\n",
       "      <td>0.417008</td>\n",
       "      <td>0.701175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <td>0.517253</td>\n",
       "      <td>0.746982</td>\n",
       "      <td>0.714243</td>\n",
       "      <td>0.608281</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.580487</td>\n",
       "      <td>0.620526</td>\n",
       "      <td>0.633880</td>\n",
       "      <td>0.482517</td>\n",
       "      <td>0.681063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <td>0.590648</td>\n",
       "      <td>0.684571</td>\n",
       "      <td>0.704165</td>\n",
       "      <td>0.672512</td>\n",
       "      <td>0.580487</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.672754</td>\n",
       "      <td>0.578161</td>\n",
       "      <td>0.342823</td>\n",
       "      <td>0.816655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <td>0.561063</td>\n",
       "      <td>0.759632</td>\n",
       "      <td>0.738576</td>\n",
       "      <td>0.669860</td>\n",
       "      <td>0.620526</td>\n",
       "      <td>0.672754</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.669099</td>\n",
       "      <td>0.343769</td>\n",
       "      <td>0.756736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <td>0.535507</td>\n",
       "      <td>0.727073</td>\n",
       "      <td>0.724519</td>\n",
       "      <td>0.602228</td>\n",
       "      <td>0.633880</td>\n",
       "      <td>0.578161</td>\n",
       "      <td>0.669099</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.427490</td>\n",
       "      <td>0.715363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mitoses</th>\n",
       "      <td>0.350196</td>\n",
       "      <td>0.459918</td>\n",
       "      <td>0.440435</td>\n",
       "      <td>0.417008</td>\n",
       "      <td>0.482517</td>\n",
       "      <td>0.342823</td>\n",
       "      <td>0.343769</td>\n",
       "      <td>0.427490</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.423947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <td>0.716812</td>\n",
       "      <td>0.817586</td>\n",
       "      <td>0.817551</td>\n",
       "      <td>0.701175</td>\n",
       "      <td>0.681063</td>\n",
       "      <td>0.816655</td>\n",
       "      <td>0.756736</td>\n",
       "      <td>0.715363</td>\n",
       "      <td>0.423947</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             ClumpThickness  Cell Size  Cell Shape  \\\n",
       "ClumpThickness                     1.000000   0.643173    0.653589   \n",
       "Cell Size                          0.643173   1.000000    0.905349   \n",
       "Cell Shape                         0.653589   0.905349    1.000000   \n",
       "Marginal Adhesion                  0.487715   0.712933    0.690782   \n",
       "Single Epithelial Cell Size        0.517253   0.746982    0.714243   \n",
       "Bare Nuclei                        0.590648   0.684571    0.704165   \n",
       "Normal Nucleoli                    0.561063   0.759632    0.738576   \n",
       "Bland Chromatin                    0.535507   0.727073    0.724519   \n",
       "Mitoses                            0.350196   0.459918    0.440435   \n",
       "Class                              0.716812   0.817586    0.817551   \n",
       "\n",
       "                             Marginal Adhesion  Single Epithelial Cell Size  \\\n",
       "ClumpThickness                        0.487715                     0.517253   \n",
       "Cell Size                             0.712933                     0.746982   \n",
       "Cell Shape                            0.690782                     0.714243   \n",
       "Marginal Adhesion                     1.000000                     0.608281   \n",
       "Single Epithelial Cell Size           0.608281                     1.000000   \n",
       "Bare Nuclei                           0.672512                     0.580487   \n",
       "Normal Nucleoli                       0.669860                     0.620526   \n",
       "Bland Chromatin                       0.602228                     0.633880   \n",
       "Mitoses                               0.417008                     0.482517   \n",
       "Class                                 0.701175                     0.681063   \n",
       "\n",
       "                             Bare Nuclei  Normal Nucleoli  Bland Chromatin  \\\n",
       "ClumpThickness                  0.590648         0.561063         0.535507   \n",
       "Cell Size                       0.684571         0.759632         0.727073   \n",
       "Cell Shape                      0.704165         0.738576         0.724519   \n",
       "Marginal Adhesion               0.672512         0.669860         0.602228   \n",
       "Single Epithelial Cell Size     0.580487         0.620526         0.633880   \n",
       "Bare Nuclei                     1.000000         0.672754         0.578161   \n",
       "Normal Nucleoli                 0.672754         1.000000         0.669099   \n",
       "Bland Chromatin                 0.578161         0.669099         1.000000   \n",
       "Mitoses                         0.342823         0.343769         0.427490   \n",
       "Class                           0.816655         0.756736         0.715363   \n",
       "\n",
       "                              Mitoses     Class  \n",
       "ClumpThickness               0.350196  0.716812  \n",
       "Cell Size                    0.459918  0.817586  \n",
       "Cell Shape                   0.440435  0.817551  \n",
       "Marginal Adhesion            0.417008  0.701175  \n",
       "Single Epithelial Cell Size  0.482517  0.681063  \n",
       "Bare Nuclei                  0.342823  0.816655  \n",
       "Normal Nucleoli              0.343769  0.756736  \n",
       "Bland Chromatin              0.427490  0.715363  \n",
       "Mitoses                      1.000000  0.423947  \n",
       "Class                        0.423947  1.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ysZh50iDWCxz"
   },
   "outputs": [],
   "source": [
    "# Cell size shows high significance with cell shape,marginal adhesion, single epithelial cell size,bare nuclei, normal nucleoli \n",
    "# and bland chromatin\n",
    "# Target variable shows high correlation with most of these variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5Wt84dgVSM4s"
   },
   "outputs": [],
   "source": [
    "#Let us check for pair plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Install\\anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:487: RuntimeWarning: invalid value encountered in true_divide\n",
      "  binned = fast_linbin(X, a, b, gridsize) / (delta * nobs)\n",
      "C:\\Install\\anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:34: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  FAC1 = 2*(np.pi*bw/RANGE)**2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2c8dc3316d8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1842.38x1800 with 110 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data, hue='Class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inferences from the pairplot:\n",
    "- Almost all variables have good separation across Class and so are good candidates for the clustering\n",
    "- Distributions of most variables are right-skewed, which means, most values are concentrated on lower side\n",
    "- Except Mitoses, all variabels are having correlation > 0.5 with other variables\n",
    "- Mitoses is having correlation < 0.5 with other variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OTh11akvWCx8"
   },
   "outputs": [],
   "source": [
    "# Relationship between variables shows come correlation. \n",
    "# Distribution of variables shows most of the values are concentrated on lower side, though range remains same for all that is \n",
    "# Between 1 to 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9hxe7D9BWCx-"
   },
   "source": [
    "# Q5 Remove any unwanted columns or outliers, standardize variables in pre-processing step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TqhCGZe4SM43",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c8e5641e80>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.boxplot(figsize=(15, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on above boxplot, most of the outliers are on higher side on the range of 1 to 10.\n",
    "\n",
    "Choosing these columns to process the outliers:\n",
    "Marginal Adhesion\n",
    "Single Epithelial Cell Size\n",
    "Normal Nucleoli\n",
    "Bland Chromatin\n",
    "Mitoses\n",
    "\n",
    "The outliers are assigned the values to the nearest quartile range (instead of mean or mode of all columns)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data['Marginal Adhesion'] > 8, 'Marginal Adhesion'] = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data['Single Epithelial Cell Size'] > 7, 'Single Epithelial Cell Size'] = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data['Normal Nucleoli'] > 9, 'Normal Nucleoli'] = 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data['Bland Chromatin'] > 8, 'Bland Chromatin'] = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data['Mitoses'] > 1, 'Mitoses'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>ClumpThickness</th>\n",
       "      <th>Cell Size</th>\n",
       "      <th>Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.0</td>\n",
       "      <td>690.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.428986</td>\n",
       "      <td>3.133333</td>\n",
       "      <td>3.204348</td>\n",
       "      <td>2.660870</td>\n",
       "      <td>3.047826</td>\n",
       "      <td>3.482609</td>\n",
       "      <td>3.407246</td>\n",
       "      <td>2.685507</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.689855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.817378</td>\n",
       "      <td>3.042451</td>\n",
       "      <td>2.960844</td>\n",
       "      <td>2.478542</td>\n",
       "      <td>1.769530</td>\n",
       "      <td>3.617064</td>\n",
       "      <td>2.370771</td>\n",
       "      <td>2.635240</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.951379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ClumpThickness   Cell Size  Cell Shape  Marginal Adhesion  \\\n",
       "count      690.000000  690.000000  690.000000         690.000000   \n",
       "mean         4.428986    3.133333    3.204348           2.660870   \n",
       "std          2.817378    3.042451    2.960844           2.478542   \n",
       "min          1.000000    1.000000    1.000000           1.000000   \n",
       "25%          2.000000    1.000000    1.000000           1.000000   \n",
       "50%          4.000000    1.000000    1.000000           1.000000   \n",
       "75%          6.000000    5.000000    5.000000           4.000000   \n",
       "max         10.000000   10.000000   10.000000           8.000000   \n",
       "\n",
       "       Single Epithelial Cell Size  Bare Nuclei  Normal Nucleoli  \\\n",
       "count                   690.000000   690.000000       690.000000   \n",
       "mean                      3.047826     3.482609         3.407246   \n",
       "std                       1.769530     3.617064         2.370771   \n",
       "min                       1.000000     1.000000         1.000000   \n",
       "25%                       2.000000     1.000000         2.000000   \n",
       "50%                       2.000000     1.000000         3.000000   \n",
       "75%                       4.000000     5.000000         5.000000   \n",
       "max                       7.000000    10.000000         9.000000   \n",
       "\n",
       "       Bland Chromatin  Mitoses       Class  \n",
       "count       690.000000    690.0  690.000000  \n",
       "mean          2.685507      1.0    2.689855  \n",
       "std           2.635240      0.0    0.951379  \n",
       "min           1.000000      1.0    2.000000  \n",
       "25%           1.000000      1.0    2.000000  \n",
       "50%           1.000000      1.0    2.000000  \n",
       "75%           4.000000      1.0    4.000000  \n",
       "max           8.000000      1.0    4.000000  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c8e73520f0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.boxplot(figsize=(15, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ViVkMsQTWCza"
   },
   "source": [
    "We could that see most of the outliers are now removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop(columns='Class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_sc = sc.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lmMHMAtkWCzm"
   },
   "source": [
    "# Q6 Create a covariance matrix for identifying Principal components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "X3pUdlSdSM5D"
   },
   "outputs": [],
   "source": [
    "# PCA\n",
    "# Step 1 - Create covariance matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ppL2hWreSM5G"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Covariance Matrix \n",
      "%s [[1.00145138 0.64410677 0.65453792 0.50566766 0.54660373 0.59150555\n",
      "  0.56762194 0.54895554 0.        ]\n",
      " [0.64410677 1.00145138 0.9066632  0.73190433 0.78174972 0.68556472\n",
      "  0.76060761 0.7322064  0.        ]\n",
      " [0.65453792 0.9066632  1.00145138 0.70664526 0.75160088 0.70518656\n",
      "  0.73975369 0.72768806 0.        ]\n",
      " [0.50566766 0.73190433 0.70664526 1.00145138 0.66784054 0.68919517\n",
      "  0.68896322 0.6334195  0.        ]\n",
      " [0.54660373 0.78174972 0.75160088 0.66784054 1.00145138 0.6263289\n",
      "  0.66056222 0.68990315 0.        ]\n",
      " [0.59150555 0.68556472 0.70518656 0.68919517 0.6263289  1.00145138\n",
      "  0.68161737 0.60106392 0.        ]\n",
      " [0.56762194 0.76060761 0.73975369 0.68896322 0.66056222 0.68161737\n",
      "  1.00145138 0.6803516  0.        ]\n",
      " [0.54895554 0.7322064  0.72768806 0.6334195  0.68990315 0.60106392\n",
      "  0.6803516  1.00145138 0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.        ]]\n"
     ]
    }
   ],
   "source": [
    "cov_matrix = np.cov(X_sc.T)\n",
    "print('Covariance Matrix \\n%s', cov_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vbZ-YcsdWCzz"
   },
   "source": [
    "# Q7 Identify eigen values and eigen vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sH-AksvLSM5L"
   },
   "outputs": [],
   "source": [
    "# Step 2- Get eigen values and eigen vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YaNotXfFSM5O"
   },
   "outputs": [],
   "source": [
    "eig_vals, eig_vecs = np.linalg.eig(cov_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eigen Vectors \n",
      "%s [[ 0.30799679 -0.88855863 -0.00115266  0.05182637  0.19731942 -0.26631535\n",
      "  -0.02340462  0.05007625  0.        ]\n",
      " [ 0.38632446  0.04486697 -0.73943418  0.19756566 -0.34955138  0.03868533\n",
      "  -0.20289405 -0.31320367  0.        ]\n",
      " [ 0.38305998 -0.02112671  0.65853186  0.16859924 -0.52512896  0.11953191\n",
      "  -0.17281745 -0.26611016  0.        ]\n",
      " [ 0.34656129  0.33312259  0.05684034 -0.42387996  0.03652014 -0.73667336\n",
      "  -0.19357909  0.06727064  0.        ]\n",
      " [ 0.35338061  0.21633291  0.07824581  0.3339381   0.57169387  0.26208414\n",
      "  -0.51592101  0.22133388  0.        ]\n",
      " [ 0.34279211 -0.08446144 -0.0792238  -0.6516513  -0.14992364  0.51277586\n",
      "   0.03050267  0.39760207  0.        ]\n",
      " [ 0.35647419  0.12116092  0.05896917 -0.15516848  0.44187298  0.14884046\n",
      "   0.51600916 -0.58908479  0.        ]\n",
      " [ 0.34582304  0.16858195 -0.02155318  0.43582173 -0.13123266 -0.12970504\n",
      "   0.59797754  0.51957568  0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          1.        ]]\n",
      "\n",
      " Eigen Values \n",
      "%s [5.75121822 0.53651102 0.09014311 0.45176508 0.25234651 0.28616215\n",
      " 0.33869038 0.30477455 0.        ]\n"
     ]
    }
   ],
   "source": [
    "print('Eigen Vectors \\n%s', eig_vecs)\n",
    "print('\\n Eigen Values \\n%s', eig_vals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-2KqQg2KWCz7"
   },
   "source": [
    "# Q8 Find variance and cumulative variance by each eigen vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HVrVYdZpSM5e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cumulative Variance Explained:\n",
      "[ 71.78603898  78.48270738  84.12158678  88.34908082  92.15324148\n",
      "  95.72508426  98.87484419 100.         100.        ]\n"
     ]
    }
   ],
   "source": [
    "tot = sum(eig_vals)\n",
    "var_exp = [( i /tot ) * 100 for i in sorted(eig_vals, reverse=True)]\n",
    "cum_var_exp = np.cumsum(var_exp)\n",
    "print(\"Cumulative Variance Explained:\")\n",
    "print(cum_var_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wGszn1TqSM5i"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure(figsize=(6, 4))\n",
    "plt.bar(range(9), var_exp, alpha = 0.5, align = 'center', label = 'Individual explained variance')\n",
    "plt.step(range(9), cum_var_exp, where='mid', label = 'Cumulative explained variance')\n",
    "plt.ylabel('Explained Variance Ratio')\n",
    "plt.xlabel('Principal Components')\n",
    "plt.legend(loc = 'best')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "x8YxQPNGWC0e"
   },
   "source": [
    "# Q9 Use PCA command from sklearn and find Principal Components. Transform data to components formed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will be taking 4 Principal Components which is explaining 88% of variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZgniQrGWSM5l"
   },
   "outputs": [],
   "source": [
    "X_reduced = pca.fit_transform(X_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-06rQu3PSM5o"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are the transformed features:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-1.45389231, -0.61826293,  0.02794098, -0.20951456],\n",
       "       [ 1.85955123,  0.40801342, -0.80358543,  1.63437454],\n",
       "       [-1.57785207, -0.01040327, -0.1891672 , -0.23458018],\n",
       "       ...,\n",
       "       [ 3.99536572,  0.98811752,  2.19806797, -0.16353707],\n",
       "       [ 2.43714359,  0.84764399,  0.31840765, -1.39997342],\n",
       "       [ 2.86804471,  0.93880135, -0.06121041, -0.4675035 ]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('These are the transformed features:')\n",
    "X_reduced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are the transformed components:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.30799679,  0.38632446,  0.38305998,  0.34656129,  0.35338061,\n",
       "         0.34279211,  0.35647419,  0.34582304, -0.        ],\n",
       "       [-0.88855863,  0.04486697, -0.02112671,  0.33312259,  0.21633291,\n",
       "        -0.08446144,  0.12116092,  0.16858195, -0.        ],\n",
       "       [ 0.05182637,  0.19756566,  0.16859924, -0.42387996,  0.3339381 ,\n",
       "        -0.6516513 , -0.15516848,  0.43582173, -0.        ],\n",
       "       [ 0.02340462,  0.20289405,  0.17281745,  0.19357909,  0.51592101,\n",
       "        -0.03050267, -0.51600916, -0.59797754,  0.        ]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('These are the transformed components:')\n",
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc = pca.explained_variance_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.75121822, 6.28772924, 6.73949433, 7.0781847 ])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(pc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8L-IGI0QWC0t"
   },
   "source": [
    "# Q10 Find correlation between components and features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eLi4UsMcSM5r"
   },
   "outputs": [],
   "source": [
    "corr_comp_feature = pd.DataFrame(pca.components_, columns = list(X.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bXUp9TCDWC1G"
   },
   "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>ClumpThickness</th>\n",
       "      <th>Cell Size</th>\n",
       "      <th>Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Mitoses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.307997</td>\n",
       "      <td>0.386324</td>\n",
       "      <td>0.383060</td>\n",
       "      <td>0.346561</td>\n",
       "      <td>0.353381</td>\n",
       "      <td>0.342792</td>\n",
       "      <td>0.356474</td>\n",
       "      <td>0.345823</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.888559</td>\n",
       "      <td>0.044867</td>\n",
       "      <td>-0.021127</td>\n",
       "      <td>0.333123</td>\n",
       "      <td>0.216333</td>\n",
       "      <td>-0.084461</td>\n",
       "      <td>0.121161</td>\n",
       "      <td>0.168582</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.051826</td>\n",
       "      <td>0.197566</td>\n",
       "      <td>0.168599</td>\n",
       "      <td>-0.423880</td>\n",
       "      <td>0.333938</td>\n",
       "      <td>-0.651651</td>\n",
       "      <td>-0.155168</td>\n",
       "      <td>0.435822</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.023405</td>\n",
       "      <td>0.202894</td>\n",
       "      <td>0.172817</td>\n",
       "      <td>0.193579</td>\n",
       "      <td>0.515921</td>\n",
       "      <td>-0.030503</td>\n",
       "      <td>-0.516009</td>\n",
       "      <td>-0.597978</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ClumpThickness  Cell Size  Cell Shape  Marginal Adhesion  \\\n",
       "0        0.307997   0.386324    0.383060           0.346561   \n",
       "1       -0.888559   0.044867   -0.021127           0.333123   \n",
       "2        0.051826   0.197566    0.168599          -0.423880   \n",
       "3        0.023405   0.202894    0.172817           0.193579   \n",
       "\n",
       "   Single Epithelial Cell Size  Bare Nuclei  Normal Nucleoli  Bland Chromatin  \\\n",
       "0                     0.353381     0.342792         0.356474         0.345823   \n",
       "1                     0.216333    -0.084461         0.121161         0.168582   \n",
       "2                     0.333938    -0.651651        -0.155168         0.435822   \n",
       "3                     0.515921    -0.030503        -0.516009        -0.597978   \n",
       "\n",
       "   Mitoses  \n",
       "0     -0.0  \n",
       "1     -0.0  \n",
       "2     -0.0  \n",
       "3      0.0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_comp_feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HnHXocYbSM6n"
   },
   "source": [
    "# Popularity Based Recommendation System"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "r8LHW5isSM6o"
   },
   "source": [
    "### About Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "m8i-LSc4SM6p"
   },
   "source": [
    "Anonymous Ratings on jokes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "y-wmIsyYSM6p"
   },
   "source": [
    "1. Ratings are real values ranging from -10.00 to +10.00 (the value \"99\" corresponds to \"null\" = \"not rated\").\n",
    "\n",
    "2. One row per user\n",
    "\n",
    "3. The first column gives the number of jokes rated by that user. The next 100 columns give the ratings for jokes 01 - 100."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LC-Uca2PSM6q"
   },
   "source": [
    "# Q11 Read the dataset(jokes.csv)\n",
    "\n",
    "Take care about the header in read_csv() as there are no column names given in the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mndzWciDSM6r"
   },
   "outputs": [],
   "source": [
    "ratings_data = pd.read_csv('./Data/jokes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24983, 101)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['NumJokes', 'Joke1', 'Joke2', 'Joke3', 'Joke4', 'Joke5', 'Joke6',\n",
       "       'Joke7', 'Joke8', 'Joke9',\n",
       "       ...\n",
       "       'Joke91', 'Joke92', 'Joke93', 'Joke94', 'Joke95', 'Joke96', 'Joke97',\n",
       "       'Joke98', 'Joke99', 'Joke100'],\n",
       "      dtype='object', length=101)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The headers are already given in the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6QEWF8o-SM6t"
   },
   "source": [
    "# Q12 Consider `ratings` named dataframe with only first 200 rows and all columns from 1(first column is 0) of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = ratings_data.iloc[0:200, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xc0b-lBRSM6u"
   },
   "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>Joke1</th>\n",
       "      <th>Joke2</th>\n",
       "      <th>Joke3</th>\n",
       "      <th>Joke4</th>\n",
       "      <th>Joke5</th>\n",
       "      <th>Joke6</th>\n",
       "      <th>Joke7</th>\n",
       "      <th>Joke8</th>\n",
       "      <th>Joke9</th>\n",
       "      <th>Joke10</th>\n",
       "      <th>...</th>\n",
       "      <th>Joke91</th>\n",
       "      <th>Joke92</th>\n",
       "      <th>Joke93</th>\n",
       "      <th>Joke94</th>\n",
       "      <th>Joke95</th>\n",
       "      <th>Joke96</th>\n",
       "      <th>Joke97</th>\n",
       "      <th>Joke98</th>\n",
       "      <th>Joke99</th>\n",
       "      <th>Joke100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-7.82</td>\n",
       "      <td>8.79</td>\n",
       "      <td>-9.66</td>\n",
       "      <td>-8.16</td>\n",
       "      <td>-7.52</td>\n",
       "      <td>-8.50</td>\n",
       "      <td>-9.85</td>\n",
       "      <td>4.17</td>\n",
       "      <td>-8.98</td>\n",
       "      <td>-4.76</td>\n",
       "      <td>...</td>\n",
       "      <td>2.82</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>-5.63</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.08</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>6.36</td>\n",
       "      <td>4.37</td>\n",
       "      <td>-2.38</td>\n",
       "      <td>-9.66</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-5.34</td>\n",
       "      <td>8.88</td>\n",
       "      <td>9.22</td>\n",
       "      <td>...</td>\n",
       "      <td>2.82</td>\n",
       "      <td>-4.95</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>7.86</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-2.14</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-4.32</td>\n",
       "      <td>1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>9.03</td>\n",
       "      <td>9.27</td>\n",
       "      <td>9.03</td>\n",
       "      <td>9.27</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>...</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>9.08</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99.00</td>\n",
       "      <td>8.35</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>1.80</td>\n",
       "      <td>8.16</td>\n",
       "      <td>-2.82</td>\n",
       "      <td>6.21</td>\n",
       "      <td>99.00</td>\n",
       "      <td>1.84</td>\n",
       "      <td>...</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>0.53</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.50</td>\n",
       "      <td>4.61</td>\n",
       "      <td>-4.17</td>\n",
       "      <td>-5.39</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1.60</td>\n",
       "      <td>7.04</td>\n",
       "      <td>4.61</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>5.73</td>\n",
       "      <td>...</td>\n",
       "      <td>5.19</td>\n",
       "      <td>5.58</td>\n",
       "      <td>4.27</td>\n",
       "      <td>5.19</td>\n",
       "      <td>5.73</td>\n",
       "      <td>1.55</td>\n",
       "      <td>3.11</td>\n",
       "      <td>6.55</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Joke1  Joke2  Joke3  Joke4  Joke5  Joke6  Joke7  Joke8  Joke9  Joke10  ...  \\\n",
       "0  -7.82   8.79  -9.66  -8.16  -7.52  -8.50  -9.85   4.17  -8.98   -4.76  ...   \n",
       "1   4.08  -0.29   6.36   4.37  -2.38  -9.66  -0.73  -5.34   8.88    9.22  ...   \n",
       "2  99.00  99.00  99.00  99.00   9.03   9.27   9.03   9.27  99.00   99.00  ...   \n",
       "3  99.00   8.35  99.00  99.00   1.80   8.16  -2.82   6.21  99.00    1.84  ...   \n",
       "4   8.50   4.61  -4.17  -5.39   1.36   1.60   7.04   4.61  -0.44    5.73  ...   \n",
       "\n",
       "   Joke91  Joke92  Joke93  Joke94  Joke95  Joke96  Joke97  Joke98  Joke99  \\\n",
       "0    2.82   99.00   99.00   99.00   99.00   99.00   -5.63   99.00   99.00   \n",
       "1    2.82   -4.95   -0.29    7.86   -0.19   -2.14    3.06    0.34   -4.32   \n",
       "2   99.00   99.00   99.00    9.08   99.00   99.00   99.00   99.00   99.00   \n",
       "3   99.00   99.00   99.00    0.53   99.00   99.00   99.00   99.00   99.00   \n",
       "4    5.19    5.58    4.27    5.19    5.73    1.55    3.11    6.55    1.80   \n",
       "\n",
       "   Joke100  \n",
       "0    99.00  \n",
       "1     1.07  \n",
       "2    99.00  \n",
       "3    99.00  \n",
       "4     1.60  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yl1TNKQGSM6y"
   },
   "source": [
    "# Q13 Change the column indices from 0 to 99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "376Xre-TSM6z"
   },
   "outputs": [],
   "source": [
    "ratings.columns = range(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-7.82</td>\n",
       "      <td>8.79</td>\n",
       "      <td>-9.66</td>\n",
       "      <td>-8.16</td>\n",
       "      <td>-7.52</td>\n",
       "      <td>-8.50</td>\n",
       "      <td>-9.85</td>\n",
       "      <td>4.17</td>\n",
       "      <td>-8.98</td>\n",
       "      <td>-4.76</td>\n",
       "      <td>...</td>\n",
       "      <td>2.82</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>-5.63</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.08</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>6.36</td>\n",
       "      <td>4.37</td>\n",
       "      <td>-2.38</td>\n",
       "      <td>-9.66</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-5.34</td>\n",
       "      <td>8.88</td>\n",
       "      <td>9.22</td>\n",
       "      <td>...</td>\n",
       "      <td>2.82</td>\n",
       "      <td>-4.95</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>7.86</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-2.14</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-4.32</td>\n",
       "      <td>1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>9.03</td>\n",
       "      <td>9.27</td>\n",
       "      <td>9.03</td>\n",
       "      <td>9.27</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>...</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>9.08</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99.00</td>\n",
       "      <td>8.35</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>1.80</td>\n",
       "      <td>8.16</td>\n",
       "      <td>-2.82</td>\n",
       "      <td>6.21</td>\n",
       "      <td>99.00</td>\n",
       "      <td>1.84</td>\n",
       "      <td>...</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>0.53</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.50</td>\n",
       "      <td>4.61</td>\n",
       "      <td>-4.17</td>\n",
       "      <td>-5.39</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1.60</td>\n",
       "      <td>7.04</td>\n",
       "      <td>4.61</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>5.73</td>\n",
       "      <td>...</td>\n",
       "      <td>5.19</td>\n",
       "      <td>5.58</td>\n",
       "      <td>4.27</td>\n",
       "      <td>5.19</td>\n",
       "      <td>5.73</td>\n",
       "      <td>1.55</td>\n",
       "      <td>3.11</td>\n",
       "      <td>6.55</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0      1      2      3     4     5     6     7      8      9   ...  \\\n",
       "0  -7.82   8.79  -9.66  -8.16 -7.52 -8.50 -9.85  4.17  -8.98  -4.76  ...   \n",
       "1   4.08  -0.29   6.36   4.37 -2.38 -9.66 -0.73 -5.34   8.88   9.22  ...   \n",
       "2  99.00  99.00  99.00  99.00  9.03  9.27  9.03  9.27  99.00  99.00  ...   \n",
       "3  99.00   8.35  99.00  99.00  1.80  8.16 -2.82  6.21  99.00   1.84  ...   \n",
       "4   8.50   4.61  -4.17  -5.39  1.36  1.60  7.04  4.61  -0.44   5.73  ...   \n",
       "\n",
       "      90     91     92     93     94     95     96     97     98     99  \n",
       "0   2.82  99.00  99.00  99.00  99.00  99.00  -5.63  99.00  99.00  99.00  \n",
       "1   2.82  -4.95  -0.29   7.86  -0.19  -2.14   3.06   0.34  -4.32   1.07  \n",
       "2  99.00  99.00  99.00   9.08  99.00  99.00  99.00  99.00  99.00  99.00  \n",
       "3  99.00  99.00  99.00   0.53  99.00  99.00  99.00  99.00  99.00  99.00  \n",
       "4   5.19   5.58   4.27   5.19   5.73   1.55   3.11   6.55   1.80   1.60  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "d-0_FGl0SM6-"
   },
   "source": [
    "# Q14 In the dataset, the null ratings are given as 99.00, so replace all 99.00s with 0\n",
    "Hint: You can use `ratings.replace(<the given value>, <new value you wanted to change with>)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "veL5hwRLSM6_"
   },
   "outputs": [],
   "source": [
    "ratings.replace(to_replace=99.00, value=0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tgXnvJajSM7E"
   },
   "source": [
    "# Q15 Normalize the ratings using StandardScaler and save them in `ratings_diff` variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we do the direct scaling of ratings data, it will compute the mean as zero for all jokes.\n",
    "\n",
    "We will be doing the scaling across users (rowwise), so that every joke will have scaled ratings for every user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JS3Mx_DjSM7K"
   },
   "outputs": [],
   "source": [
    "ratings_diff = pd.DataFrame(sc.fit_transform(ratings.T).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.927207</td>\n",
       "      <td>1.989411</td>\n",
       "      <td>-1.250300</td>\n",
       "      <td>-0.986909</td>\n",
       "      <td>-0.874529</td>\n",
       "      <td>-1.046611</td>\n",
       "      <td>-1.283663</td>\n",
       "      <td>1.178166</td>\n",
       "      <td>-1.130896</td>\n",
       "      <td>-0.389889</td>\n",
       "      <td>...</td>\n",
       "      <td>0.941114</td>\n",
       "      <td>0.445939</td>\n",
       "      <td>0.445939</td>\n",
       "      <td>0.445939</td>\n",
       "      <td>0.445939</td>\n",
       "      <td>0.445939</td>\n",
       "      <td>-0.542656</td>\n",
       "      <td>0.445939</td>\n",
       "      <td>0.445939</td>\n",
       "      <td>0.445939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.274579</td>\n",
       "      <td>-0.625106</td>\n",
       "      <td>0.743980</td>\n",
       "      <td>0.334284</td>\n",
       "      <td>-1.055391</td>\n",
       "      <td>-2.554180</td>\n",
       "      <td>-0.715693</td>\n",
       "      <td>-1.664789</td>\n",
       "      <td>1.262792</td>\n",
       "      <td>1.332790</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015173</td>\n",
       "      <td>-1.584496</td>\n",
       "      <td>-0.625106</td>\n",
       "      <td>1.052797</td>\n",
       "      <td>-0.604519</td>\n",
       "      <td>-1.005980</td>\n",
       "      <td>0.064584</td>\n",
       "      <td>-0.495403</td>\n",
       "      <td>-1.454794</td>\n",
       "      <td>-0.345113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.787867</td>\n",
       "      <td>-0.787867</td>\n",
       "      <td>-0.787867</td>\n",
       "      <td>-0.787867</td>\n",
       "      <td>1.257275</td>\n",
       "      <td>1.311631</td>\n",
       "      <td>1.257275</td>\n",
       "      <td>1.311631</td>\n",
       "      <td>-0.787867</td>\n",
       "      <td>-0.787867</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.787867</td>\n",
       "      <td>-0.787867</td>\n",
       "      <td>-0.787867</td>\n",
       "      <td>1.268600</td>\n",
       "      <td>-0.787867</td>\n",
       "      <td>-0.787867</td>\n",
       "      <td>-0.787867</td>\n",
       "      <td>-0.787867</td>\n",
       "      <td>-0.787867</td>\n",
       "      <td>-0.787867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.347962</td>\n",
       "      <td>1.929242</td>\n",
       "      <td>-0.347962</td>\n",
       "      <td>-0.347962</td>\n",
       "      <td>0.142932</td>\n",
       "      <td>1.877425</td>\n",
       "      <td>-1.117030</td>\n",
       "      <td>1.345623</td>\n",
       "      <td>-0.347962</td>\n",
       "      <td>0.153841</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.347962</td>\n",
       "      <td>-0.347962</td>\n",
       "      <td>-0.347962</td>\n",
       "      <td>-0.203421</td>\n",
       "      <td>-0.347962</td>\n",
       "      <td>-0.347962</td>\n",
       "      <td>-0.347962</td>\n",
       "      <td>-0.347962</td>\n",
       "      <td>-0.347962</td>\n",
       "      <td>-0.347962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.306203</td>\n",
       "      <td>0.389048</td>\n",
       "      <td>-1.681034</td>\n",
       "      <td>-1.968676</td>\n",
       "      <td>-0.377212</td>\n",
       "      <td>-0.320627</td>\n",
       "      <td>0.961975</td>\n",
       "      <td>0.389048</td>\n",
       "      <td>-0.801603</td>\n",
       "      <td>0.653113</td>\n",
       "      <td>...</td>\n",
       "      <td>0.525796</td>\n",
       "      <td>0.617747</td>\n",
       "      <td>0.308885</td>\n",
       "      <td>0.525796</td>\n",
       "      <td>0.653113</td>\n",
       "      <td>-0.332416</td>\n",
       "      <td>0.035389</td>\n",
       "      <td>0.846447</td>\n",
       "      <td>-0.273472</td>\n",
       "      <td>-0.320627</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6   \\\n",
       "0 -0.927207  1.989411 -1.250300 -0.986909 -0.874529 -1.046611 -1.283663   \n",
       "1  0.274579 -0.625106  0.743980  0.334284 -1.055391 -2.554180 -0.715693   \n",
       "2 -0.787867 -0.787867 -0.787867 -0.787867  1.257275  1.311631  1.257275   \n",
       "3 -0.347962  1.929242 -0.347962 -0.347962  0.142932  1.877425 -1.117030   \n",
       "4  1.306203  0.389048 -1.681034 -1.968676 -0.377212 -0.320627  0.961975   \n",
       "\n",
       "         7         8         9   ...        90        91        92        93  \\\n",
       "0  1.178166 -1.130896 -0.389889  ...  0.941114  0.445939  0.445939  0.445939   \n",
       "1 -1.664789  1.262792  1.332790  ...  0.015173 -1.584496 -0.625106  1.052797   \n",
       "2  1.311631 -0.787867 -0.787867  ... -0.787867 -0.787867 -0.787867  1.268600   \n",
       "3  1.345623 -0.347962  0.153841  ... -0.347962 -0.347962 -0.347962 -0.203421   \n",
       "4  0.389048 -0.801603  0.653113  ...  0.525796  0.617747  0.308885  0.525796   \n",
       "\n",
       "         94        95        96        97        98        99  \n",
       "0  0.445939  0.445939 -0.542656  0.445939  0.445939  0.445939  \n",
       "1 -0.604519 -1.005980  0.064584 -0.495403 -1.454794 -0.345113  \n",
       "2 -0.787867 -0.787867 -0.787867 -0.787867 -0.787867 -0.787867  \n",
       "3 -0.347962 -0.347962 -0.347962 -0.347962 -0.347962 -0.347962  \n",
       "4  0.653113 -0.332416  0.035389  0.846447 -0.273472 -0.320627  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_diff.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bWFmyVobSM7R"
   },
   "source": [
    "### Popularity based recommendation system"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "T8K_S9ukSM7b"
   },
   "source": [
    "# Q16  Find the mean for each column  in `ratings_diff` i.e, for each joke\n",
    "Consider all the mean ratings and find the jokes with highest mean value and display the top 10 joke IDs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "usXWscbfSM7c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are the index of the top 10 jokes with highest ratings:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[49, 35, 52, 34, 28, 26, 61, 48, 31, 30]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('These are the index of the top 10 jokes with highest ratings:')\n",
    "list(np.argsort(ratings_diff.mean())[::-1][:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are the top 10 highest ratings:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.87821678, 0.75197178, 0.73670016, 0.69230389, 0.6642304 ,\n",
       "       0.60972362, 0.55754984, 0.5320071 , 0.52189075, 0.50249102])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('These are the top 10 highest ratings:')\n",
    "np.sort(ratings_diff.mean())[::-1][:10]"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "R5_Internal_Lab_PCA_PBRS.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}