R5_Internal_Lab_PCA.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": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = 'all'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6FZ90t1vWCvq"
   },
   "source": [
    "# Q1. Load the Data file into Python DataFrame and view top 10 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('breast-cancer-wisconsin-data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<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",
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      "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",
       "\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",
       "\n",
       "   Mitoses  Class  \n",
       "0        1      2  \n",
       "1        1      2  \n",
       "2        1      2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(699, 11)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "7689"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.shape\n",
    "data.size\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <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",
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       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
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       "    <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",
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       "      <td>1.589413</td>\n",
       "      <td>2.689557</td>\n",
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       "      <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",
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       "    <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",
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       "      <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",
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      "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "ID                              int64\n",
       "ClumpThickness                  int64\n",
       "Cell Size                       int64\n",
       "Cell Shape                      int64\n",
       "Marginal Adhesion               int64\n",
       "Single Epithelial Cell Size     int64\n",
       "Bare Nuclei                    object\n",
       "Normal Nucleoli                 int64\n",
       "Bland Chromatin                 int64\n",
       "Mitoses                         int64\n",
       "Class                           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'ClumpThickness', 'Cell Size', 'Cell Shape', 'Marginal Adhesion',\n",
       "       'Single Epithelial Cell Size', 'Bare Nuclei', 'Normal Nucleoli',\n",
       "       'Bland Chromatin', 'Mitoses', 'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()\n",
    "data.dtypes\n",
    "data.columns"
   ]
  },
  {
   "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                             0\n",
       "ClumpThickness                 0\n",
       "Cell Size                      0\n",
       "Cell Shape                     0\n",
       "Marginal Adhesion              0\n",
       "Single Epithelial Cell Size    0\n",
       "Bare Nuclei                    0\n",
       "Normal Nucleoli                0\n",
       "Bland Chromatin                0\n",
       "Mitoses                        0\n",
       "Class                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "      <th>5</th>\n",
       "      <td>1017122</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1018099</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
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       "      <td>2</td>\n",
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       "      <th>7</th>\n",
       "      <td>1018561</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>8</th>\n",
       "      <td>1033078</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1033078</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1035283</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>11</th>\n",
       "      <td>1036172</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1041801</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1043999</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1044572</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1047630</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1048672</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1049815</td>\n",
       "      <td>4</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>18</th>\n",
       "      <td>1050670</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1050718</td>\n",
       "      <td>6</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>20</th>\n",
       "      <td>1054590</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1054593</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1056784</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1057013</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1059552</td>\n",
       "      <td>1</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>25</th>\n",
       "      <td>1065726</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1066373</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1066979</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>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1067444</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1070935</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>1350423</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>1352848</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>1353092</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>672</th>\n",
       "      <td>1354840</td>\n",
       "      <td>2</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>673</th>\n",
       "      <td>1354840</td>\n",
       "      <td>5</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>1355260</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>1365075</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>1365328</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>1368267</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>1368273</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>1368882</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>1369821</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>1371026</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>1371920</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>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>466906</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>466906</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>534555</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>536708</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>566346</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>603148</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>654546</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>654546</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>695091</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>714039</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>763235</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>776715</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>841769</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>888820</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>897471</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>897471</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>699 rows × 11 columns</p>\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",
       "5    1017122               8         10          10                  8   \n",
       "6    1018099               1          1           1                  1   \n",
       "7    1018561               2          1           2                  1   \n",
       "8    1033078               2          1           1                  1   \n",
       "9    1033078               4          2           1                  1   \n",
       "10   1035283               1          1           1                  1   \n",
       "11   1036172               2          1           1                  1   \n",
       "12   1041801               5          3           3                  3   \n",
       "13   1043999               1          1           1                  1   \n",
       "14   1044572               8          7           5                 10   \n",
       "15   1047630               7          4           6                  4   \n",
       "16   1048672               4          1           1                  1   \n",
       "17   1049815               4          1           1                  1   \n",
       "18   1050670              10          7           7                  6   \n",
       "19   1050718               6          1           1                  1   \n",
       "20   1054590               7          3           2                 10   \n",
       "21   1054593              10          5           5                  3   \n",
       "22   1056784               3          1           1                  1   \n",
       "23   1057013               8          4           5                  1   \n",
       "24   1059552               1          1           1                  1   \n",
       "25   1065726               5          2           3                  4   \n",
       "26   1066373               3          2           1                  1   \n",
       "27   1066979               5          1           1                  1   \n",
       "28   1067444               2          1           1                  1   \n",
       "29   1070935               1          1           3                  1   \n",
       "..       ...             ...        ...         ...                ...   \n",
       "669  1350423               5         10          10                  8   \n",
       "670  1352848               3         10           7                  8   \n",
       "671  1353092               3          2           1                  2   \n",
       "672  1354840               2          1           1                  1   \n",
       "673  1354840               5          3           2                  1   \n",
       "674  1355260               1          1           1                  1   \n",
       "675  1365075               4          1           4                  1   \n",
       "676  1365328               1          1           2                  1   \n",
       "677  1368267               5          1           1                  1   \n",
       "678  1368273               1          1           1                  1   \n",
       "679  1368882               2          1           1                  1   \n",
       "680  1369821              10         10          10                 10   \n",
       "681  1371026               5         10          10                 10   \n",
       "682  1371920               5          1           1                  1   \n",
       "683   466906               1          1           1                  1   \n",
       "684   466906               1          1           1                  1   \n",
       "685   534555               1          1           1                  1   \n",
       "686   536708               1          1           1                  1   \n",
       "687   566346               3          1           1                  1   \n",
       "688   603148               4          1           1                  1   \n",
       "689   654546               1          1           1                  1   \n",
       "690   654546               1          1           1                  3   \n",
       "691   695091               5         10          10                  5   \n",
       "692   714039               3          1           1                  1   \n",
       "693   763235               3          1           1                  1   \n",
       "694   776715               3          1           1                  1   \n",
       "695   841769               2          1           1                  1   \n",
       "696   888820               5         10          10                  3   \n",
       "697   897471               4          8           6                  4   \n",
       "698   897471               4          8           8                  5   \n",
       "\n",
       "     Single Epithelial Cell Size Bare Nuclei  Normal Nucleoli  \\\n",
       "0                              2           1                3   \n",
       "1                              7          10                3   \n",
       "2                              2           2                3   \n",
       "3                              3           4                3   \n",
       "4                              2           1                3   \n",
       "5                              7          10                9   \n",
       "6                              2          10                3   \n",
       "7                              2           1                3   \n",
       "8                              2           1                1   \n",
       "9                              2           1                2   \n",
       "10                             1           1                3   \n",
       "11                             2           1                2   \n",
       "12                             2           3                4   \n",
       "13                             2           3                3   \n",
       "14                             7           9                5   \n",
       "15                             6           1                4   \n",
       "16                             2           1                2   \n",
       "17                             2           1                3   \n",
       "18                             4          10                4   \n",
       "19                             2           1                3   \n",
       "20                             5          10                5   \n",
       "21                             6           7                7   \n",
       "22                             2           1                2   \n",
       "23                             2           ?                7   \n",
       "24                             2           1                3   \n",
       "25                             2           7                3   \n",
       "26                             1           1                2   \n",
       "27                             2           1                2   \n",
       "28                             2           1                2   \n",
       "29                             2           1                1   \n",
       "..                           ...         ...              ...   \n",
       "669                            5           5                7   \n",
       "670                            5           8                7   \n",
       "671                            2           1                3   \n",
       "672                            2           1                3   \n",
       "673                            3           1                1   \n",
       "674                            2           1                2   \n",
       "675                            2           1                1   \n",
       "676                            2           1                2   \n",
       "677                            2           1                1   \n",
       "678                            2           1                1   \n",
       "679                            2           1                1   \n",
       "680                            5          10               10   \n",
       "681                            4          10                5   \n",
       "682                            2           1                3   \n",
       "683                            2           1                1   \n",
       "684                            2           1                1   \n",
       "685                            2           1                1   \n",
       "686                            2           1                1   \n",
       "687                            2           1                2   \n",
       "688                            2           1                1   \n",
       "689                            2           1                1   \n",
       "690                            2           1                1   \n",
       "691                            4           5                4   \n",
       "692                            2           1                1   \n",
       "693                            2           1                2   \n",
       "694                            3           2                1   \n",
       "695                            2           1                1   \n",
       "696                            7           3                8   \n",
       "697                            3           4               10   \n",
       "698                            4           5               10   \n",
       "\n",
       "     Bland Chromatin  Mitoses  Class  \n",
       "0                  1        1      2  \n",
       "1                  2        1      2  \n",
       "2                  1        1      2  \n",
       "3                  7        1      2  \n",
       "4                  1        1      2  \n",
       "5                  7        1      4  \n",
       "6                  1        1      2  \n",
       "7                  1        1      2  \n",
       "8                  1        5      2  \n",
       "9                  1        1      2  \n",
       "10                 1        1      2  \n",
       "11                 1        1      2  \n",
       "12                 4        1      4  \n",
       "13                 1        1      2  \n",
       "14                 5        4      4  \n",
       "15                 3        1      4  \n",
       "16                 1        1      2  \n",
       "17                 1        1      2  \n",
       "18                 1        2      4  \n",
       "19                 1        1      2  \n",
       "20                 4        4      4  \n",
       "21                10        1      4  \n",
       "22                 1        1      2  \n",
       "23                 3        1      4  \n",
       "24                 1        1      2  \n",
       "25                 6        1      4  \n",
       "26                 1        1      2  \n",
       "27                 1        1      2  \n",
       "28                 1        1      2  \n",
       "29                 1        1      2  \n",
       "..               ...      ...    ...  \n",
       "669               10        1      4  \n",
       "670                4        1      4  \n",
       "671                1        1      2  \n",
       "672                1        1      2  \n",
       "673                1        1      2  \n",
       "674                1        1      2  \n",
       "675                1        1      2  \n",
       "676                1        1      2  \n",
       "677                1        1      2  \n",
       "678                1        1      2  \n",
       "679                1        1      2  \n",
       "680               10        7      4  \n",
       "681                6        3      4  \n",
       "682                2        1      2  \n",
       "683                1        1      2  \n",
       "684                1        1      2  \n",
       "685                1        1      2  \n",
       "686                1        1      2  \n",
       "687                3        1      2  \n",
       "688                1        1      2  \n",
       "689                1        8      2  \n",
       "690                1        1      2  \n",
       "691                4        1      4  \n",
       "692                1        1      2  \n",
       "693                1        2      2  \n",
       "694                1        1      2  \n",
       "695                1        1      2  \n",
       "696               10        2      4  \n",
       "697                6        1      4  \n",
       "698                4        1      4  \n",
       "\n",
       "[699 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We could see \"?\" values in column, this should be removed from data set\n",
    "data.isnull().sum()\n",
    "data[~ data.applymap(np.isreal).all(1)]\n",
    "# Check for missing value in any other column"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Bare Nuclei is an object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '10', '2', '4', '3', '9', '7', '?', '5', '8', '6'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "ID                             16\n",
       "ClumpThickness                 16\n",
       "Cell Size                      16\n",
       "Cell Shape                     16\n",
       "Marginal Adhesion              16\n",
       "Single Epithelial Cell Size    16\n",
       "Bare Nuclei                    16\n",
       "Normal Nucleoli                16\n",
       "Bland Chromatin                16\n",
       "Mitoses                        16\n",
       "Class                          16\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1     402\n",
       "10    132\n",
       "5      30\n",
       "2      30\n",
       "3      28\n",
       "8      21\n",
       "4      19\n",
       "?      16\n",
       "9       9\n",
       "7       8\n",
       "6       4\n",
       "Name: Bare Nuclei, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No missing values found. So let us try to remove ? from bare nuclei column\n",
    "data['Bare Nuclei'].unique()\n",
    "# Get count of rows having ?\n",
    "data[data[\"Bare Nuclei\"] == '?'] .count()\n",
    "data[\"Bare Nuclei\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Bare Nuclei\"] = data[\"Bare Nuclei\"].replace('?',np.NaN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                              0\n",
       "ClumpThickness                  0\n",
       "Cell Size                       0\n",
       "Cell Shape                      0\n",
       "Marginal Adhesion               0\n",
       "Single Epithelial Cell Size     0\n",
       "Bare Nuclei                    16\n",
       "Normal Nucleoli                 0\n",
       "Bland Chromatin                 0\n",
       "Mitoses                         0\n",
       "Class                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JD\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:58: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "missing = Imputer(missing_values='NaN',strategy= 'mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df  = missing.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1000025.0</td>\n",
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       "4     0\n",
       "5     0\n",
       "6     0\n",
       "7     0\n",
       "8     0\n",
       "9     0\n",
       "10    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df = pd.DataFrame(data_df)\n",
    "data_df.head(2)\n",
    "data_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df.columns = ['ID', 'ClumpThickness', 'Cell Size', 'Cell Shape', 'Marginal Adhesion',\n",
    "       'Single Epithelial Cell Size', 'Bare Nuclei', 'Normal Nucleoli',\n",
    "       'Bland Chromatin', 'Mitoses', 'Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "          ID  ClumpThickness  Cell Size  Cell Shape  Marginal Adhesion  \\\n",
       "0  1000025.0             5.0        1.0         1.0                1.0   \n",
       "1  1002945.0             5.0        4.0         4.0                5.0   \n",
       "2  1015425.0             3.0        1.0         1.0                1.0   \n",
       "\n",
       "   Single Epithelial Cell Size  Bare Nuclei  Normal Nucleoli  Bland Chromatin  \\\n",
       "0                          2.0          1.0              3.0              1.0   \n",
       "1                          7.0         10.0              3.0              2.0   \n",
       "2                          2.0          2.0              3.0              1.0   \n",
       "\n",
       "   Mitoses  Class  \n",
       "0      1.0    2.0  \n",
       "1      1.0    2.0  \n",
       "2      1.0    2.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df['Bare Nuclei'] = data_df['Bare Nuclei'].astype('int64')"
   ]
  },
  {
   "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": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = list(data_df.columns)\n",
    "for i in col:\n",
    "    data_df[i] = data_df[i].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                             int64\n",
       "ClumpThickness                 int64\n",
       "Cell Size                      int64\n",
       "Cell Shape                     int64\n",
       "Marginal Adhesion              int64\n",
       "Single Epithelial Cell Size    int64\n",
       "Bare Nuclei                    int64\n",
       "Normal Nucleoli                int64\n",
       "Bland Chromatin                int64\n",
       "Mitoses                        int64\n",
       "Class                          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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      "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",
       "\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",
       "\n",
       "   Mitoses  Class  \n",
       "0        1      2  \n",
       "1        1      2  \n",
       "2        1      2  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <td>-0.064878</td>\n",
       "      <td>0.486356</td>\n",
       "      <td>0.705582</td>\n",
       "      <td>0.683079</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.599599</td>\n",
       "      <td>0.666086</td>\n",
       "      <td>0.666715</td>\n",
       "      <td>0.603352</td>\n",
       "      <td>0.417633</td>\n",
       "      <td>0.696800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <td>-0.045528</td>\n",
       "      <td>0.521816</td>\n",
       "      <td>0.751799</td>\n",
       "      <td>0.719668</td>\n",
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       "      <td>0.479101</td>\n",
       "      <td>0.682785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <td>-0.097440</td>\n",
       "      <td>0.588433</td>\n",
       "      <td>0.687417</td>\n",
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       "      <td>0.339844</td>\n",
       "      <td>0.817442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <td>-0.060051</td>\n",
       "      <td>0.558428</td>\n",
       "      <td>0.755721</td>\n",
       "      <td>0.735948</td>\n",
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       "      <td>0.344169</td>\n",
       "      <td>0.756616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <td>-0.052072</td>\n",
       "      <td>0.535835</td>\n",
       "      <td>0.722865</td>\n",
       "      <td>0.719446</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.428336</td>\n",
       "      <td>0.712244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mitoses</th>\n",
       "      <td>-0.034901</td>\n",
       "      <td>0.350034</td>\n",
       "      <td>0.458693</td>\n",
       "      <td>0.438911</td>\n",
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       "      <td>0.428336</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.423170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <td>-0.080226</td>\n",
       "      <td>0.716001</td>\n",
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       "      <td>1.000000</td>\n",
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      ],
      "text/plain": [
       "                                   ID  ClumpThickness  Cell Size  Cell Shape  \\\n",
       "ID                           1.000000       -0.055308  -0.041603   -0.041576   \n",
       "ClumpThickness              -0.055308        1.000000   0.644913    0.654589   \n",
       "Cell Size                   -0.041603        0.644913   1.000000    0.906882   \n",
       "Cell Shape                  -0.041576        0.654589   0.906882    1.000000   \n",
       "Marginal Adhesion           -0.064878        0.486356   0.705582    0.683079   \n",
       "Single Epithelial Cell Size -0.045528        0.521816   0.751799    0.719668   \n",
       "Bare Nuclei                 -0.097440        0.588433   0.687417    0.709812   \n",
       "Normal Nucleoli             -0.060051        0.558428   0.755721    0.735948   \n",
       "Bland Chromatin             -0.052072        0.535835   0.722865    0.719446   \n",
       "Mitoses                     -0.034901        0.350034   0.458693    0.438911   \n",
       "Class                       -0.080226        0.716001   0.817904    0.818934   \n",
       "\n",
       "                             Marginal Adhesion  Single Epithelial Cell Size  \\\n",
       "ID                                   -0.064878                    -0.045528   \n",
       "ClumpThickness                        0.486356                     0.521816   \n",
       "Cell Size                             0.705582                     0.751799   \n",
       "Cell Shape                            0.683079                     0.719668   \n",
       "Marginal Adhesion                     1.000000                     0.599599   \n",
       "Single Epithelial Cell Size           0.599599                     1.000000   \n",
       "Bare Nuclei                           0.666086                     0.582331   \n",
       "Normal Nucleoli                       0.666715                     0.616102   \n",
       "Bland Chromatin                       0.603352                     0.628881   \n",
       "Mitoses                               0.417633                     0.479101   \n",
       "Class                                 0.696800                     0.682785   \n",
       "\n",
       "                             Bare Nuclei  Normal Nucleoli  Bland Chromatin  \\\n",
       "ID                             -0.097440        -0.060051        -0.052072   \n",
       "ClumpThickness                  0.588433         0.558428         0.535835   \n",
       "Cell Size                       0.687417         0.755721         0.722865   \n",
       "Cell Shape                      0.709812         0.735948         0.719446   \n",
       "Marginal Adhesion               0.666086         0.666715         0.603352   \n",
       "Single Epithelial Cell Size     0.582331         0.616102         0.628881   \n",
       "Bare Nuclei                     1.000000         0.676167         0.577347   \n",
       "Normal Nucleoli                 0.676167         1.000000         0.665878   \n",
       "Bland Chromatin                 0.577347         0.665878         1.000000   \n",
       "Mitoses                         0.339844         0.344169         0.428336   \n",
       "Class                           0.817442         0.756616         0.712244   \n",
       "\n",
       "                              Mitoses     Class  \n",
       "ID                          -0.034901 -0.080226  \n",
       "ClumpThickness               0.350034  0.716001  \n",
       "Cell Size                    0.458693  0.817904  \n",
       "Cell Shape                   0.438911  0.818934  \n",
       "Marginal Adhesion            0.417633  0.696800  \n",
       "Single Epithelial Cell Size  0.479101  0.682785  \n",
       "Bare Nuclei                  0.339844  0.817442  \n",
       "Normal Nucleoli              0.344169  0.756616  \n",
       "Bland Chromatin              0.428336  0.712244  \n",
       "Mitoses                      1.000000  0.423170  \n",
       "Class                        0.423170  1.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.corr(method='pearson', min_periods=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24953df1e48>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data = data_df.corr(method='pearson', min_periods=1),annot=True,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JD\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:488: RuntimeWarning: invalid value encountered in true_divide\n",
      "  binned = fast_linbin(X, a, b, gridsize) / (delta * nobs)\n",
      "C:\\Users\\JD\\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 0x24954255e10>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2022.38x1980 with 132 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data = data_df,diag_kind='kde',hue ='Class' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check for correlation of variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let us check for pair plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df = data_df.drop(columns=['ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24957f85518>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_df.boxplot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ClumpThickness</th>\n",
       "      <td>699.0</td>\n",
       "      <td>4.417740</td>\n",
       "      <td>2.815741</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell Size</th>\n",
       "      <td>699.0</td>\n",
       "      <td>3.134478</td>\n",
       "      <td>3.051459</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell Shape</th>\n",
       "      <td>699.0</td>\n",
       "      <td>3.207439</td>\n",
       "      <td>2.971913</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <td>699.0</td>\n",
       "      <td>2.806867</td>\n",
       "      <td>2.855379</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <td>699.0</td>\n",
       "      <td>3.216023</td>\n",
       "      <td>2.214300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <td>699.0</td>\n",
       "      <td>3.532189</td>\n",
       "      <td>3.602774</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <td>699.0</td>\n",
       "      <td>3.437768</td>\n",
       "      <td>2.438364</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <td>699.0</td>\n",
       "      <td>2.866953</td>\n",
       "      <td>3.053634</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mitoses</th>\n",
       "      <td>699.0</td>\n",
       "      <td>1.589413</td>\n",
       "      <td>1.715078</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <td>699.0</td>\n",
       "      <td>2.689557</td>\n",
       "      <td>0.951273</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             count      mean       std  min  25%  50%  75%  \\\n",
       "ClumpThickness               699.0  4.417740  2.815741  1.0  2.0  4.0  6.0   \n",
       "Cell Size                    699.0  3.134478  3.051459  1.0  1.0  1.0  5.0   \n",
       "Cell Shape                   699.0  3.207439  2.971913  1.0  1.0  1.0  5.0   \n",
       "Marginal Adhesion            699.0  2.806867  2.855379  1.0  1.0  1.0  4.0   \n",
       "Single Epithelial Cell Size  699.0  3.216023  2.214300  1.0  2.0  2.0  4.0   \n",
       "Bare Nuclei                  699.0  3.532189  3.602774  1.0  1.0  1.0  5.0   \n",
       "Normal Nucleoli              699.0  3.437768  2.438364  1.0  2.0  3.0  5.0   \n",
       "Bland Chromatin              699.0  2.866953  3.053634  1.0  1.0  1.0  4.0   \n",
       "Mitoses                      699.0  1.589413  1.715078  1.0  1.0  1.0  1.0   \n",
       "Class                        699.0  2.689557  0.951273  2.0  2.0  2.0  4.0   \n",
       "\n",
       "                              max  \n",
       "ClumpThickness               10.0  \n",
       "Cell Size                    10.0  \n",
       "Cell Shape                   10.0  \n",
       "Marginal Adhesion            10.0  \n",
       "Single Epithelial Cell Size  10.0  \n",
       "Bare Nuclei                  10.0  \n",
       "Normal Nucleoli              10.0  \n",
       "Bland Chromatin              10.0  \n",
       "Mitoses                      10.0  \n",
       "Class                         4.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data_df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ViVkMsQTWCza"
   },
   "outputs": [],
   "source": [
    "# We could see most of the outliers are now removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "Class                2   4\n",
       "Marginal Adhesion         \n",
       "1                  375  32\n",
       "2                   37  21\n",
       "3                   31  27\n",
       "4                    5  28\n",
       "5                    4  19\n",
       "6                    4  18\n",
       "7                    0  13\n",
       "8                    0  25\n",
       "9                    1   4\n",
       "10                   1  54"
      ]
     },
     "execution_count": 33,
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       "Class                          2   4\n",
       "Single Epithelial Cell Size         \n",
       "1                             46   1\n",
       "2                            363  23\n",
       "3                             29  43\n",
       "4                              7  41\n",
       "5                              5  34\n",
       "6                              2  39\n",
       "7                              3   9\n",
       "8                              2  19\n",
       "9                              0   2\n",
       "10                             1  30"
      ]
     },
     "execution_count": 33,
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     "output_type": "execute_result"
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       "Class              2   4\n",
       "Normal Nucleoli         \n",
       "1                150   2\n",
       "2                159   7\n",
       "3                129  36\n",
       "4                  8  32\n",
       "5                  4  30\n",
       "6                  1   9\n",
       "7                  7  66\n",
       "8                  0  28\n",
       "9                  0  11\n",
       "10                 0  20"
      ]
     },
     "execution_count": 33,
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      "text/plain": [
       "Class              2   4\n",
       "Bland Chromatin         \n",
       "1                402  41\n",
       "2                 30   6\n",
       "3                 12  32\n",
       "4                  1  17\n",
       "5                  2  17\n",
       "6                  4  18\n",
       "7                  2  14\n",
       "8                  4  20\n",
       "9                  1  15\n",
       "10                 0  61"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "Class      2    4\n",
       "Mitoses          \n",
       "1        445  134\n",
       "2          8   27\n",
       "3          2   31\n",
       "4          0   12\n",
       "5          1    5\n",
       "6          0    3\n",
       "7          1    8\n",
       "8          1    7\n",
       "10         0   14"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(data_df['Marginal Adhesion'], data_df['Class'])\n",
    "pd.crosstab(data_df['Single Epithelial Cell Size'], data_df['Class'])\n",
    "pd.crosstab(data_df['Normal Nucleoli'], data_df['Class'])\n",
    "pd.crosstab(data_df['Bland Chromatin'], data_df['Class'])\n",
    "pd.crosstab(data_df['Mitoses'], data_df['Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df['Marginal Adhesion'] = np.where(data_df['Marginal Adhesion'] >4, 4, data_df['Marginal Adhesion'])\n",
    "data_df['Single Epithelial Cell Size'] = np.where(data_df['Single Epithelial Cell Size'] >6, 6, data_df['Single Epithelial Cell Size'])\n",
    "data_df['Normal Nucleoli'] = np.where(data_df['Normal Nucleoli'] >6, 6, data_df['Normal Nucleoli'])\n",
    "data_df['Mitoses'] = np.where(data_df['Mitoses'] >3, 3, data_df['Mitoses'])\n",
    "data_df['Bland Chromatin'] = np.where(data_df['Bland Chromatin'] >5, 5, data_df['Bland Chromatin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "Class                2    4\n",
       "Marginal Adhesion          \n",
       "1                  375   32\n",
       "2                   37   21\n",
       "3                   31   27\n",
       "4                   15  161"
      ]
     },
     "execution_count": 35,
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    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Class</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>363</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Class                          2   4\n",
       "Single Epithelial Cell Size         \n",
       "1                             46   1\n",
       "2                            363  23\n",
       "3                             29  43\n",
       "4                              7  41\n",
       "5                              5  34\n",
       "6                              8  99"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "      <th>Class</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150</td>\n",
       "      <td>2</td>\n",
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       "      <th>2</th>\n",
       "      <td>159</td>\n",
       "      <td>7</td>\n",
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       "      <th>3</th>\n",
       "      <td>129</td>\n",
       "      <td>36</td>\n",
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       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
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       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Class              2    4\n",
       "Normal Nucleoli          \n",
       "1                150    2\n",
       "2                159    7\n",
       "3                129   36\n",
       "4                  8   32\n",
       "5                  4   30\n",
       "6                  8  134"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>402</td>\n",
       "      <td>41</td>\n",
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       "      <th>2</th>\n",
       "      <td>30</td>\n",
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       "      <th>3</th>\n",
       "      <td>12</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13</td>\n",
       "      <td>145</td>\n",
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      ],
      "text/plain": [
       "Class              2    4\n",
       "Bland Chromatin          \n",
       "1                402   41\n",
       "2                 30    6\n",
       "3                 12   32\n",
       "4                  1   17\n",
       "5                 13  145"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mitoses</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>445</td>\n",
       "      <td>134</td>\n",
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       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>27</td>\n",
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       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>80</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "Class      2    4\n",
       "Mitoses          \n",
       "1        445  134\n",
       "2          8   27\n",
       "3          5   80"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(data_df['Marginal Adhesion'], data_df['Class'])\n",
    "pd.crosstab(data_df['Single Epithelial Cell Size'], data_df['Class'])\n",
    "pd.crosstab(data_df['Normal Nucleoli'], data_df['Class'])\n",
    "pd.crosstab(data_df['Bland Chromatin'], data_df['Class'])\n",
    "pd.crosstab(data_df['Mitoses'], data_df['Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JD\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:625: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\JD\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:462: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scale = StandardScaler()\n",
    "data_df_scale = scale.fit_transform(data_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df_scale = pd.DataFrame(data_df_scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df_scale.columns = ['ClumpThickness', 'Cell Size', 'Cell Shape', 'Marginal Adhesion',\n",
    "       'Single Epithelial Cell Size', 'Bare Nuclei', 'Normal Nucleoli',\n",
    "       'Bland Chromatin', 'Mitoses', 'Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>0</th>\n",
       "      <td>0.206936</td>\n",
       "      <td>-0.699995</td>\n",
       "      <td>-0.743299</td>\n",
       "      <td>-0.776593</td>\n",
       "      <td>-0.603991</td>\n",
       "      <td>-0.703348</td>\n",
       "      <td>-0.051352</td>\n",
       "      <td>-0.691425</td>\n",
       "      <td>-0.436962</td>\n",
       "      <td>-0.725397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.206936</td>\n",
       "      <td>0.283845</td>\n",
       "      <td>0.266875</td>\n",
       "      <td>1.543230</td>\n",
       "      <td>1.931684</td>\n",
       "      <td>1.796516</td>\n",
       "      <td>-0.051352</td>\n",
       "      <td>-0.094751</td>\n",
       "      <td>-0.436962</td>\n",
       "      <td>-0.725397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ClumpThickness  Cell Size  Cell Shape  Marginal Adhesion  \\\n",
       "0        0.206936  -0.699995   -0.743299          -0.776593   \n",
       "1        0.206936   0.283845    0.266875           1.543230   \n",
       "\n",
       "   Single Epithelial Cell Size  Bare Nuclei  Normal Nucleoli  Bland Chromatin  \\\n",
       "0                    -0.603991    -0.703348        -0.051352        -0.691425   \n",
       "1                     1.931684     1.796516        -0.051352        -0.094751   \n",
       "\n",
       "    Mitoses     Class  \n",
       "0 -0.436962 -0.725397  \n",
       "1 -0.436962 -0.725397  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df_scale.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2495bbd0908>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_df_scale.boxplot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lmMHMAtkWCzm"
   },
   "source": [
    "# Q6 Create a covariance matrix for identifying Principal components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.00143266, 0.64583645, 0.65552689, 0.55151351, 0.56413691,\n",
       "        0.58927575, 0.57835893, 0.56679455, 0.42662567, 0.71702715],\n",
       "       [0.64583645, 1.00143266, 0.90818117, 0.71521785, 0.79059498,\n",
       "        0.68840199, 0.73714735, 0.73114712, 0.51272252, 0.81907552],\n",
       "       [0.65552689, 0.90818117, 1.00143266, 0.69734899, 0.76233939,\n",
       "        0.7108287 , 0.72206434, 0.72305564, 0.48335106, 0.820107  ],\n",
       "       [0.55151351, 0.71521785, 0.69734899, 1.00143266, 0.66305424,\n",
       "        0.69002674, 0.67025804, 0.65607922, 0.42440609, 0.73646839],\n",
       "       [0.56413691, 0.79059498, 0.76233939, 0.66305424, 1.00143266,\n",
       "        0.64744645, 0.6667727 , 0.70621971, 0.49887609, 0.74975332],\n",
       "       [0.58927575, 0.68840199, 0.7108287 , 0.69002674, 0.64744645,\n",
       "        1.00143266, 0.70235998, 0.63656181, 0.45054978, 0.81861341],\n",
       "       [0.57835893, 0.73714735, 0.72206434, 0.67025804, 0.6667727 ,\n",
       "        0.70235998, 1.00143266, 0.68935702, 0.41450493, 0.77924187],\n",
       "       [0.56679455, 0.73114712, 0.72305564, 0.65607922, 0.70621971,\n",
       "        0.63656181, 0.68935702, 1.00143266, 0.492196  , 0.75849326],\n",
       "       [0.42662567, 0.51272252, 0.48335106, 0.42440609, 0.49887609,\n",
       "        0.45054978, 0.41450493, 0.492196  , 1.00143266, 0.5224007 ],\n",
       "       [0.71702715, 0.81907552, 0.820107  , 0.73646839, 0.74975332,\n",
       "        0.81861341, 0.77924187, 0.75849326, 0.5224007 , 1.00143266]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov_mat = np.cov(data_df_scale.T)\n",
    "cov_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PCA\n",
    "# Step 1 - Create covariance matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vbZ-YcsdWCzz"
   },
   "source": [
    "# Q7 Identify eigen values and eigen vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2- Get eigen values and eigen vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.28539859,  0.00392257,  0.8842458 , -0.02335878, -0.1649636 ,\n",
       "         0.16596341,  0.16569051, -0.01435903,  0.23103677, -0.01736735],\n",
       "       [ 0.34594868,  0.04103236, -0.0984155 , -0.73533202, -0.02518996,\n",
       "         0.35414668, -0.29148771, -0.19257409, -0.21488685, -0.18427273],\n",
       "       [ 0.34325137,  0.08441032, -0.03504917,  0.65860223, -0.15548252,\n",
       "         0.32706856, -0.43805061, -0.1030808 , -0.27600562, -0.17385713],\n",
       "       [ 0.31087398,  0.16754601, -0.20242256,  0.04401783, -0.00706605,\n",
       "        -0.35410655,  0.02133503, -0.2420733 ,  0.56845749, -0.56985373],\n",
       "       [ 0.32219895, -0.02914027, -0.28356776,  0.07095084, -0.0308393 ,\n",
       "         0.3715537 ,  0.72517157,  0.32168812, -0.1070741 , -0.17340373],\n",
       "       [ 0.31690231,  0.13821791,  0.06123281, -0.10721265, -0.35472353,\n",
       "        -0.5995597 , -0.08289933,  0.48269183, -0.37368618, -0.02047067],\n",
       "       [ 0.31873963,  0.21364349, -0.09829259,  0.04581841, -0.10804306,\n",
       "        -0.20259047,  0.30336115, -0.63348609, -0.16621673,  0.51703378],\n",
       "       [ 0.31768216, -0.02061782, -0.23861553, -0.0253791 , -0.12886911,\n",
       "         0.1623854 , -0.2576711 ,  0.33968252,  0.55740208,  0.55282866],\n",
       "       [ 0.23001762, -0.94451226, -0.02994223,  0.03160085, -0.02301365,\n",
       "        -0.17628591, -0.01227634, -0.13980602, -0.04082966, -0.01111287],\n",
       "       [ 0.35318376,  0.07056065,  0.12068529,  0.05268882,  0.89013354,\n",
       "        -0.13659559, -0.06260069,  0.14413426, -0.10012127,  0.08501473]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([6.97264418, 0.68632302, 0.50338488, 0.08838639, 0.12617549,\n",
       "       0.43412687, 0.25236119, 0.28302498, 0.32527776, 0.34262188])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eign_val, eign_vect = np.linalg.eig(cov_mat)\n",
    "\n",
    "eign_vect\n",
    "print ('-'*100)\n",
    "eign_val"
   ]
  },
  {
   "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": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = sum(eign_val)\n",
    "var_exp = [( i /total ) * 100 for i in sorted(eign_val, reverse=True)]\n",
    "cum_var_exp = np.cumsum(var_exp)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "total = sum(eign_val)\n",
    "for i in sorted(eign_val):\n",
    "    var_exp = (i/total)*100\n",
    "    print (var_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 69.62669014,  76.48010171,  81.50674898,  85.84180697,\n",
       "        89.26312414,  92.51124831,  95.33744912,  97.85745069,\n",
       "        99.11740054, 100.        ])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cum_var = np.cumsum(var_exp)\n",
    "cum_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2495baaf518>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(var_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2495bae93c8>]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2495bb11358>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ploting \n",
    "plt.figure(figsize=(10 , 5))\n",
    "plt.bar(range(1, eign_val.size + 1), var_exp, alpha = 0.5, align = 'center', label = 'explained variance')\n",
    "plt.step(range(1, eign_val.size + 1), cum_var, where='mid', label = 'Cumulative variance')\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": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.68989073e+00,  1.51714105e+00, -1.80472879e+00, ...,\n",
       "         4.22554222e+00,  2.80784073e+00,  3.14172197e+00],\n",
       "       [-6.44336175e-02, -8.38082814e-01, -1.00037226e-01, ...,\n",
       "         2.45182416e-01, -1.24344837e+00, -1.33251577e+00],\n",
       "       [ 6.58900006e-01, -6.51253210e-01,  4.73851179e-02, ...,\n",
       "        -1.26145151e+00, -1.01958658e+00, -1.06356547e+00],\n",
       "       [ 8.98559112e-02, -5.12576132e-01, -1.94646400e-01, ...,\n",
       "         1.68438718e+00,  6.82860506e-02,  2.60658506e-01],\n",
       "       [ 3.50697239e-01, -1.48919467e+00,  3.57355983e-01, ...,\n",
       "         2.99251474e-01,  5.60444688e-01, -2.10762134e-03],\n",
       "       [-3.02953729e-03,  4.15596529e-02,  2.64987772e-01, ...,\n",
       "         2.88410573e-01, -7.42413202e-01, -5.22777535e-02]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.28539859,  0.34594868,  0.34325137,  0.31087398,  0.32219895,\n",
       "         0.31690231,  0.31873963,  0.31768216,  0.23001762,  0.35318376],\n",
       "       [-0.00392257, -0.04103236, -0.08441032, -0.16754601,  0.02914027,\n",
       "        -0.13821791, -0.21364349,  0.02061782,  0.94451226, -0.07056065],\n",
       "       [ 0.8842458 , -0.0984155 , -0.03504917, -0.20242256, -0.28356776,\n",
       "         0.06123281, -0.09829259, -0.23861553, -0.02994223,  0.12068529],\n",
       "       [ 0.16596341,  0.35414668,  0.32706856, -0.35410655,  0.3715537 ,\n",
       "        -0.5995597 , -0.20259047,  0.1623854 , -0.17628591, -0.13659559],\n",
       "       [-0.01736735, -0.18427273, -0.17385713, -0.56985373, -0.17340373,\n",
       "        -0.02047067,  0.51703378,  0.55282866, -0.01111287,  0.08501473],\n",
       "       [-0.23103677,  0.21488685,  0.27600562, -0.56845749,  0.1070741 ,\n",
       "         0.37368618,  0.16621673, -0.55740208,  0.04082966,  0.10012127]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using scikit learn PCA here. It does all the above steps and maps data to PCA dimensions in one shot\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# NOTE - we are generating only 7 PCA dimensions (dimensionality reduction from 9 to 7)\n",
    "\n",
    "pca = PCA(n_components=6)\n",
    "data_reduced = pca.fit_transform(data_df_scale)\n",
    "data_reduced.transpose()\n",
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8L-IGI0QWC0t"
   },
   "source": [
    "# Q10 Find correlation between components and features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>0</th>\n",
       "      <td>0.285399</td>\n",
       "      <td>0.345949</td>\n",
       "      <td>0.343251</td>\n",
       "      <td>0.310874</td>\n",
       "      <td>0.322199</td>\n",
       "      <td>0.316902</td>\n",
       "      <td>0.318740</td>\n",
       "      <td>0.317682</td>\n",
       "      <td>0.230018</td>\n",
       "      <td>0.353184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.003923</td>\n",
       "      <td>-0.041032</td>\n",
       "      <td>-0.084410</td>\n",
       "      <td>-0.167546</td>\n",
       "      <td>0.029140</td>\n",
       "      <td>-0.138218</td>\n",
       "      <td>-0.213643</td>\n",
       "      <td>0.020618</td>\n",
       "      <td>0.944512</td>\n",
       "      <td>-0.070561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.884246</td>\n",
       "      <td>-0.098416</td>\n",
       "      <td>-0.035049</td>\n",
       "      <td>-0.202423</td>\n",
       "      <td>-0.283568</td>\n",
       "      <td>0.061233</td>\n",
       "      <td>-0.098293</td>\n",
       "      <td>-0.238616</td>\n",
       "      <td>-0.029942</td>\n",
       "      <td>0.120685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.165963</td>\n",
       "      <td>0.354147</td>\n",
       "      <td>0.327069</td>\n",
       "      <td>-0.354107</td>\n",
       "      <td>0.371554</td>\n",
       "      <td>-0.599560</td>\n",
       "      <td>-0.202590</td>\n",
       "      <td>0.162385</td>\n",
       "      <td>-0.176286</td>\n",
       "      <td>-0.136596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.017367</td>\n",
       "      <td>-0.184273</td>\n",
       "      <td>-0.173857</td>\n",
       "      <td>-0.569854</td>\n",
       "      <td>-0.173404</td>\n",
       "      <td>-0.020471</td>\n",
       "      <td>0.517034</td>\n",
       "      <td>0.552829</td>\n",
       "      <td>-0.011113</td>\n",
       "      <td>0.085015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ClumpThickness  Cell Size  Cell Shape  Marginal Adhesion  \\\n",
       "0        0.285399   0.345949    0.343251           0.310874   \n",
       "1       -0.003923  -0.041032   -0.084410          -0.167546   \n",
       "2        0.884246  -0.098416   -0.035049          -0.202423   \n",
       "3        0.165963   0.354147    0.327069          -0.354107   \n",
       "4       -0.017367  -0.184273   -0.173857          -0.569854   \n",
       "\n",
       "   Single Epithelial Cell Size  Bare Nuclei  Normal Nucleoli  Bland Chromatin  \\\n",
       "0                     0.322199     0.316902         0.318740         0.317682   \n",
       "1                     0.029140    -0.138218        -0.213643         0.020618   \n",
       "2                    -0.283568     0.061233        -0.098293        -0.238616   \n",
       "3                     0.371554    -0.599560        -0.202590         0.162385   \n",
       "4                    -0.173404    -0.020471         0.517034         0.552829   \n",
       "\n",
       "    Mitoses     Class  \n",
       "0  0.230018  0.353184  \n",
       "1  0.944512 -0.070561  \n",
       "2 -0.029942  0.120685  \n",
       "3 -0.176286 -0.136596  \n",
       "4 -0.011113  0.085015  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_com = pd.DataFrame(pca.components_,columns=list(data_df_scale))\n",
    "df_com.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bXUp9TCDWC1G"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 864x432 with 0 Axes>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2495c129390>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.heatmap(df_com,cmap='plasma',)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Content Based Recommendation System - Optional ( Q11 - Q19 will not be graded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q11 Read the Dataset `movies_metadata.csv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q12 Create a new column with name 'description' combining `'overview' and 'tagline'` columns in the given dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q13  Lets drop the null values in `description` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q14 Keep the first occurance and drop duplicates of each title in column `title`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q15   As we might have dropped a few rows with duplicate `title` in above step, just reset the index [make sure you are not adding any new column to the dataframe while doing reset index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q16    Generate tf-idf matrix using the column `description`. Consider till 3-grams, with minimum document frequency as 0.\n",
    "\n",
    "Hint:\n",
    "tf = TfidfVectorizer(analyzer='word', ngram_range=(1, 3), min_df=0, stop_words='english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q17  Create cosine similarity matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q18  Write a function with name `recommend` which takes `title` as argument and returns a list of 10 recommended title names in the output based on the above cosine similarities\n",
    "\n",
    "Hint:\n",
    "\n",
    "titles = df['title'] <br>\n",
    "indices = pd.Series(df.index, index=df['title']) <br>\n",
    "\n",
    "def recommend(title): <br>\n",
    "    idx = indices[title] <br>\n",
    "    sim_scores = list(enumerate(cosine_similarities[idx])) <br>\n",
    "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True) <br>\n",
    "    sim_scores = sim_scores[1:31] <br>\n",
    "    movie_indices = [i[0] for i in sim_scores] <br>\n",
    "    return titles.iloc[movie_indices] <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q19 Give the recommendations from above functions for movies `The Godfather` and `The Dark Knight Rises`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Popularity Based Recommendation System"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### About Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anonymous Ratings on jokes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "source": [
    "# Q20 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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q21 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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q22 Change the column indices from 0 to 99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q23 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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q24 Normalize the ratings using StandardScaler and save them in `ratings_diff` variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Popularity based recommendation system"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q25  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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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