1. Statistical Learning/R2_Internal_Lab_Questions (1).ipynb

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    "colab_type": "code",
    "id": "9cigSdo6oRIX",
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   },
   "outputs": [],
   "source": [
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MBpNtcUSoRIa"
   },
   "source": [
    "## K-Nearest-Neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "em-qAehKoRIb"
   },
   "source": [
    "KNN falls in the supervised learning family of algorithms. Informally, this means that we are given a labelled dataset consiting of training observations (x,y) and would like to capture the relationship between x and y. More formally, our goal is to learn a function h:X→Y so that given an unseen observation x, h(x) can confidently predict the corresponding output y.\n",
    "\n",
    "In this module we will explore the inner workings of KNN, choosing the optimal K values and using KNN from scikit-learn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DT7IQDlboRIc"
   },
   "source": [
    "## Overview\n",
    "\n",
    "1.Read the problem statement.\n",
    "\n",
    "2.Get the dataset.\n",
    "\n",
    "3.Explore the dataset.\n",
    "\n",
    "4.Pre-processing of dataset.\n",
    "\n",
    "5.Visualization\n",
    "\n",
    "6.Transform the dataset for building machine learning model.\n",
    "\n",
    "7.Split data into train, test set.\n",
    "\n",
    "7.Build Model.\n",
    "\n",
    "8.Apply the model.\n",
    "\n",
    "9.Evaluate the model.\n",
    "\n",
    "10.Finding Optimal K value\n",
    "\n",
    "11.Repeat 7,8,9 steps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8BsgPUvKoRIc"
   },
   "source": [
    "## Problem statement\n",
    "\n",
    "### Dataset\n",
    "\n",
    "The data set we’ll be using is the Iris Flower Dataset which was first introduced in 1936 by the famous statistician Ronald Fisher and consists of 50 observations from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals.\n",
    "\n",
    "**Attributes of the dataset:** https://archive.ics.uci.edu/ml/datasets/Iris\n",
    "\n",
    "**Train the KNN algorithm to be able to distinguish the species from one another given the measurements of the 4 features.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MYqTlwmboRId"
   },
   "source": [
    "## Question 1\n",
    "\n",
    "Import the data set and print 10 random rows from the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "igR1np_YoRIe"
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm          Species\n",
       "Id                                                                            \n",
       "118            7.7           3.8            6.7           2.2   Iris-virginica\n",
       "32             5.4           3.4            1.5           0.4      Iris-setosa\n",
       "9              4.4           2.9            1.4           0.2      Iris-setosa\n",
       "29             5.2           3.4            1.4           0.2      Iris-setosa\n",
       "88             6.3           2.3            4.4           1.3  Iris-versicolor\n",
       "40             5.1           3.4            1.5           0.2      Iris-setosa\n",
       "42             4.5           2.3            1.3           0.3      Iris-setosa\n",
       "39             4.4           3.0            1.3           0.2      Iris-setosa\n",
       "4              4.6           3.1            1.5           0.2      Iris-setosa\n",
       "65             5.6           2.9            3.6           1.3  Iris-versicolor"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "\n",
    "iris=pd.read_csv(\"iris.csv\",index_col=0)\n",
    "iris.sample(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(152, 5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#iris=pd.read_csv(\"iris.csv\",skiprows=random.sample(range(1,100),95))\n",
    "#iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5SuXqTKmoRIg"
   },
   "source": [
    "## Data Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J8VyfAlVoRIg"
   },
   "source": [
    "## Question 2 - Estimating missing values\n",
    "\n",
    "*Its not good to remove the records having missing values all the time. We may end up loosing some data points. So, we will have to see how to replace those missing values with some estimated values (median) *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iris.isna    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts('iris.isna')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8a8HDPxIoRIh"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SepalLengthCm    1\n",
       "SepalWidthCm     2\n",
       "PetalLengthCm    2\n",
       "PetalWidthCm     1\n",
       "Species          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1st method df.fillna(df.mean())\n",
    "iris['SepalLengthCm'].fillna(iris['SepalLengthCm'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "iris['SepalWidthCm'].fillna(iris['SepalWidthCm'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris['PetalLengthCm'].fillna(iris['PetalLengthCm'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris['PetalWidthCm'].fillna(iris['PetalWidthCm'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.70</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.90</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.70</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.80</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.90</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.60</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.80</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.10</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.40</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.60</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.10</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.60</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.10</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.60</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.80</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.40</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.60</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.10</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.10</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.90</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.70</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.20</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.20</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.40</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.10</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.35</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>5.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.35</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>152 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm         Species\n",
       "Id                                                                           \n",
       "1              5.1           3.5           1.40           0.2     Iris-setosa\n",
       "2              4.9           3.0           1.40           0.2     Iris-setosa\n",
       "3              4.7           3.2           1.30           0.2     Iris-setosa\n",
       "4              4.6           3.1           1.50           0.2     Iris-setosa\n",
       "5              5.0           3.6           1.40           0.2     Iris-setosa\n",
       "6              5.4           3.9           1.70           0.4     Iris-setosa\n",
       "7              4.6           3.4           1.40           0.3     Iris-setosa\n",
       "8              5.0           3.4           1.50           0.2     Iris-setosa\n",
       "9              4.4           2.9           1.40           0.2     Iris-setosa\n",
       "10             4.9           3.1           1.50           0.1     Iris-setosa\n",
       "11             5.4           3.7           1.50           0.2     Iris-setosa\n",
       "12             4.8           3.4           1.60           0.2     Iris-setosa\n",
       "13             4.8           3.0           1.40           0.1     Iris-setosa\n",
       "14             4.3           3.0           1.10           0.1     Iris-setosa\n",
       "15             5.8           4.0           1.20           0.2     Iris-setosa\n",
       "16             5.7           4.4           1.50           0.4     Iris-setosa\n",
       "17             5.4           3.9           1.30           0.4     Iris-setosa\n",
       "18             5.1           3.5           1.40           0.3     Iris-setosa\n",
       "19             5.7           3.8           1.70           0.3     Iris-setosa\n",
       "20             5.1           3.8           1.50           0.3     Iris-setosa\n",
       "21             5.4           3.4           1.70           0.2     Iris-setosa\n",
       "22             5.1           3.7           1.50           0.4     Iris-setosa\n",
       "23             4.6           3.6           1.00           0.2     Iris-setosa\n",
       "24             5.1           3.3           1.70           0.5     Iris-setosa\n",
       "25             4.8           3.4           1.90           0.2     Iris-setosa\n",
       "26             5.0           3.0           1.60           0.2     Iris-setosa\n",
       "27             5.0           3.4           1.60           0.4     Iris-setosa\n",
       "28             5.2           3.5           1.50           0.2     Iris-setosa\n",
       "29             5.2           3.4           1.40           0.2     Iris-setosa\n",
       "30             4.7           3.2           1.60           0.2     Iris-setosa\n",
       "..             ...           ...            ...           ...             ...\n",
       "123            7.7           2.8           6.70           2.0  Iris-virginica\n",
       "124            6.3           2.7           4.90           1.8  Iris-virginica\n",
       "125            6.7           3.3           5.70           2.1  Iris-virginica\n",
       "126            7.2           3.2           6.00           1.8  Iris-virginica\n",
       "127            6.2           2.8           4.80           1.8  Iris-virginica\n",
       "128            6.1           3.0           4.90           1.8  Iris-virginica\n",
       "129            6.4           2.8           5.60           2.1  Iris-virginica\n",
       "130            7.2           3.0           5.80           1.6  Iris-virginica\n",
       "131            7.4           2.8           6.10           1.9  Iris-virginica\n",
       "132            7.9           3.8           6.40           2.0  Iris-virginica\n",
       "133            6.4           2.8           5.60           2.2  Iris-virginica\n",
       "134            6.3           2.8           5.10           1.5  Iris-virginica\n",
       "135            6.1           2.6           5.60           1.4  Iris-virginica\n",
       "136            7.7           3.0           6.10           2.3  Iris-virginica\n",
       "137            6.3           3.4           5.60           2.4  Iris-virginica\n",
       "138            6.4           3.1           5.50           1.8  Iris-virginica\n",
       "139            6.0           3.0           4.80           1.8  Iris-virginica\n",
       "140            6.9           3.1           5.40           2.1  Iris-virginica\n",
       "141            6.7           3.1           5.60           2.4  Iris-virginica\n",
       "142            6.9           3.1           5.10           2.3  Iris-virginica\n",
       "143            5.8           2.7           5.10           1.9  Iris-virginica\n",
       "144            6.8           3.2           5.90           2.3  Iris-virginica\n",
       "145            6.7           3.3           5.70           2.5  Iris-virginica\n",
       "146            6.7           3.0           5.20           2.3  Iris-virginica\n",
       "147            6.3           2.5           5.00           1.9  Iris-virginica\n",
       "148            6.5           3.0           5.20           2.0  Iris-virginica\n",
       "149            6.2           3.4           5.40           2.3  Iris-virginica\n",
       "150            5.9           3.0           5.10           1.8  Iris-virginica\n",
       "151            5.9           3.0           4.35           1.3  Iris-virginica\n",
       "152            5.8           3.0           4.35           1.8  Iris-virginica\n",
       "\n",
       "[152 rows x 5 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2nd method iris.fillna(iris.median())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kvEFXK8aoRIj"
   },
   "source": [
    "## Question 3 - Dealing with categorical data\n",
    "\n",
    "Change all the classes to numericals (0to2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.70</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.90</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.70</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.80</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.90</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.60</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.80</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.10</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.40</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.60</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.10</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.60</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.10</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.60</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.80</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.40</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.60</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.10</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.10</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.90</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.70</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.20</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.20</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.40</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.10</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.35</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>5.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.35</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>152 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm Species\n",
       "Id                                                                   \n",
       "1              5.1           3.5           1.40           0.2       0\n",
       "2              4.9           3.0           1.40           0.2       0\n",
       "3              4.7           3.2           1.30           0.2       0\n",
       "4              4.6           3.1           1.50           0.2       0\n",
       "5              5.0           3.6           1.40           0.2       0\n",
       "6              5.4           3.9           1.70           0.4       0\n",
       "7              4.6           3.4           1.40           0.3       0\n",
       "8              5.0           3.4           1.50           0.2       0\n",
       "9              4.4           2.9           1.40           0.2       0\n",
       "10             4.9           3.1           1.50           0.1       0\n",
       "11             5.4           3.7           1.50           0.2       0\n",
       "12             4.8           3.4           1.60           0.2       0\n",
       "13             4.8           3.0           1.40           0.1       0\n",
       "14             4.3           3.0           1.10           0.1       0\n",
       "15             5.8           4.0           1.20           0.2       0\n",
       "16             5.7           4.4           1.50           0.4       0\n",
       "17             5.4           3.9           1.30           0.4       0\n",
       "18             5.1           3.5           1.40           0.3       0\n",
       "19             5.7           3.8           1.70           0.3       0\n",
       "20             5.1           3.8           1.50           0.3       0\n",
       "21             5.4           3.4           1.70           0.2       0\n",
       "22             5.1           3.7           1.50           0.4       0\n",
       "23             4.6           3.6           1.00           0.2       0\n",
       "24             5.1           3.3           1.70           0.5       0\n",
       "25             4.8           3.4           1.90           0.2       0\n",
       "26             5.0           3.0           1.60           0.2       0\n",
       "27             5.0           3.4           1.60           0.4       0\n",
       "28             5.2           3.5           1.50           0.2       0\n",
       "29             5.2           3.4           1.40           0.2       0\n",
       "30             4.7           3.2           1.60           0.2       0\n",
       "..             ...           ...            ...           ...     ...\n",
       "123            7.7           2.8           6.70           2.0       2\n",
       "124            6.3           2.7           4.90           1.8       2\n",
       "125            6.7           3.3           5.70           2.1       2\n",
       "126            7.2           3.2           6.00           1.8       2\n",
       "127            6.2           2.8           4.80           1.8       2\n",
       "128            6.1           3.0           4.90           1.8       2\n",
       "129            6.4           2.8           5.60           2.1       2\n",
       "130            7.2           3.0           5.80           1.6       2\n",
       "131            7.4           2.8           6.10           1.9       2\n",
       "132            7.9           3.8           6.40           2.0       2\n",
       "133            6.4           2.8           5.60           2.2       2\n",
       "134            6.3           2.8           5.10           1.5       2\n",
       "135            6.1           2.6           5.60           1.4       2\n",
       "136            7.7           3.0           6.10           2.3       2\n",
       "137            6.3           3.4           5.60           2.4       2\n",
       "138            6.4           3.1           5.50           1.8       2\n",
       "139            6.0           3.0           4.80           1.8       2\n",
       "140            6.9           3.1           5.40           2.1       2\n",
       "141            6.7           3.1           5.60           2.4       2\n",
       "142            6.9           3.1           5.10           2.3       2\n",
       "143            5.8           2.7           5.10           1.9       2\n",
       "144            6.8           3.2           5.90           2.3       2\n",
       "145            6.7           3.3           5.70           2.5       2\n",
       "146            6.7           3.0           5.20           2.3       2\n",
       "147            6.3           2.5           5.00           1.9       2\n",
       "148            6.5           3.0           5.20           2.0       2\n",
       "149            6.2           3.4           5.40           2.3       2\n",
       "150            5.9           3.0           5.10           1.8       2\n",
       "151            5.9           3.0           4.35           1.3       2\n",
       "152            5.8           3.0           4.35           1.8       2\n",
       "\n",
       "[152 rows x 5 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#iris['Species']\n",
    "\n",
    "iris.Species[iris.Species=='Iris-setosa'] = 0\n",
    "iris.Species[iris.Species=='Iris-versicolor'] = 1\n",
    "iris.Species[iris.Species=='Iris-virginica'] = 2\n",
    "\n",
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#another way ------> iris['Species'].replace({\"Iris-setosa\":0,\"Iris-versicolor\":1,\"Iris-virginica\":2},inplace=True)\n",
    "#iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CEdhV6q-oRIj"
   },
   "source": [
    "## Question 4\n",
    "\n",
    "*Observe the association of each independent variable with target variable and drop variables from feature set having correlation in range -0.1 to 0.1 with target variable.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Izq1HCgloRIk"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['Species'] = iris['Species'].astype('int64')\n",
    "iris['Species'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>0.871120</td>\n",
       "      <td>0.815986</td>\n",
       "      <td>0.775061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <td>-0.109369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.420713</td>\n",
       "      <td>-0.356510</td>\n",
       "      <td>-0.417318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <td>0.871120</td>\n",
       "      <td>-0.420713</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962043</td>\n",
       "      <td>0.944477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <td>0.815986</td>\n",
       "      <td>-0.356510</td>\n",
       "      <td>0.962043</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.952513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <td>0.775061</td>\n",
       "      <td>-0.417318</td>\n",
       "      <td>0.944477</td>\n",
       "      <td>0.952513</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "SepalLengthCm       1.000000     -0.109369       0.871120      0.815986   \n",
       "SepalWidthCm       -0.109369      1.000000      -0.420713     -0.356510   \n",
       "PetalLengthCm       0.871120     -0.420713       1.000000      0.962043   \n",
       "PetalWidthCm        0.815986     -0.356510       0.962043      1.000000   \n",
       "Species             0.775061     -0.417318       0.944477      0.952513   \n",
       "\n",
       "                Species  \n",
       "SepalLengthCm  0.775061  \n",
       "SepalWidthCm  -0.417318  \n",
       "PetalLengthCm  0.944477  \n",
       "PetalWidthCm   0.952513  \n",
       "Species        1.000000  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "association=iris.corr()\n",
    "association\n",
    "# as no variables from feature set is having correlation in range -0.1 to 0.1 with target variable so we dont need to drop."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KJfDNNiJoRIl"
   },
   "source": [
    "## Question 5\n",
    "\n",
    "*Observe the independent variables variance and drop such variables having no variance or almost zero variance(variance < 0.1). They will be having almost no influence on the classification.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "c8fYDazvoRIm"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SepalLengthCm    0.676645\n",
       "SepalWidthCm     0.185552\n",
       "PetalLengthCm    3.076516\n",
       "PetalWidthCm     0.577141\n",
       "Species          0.675322\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.var()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "79S0_e9goRIo"
   },
   "source": [
    "## Question 6\n",
    "\n",
    "*Plot the scatter matrix for all the variables.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yqtNPRDWoRIo"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1a6ab13d908>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 945.725x900 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"ticks\")\n",
    "sns.pairplot(iris,hue=\"Species\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Zj8ZZmlOoRIq"
   },
   "source": [
    "## Split the dataset into training and test sets\n",
    "\n",
    "## Question 7\n",
    "\n",
    "*Split the dataset into training and test sets with 80-20 ratio.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jaJH_RdDoRIr"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=iris.drop(['Species'],axis=1)\n",
    "y=iris['Species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "xTrain, xTest, yTrain, yTest = train_test_split(x, y, test_size = 0.2, random_state = 143)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KpjOe5ZCoRIt"
   },
   "source": [
    "## Question 8 - Model\n",
    "\n",
    "*Build the model and train and test on training and test sets respectively using **scikit-learn**. Print the Accuracy of the model with different values of **k=3,5,9**.*\n",
    "\n",
    "**Hint:** For accuracy you can check **accuracy_score()** in scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier #importing knn function from neighbours from sklearn\n",
    "from scipy.stats import zscore   #standardisation\n",
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QYULK2n_oRIt"
   },
   "outputs": [],
   "source": [
    "model1= KNeighborsClassifier(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=model1.fit(xTrain,yTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(xTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9032258064516129"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(yTest,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9354838709677419"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1= KNeighborsClassifier(n_neighbors=5)\n",
    "model=model1.fit(xTrain,yTrain)\n",
    "y_pred=model.predict(xTest)\n",
    "accuracy_score(yTest,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9354838709677419"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1= KNeighborsClassifier(n_neighbors=9)\n",
    "model=model1.fit(xTrain,yTrain)\n",
    "y_pred=model.predict(xTest)\n",
    "accuracy_score(yTest,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CC2jIcvRoRIw"
   },
   "source": [
    "## Question 9 - Finding Optimal value of k.\n",
    "\n",
    "Run the KNN with no of neighbours to be 1,3,5..19 and *Find the **optimal number of neighbours** from the above list using the Miss classification error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uD9tQx20oRIw"
   },
   "source": [
    "Hint:\n",
    "\n",
    "Misclassification error (MSE) = 1 - Test accuracy score. Calculated MSE for each model with neighbours = 1,3,5...19 and find the model with lowest MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D_q46Mn3oRIx"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=np.arange(1,20)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc=[]\n",
    "for i in a:\n",
    "    model1= KNeighborsClassifier(n_neighbors= i)\n",
    "    model=model1.fit(xTrain,yTrain)\n",
    "    y_pred=model.predict(xTest)\n",
    "    acc.append(accuracy_score(yTest,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9032258064516129,\n",
       " 0.9032258064516129,\n",
       " 0.9032258064516129,\n",
       " 0.9032258064516129,\n",
       " 0.9354838709677419,\n",
       " 0.9354838709677419,\n",
       " 0.9354838709677419,\n",
       " 0.9354838709677419,\n",
       " 0.9354838709677419,\n",
       " 0.967741935483871,\n",
       " 0.9354838709677419,\n",
       " 0.9354838709677419,\n",
       " 0.9354838709677419,\n",
       " 0.9354838709677419,\n",
       " 0.9354838709677419,\n",
       " 0.9354838709677419,\n",
       " 0.9354838709677419,\n",
       " 0.9354838709677419,\n",
       " 0.9354838709677419]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=[]\n",
    "for i in acc:\n",
    "    b.append(1-i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.09677419354838712,\n",
       " 0.09677419354838712,\n",
       " 0.09677419354838712,\n",
       " 0.09677419354838712,\n",
       " 0.06451612903225812,\n",
       " 0.06451612903225812,\n",
       " 0.06451612903225812,\n",
       " 0.06451612903225812,\n",
       " 0.06451612903225812,\n",
       " 0.032258064516129004,\n",
       " 0.06451612903225812,\n",
       " 0.06451612903225812,\n",
       " 0.06451612903225812,\n",
       " 0.06451612903225812,\n",
       " 0.06451612903225812,\n",
       " 0.06451612903225812,\n",
       " 0.06451612903225812,\n",
       " 0.06451612903225812,\n",
       " 0.06451612903225812]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.032258064516129004"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.index(0.032258064516129004)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.967741935483871"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1= KNeighborsClassifier(n_neighbors=10)\n",
    "model=model1.fit(xTrain,yTrain)\n",
    "y_pred=model.predict(xTest)\n",
    "accuracy_score(yTest,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8JriZKaDoRIy"
   },
   "source": [
    "## Question 10\n",
    "\n",
    "*Plot misclassification error vs k (with k value on X-axis) using matplotlib.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qB4RffGwoRIz"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a6ad521588>]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MeBi4yd1nzGwgVz4InAB2uPtz5QtfZPHiQX557jRvsI1Ve9/TM3McPjrBm14dVO09RcIo2SMws03AHcBlwFZgp5kVL4KxB9jl7hcCEeDGXPnfAt9299fnHv95uQIXWarVvV30RbuqvirrkWMTzM5lNFAsdSdMj+BK4EF3Pw5gZvuAq4GP5Z4ngG53fyzX/l7go2b2deB1wNty5V8CflB8cDPrA/qKiocW92uILE68P0qyyolgODdjSFNHpd6EGSMYBEYKno9w9h/qher/DTAMfNrMfgTsA+bbM/Fm4Nmin0dCxi+yJImBGMOpNHNVXAE0mRqnrS3Cpg1aaE7qS5hE0MbZa1dGgLkQ9R3A68n2Jt4IfAv48jzH3w1sKfq5PGT8IkuSCKJMnZrl6IsnqvaeydQ4g+tXsaKzvWrvKRJGmEtDhzj7D3MAHCmqH5inPgWk3f07ufK9wF3FB3f3MeCsdYHNLERYIkuXn7kzPJomWLeqKu85nEqzZXB1Vd5LZDHC9AgeAK4wsw1m1gNcBXw3X+nuSWDKzC7NFV0H3O/uvwQOmdk7cuX/Afhx+UIXWbr8zKFq7dw2fXqWkRcmNT4gdalkInD3w8CtwEPAAWCvuz9hZvvNbFuu2Q7gTjN7Bujl5W/+vwn8kZn9C/BfgfeW+xcQWYqelZ2s7+uu2lITB0fTZDJaWkLqU6j7CNx9L9lLO4Vl2wsePwVcMs/rHHjr8kIUqYxEUL2ZQ/mpqnH1CKQO6c5iaVmJIMbB0QlmZ+dKN16m4VSajvY2BtdXZzxCZDGUCKRlJQaizMzOceTYZMXfK5lKs7m/l/Z2/ZOT+qNPpbSseFC93cq0xpDUMyUCaVlDG3uJRCq/W9mJqdM8/+JJjQ9I3VIikJa1ckUHwbpVFV+FND8zST0CqVdKBNLSqjFzKJ9o1COQeqVEIC0tEcQ4cmyS0zOzFXuP4dQ4K1e0s3FNT8XeQ2Q5lAikpSWCGHNzGQ4dnajYeyRT48SDKG1tkYq9h8hyKBFISzuz1EQFxwmSqTTxfo0PSP1SIpCWNrihl/a2SMU2qXlpYpqx9DSJAY0PSP1SIpCW1tnRxqaNvSRHKtMjyN+joDWGpJ4pEUjLSwSxis0cyt+joFVHpZ4pEUjLiwdRRo+fYGp6puzHTqbSrOruZG1sZdmPLVIuSgTS8vLf1iux1ER2aYkokYhmDEn9UiKQlndmt7IyXx7KZDIkU2ndUSx1T4lAWl7/ulWs6Ggr+xTS4+NTTJ48rfEBqXtKBNLy2tsibA6iZd+28szSEgPqEUh9UyIQAeL90bKPEZzZlaxfPQKpb0oEImTHCV54aYqJE6fKdszkSJq+aBere7vKdkyRSlAiEAESucs35RwnyM8YEql3SgQivLzmULlmDs3NZTg4qhlD0hg6wjQys2uB24BOYLe7311UvxW4B4gBDwM3ufuMmV0P/Bkwmmt6n7vfWq7gRcplQ1833V0dZesRHH3xBFOnZrW0hDSEkj0CM9sE3AFcBmwFdprZxUXN9gC73P1CIALcmCvfBnzA3bfmfpQEpC5FIhHiQfTMbmLL9fKuZLo0JPUvzKWhK4EH3f24u08C+4Cr85VmlgC63f2xXNG9wDW5x28Erjezn5nZHjNbU77QRcorEcR4bmScTCaz7GPl1y7SrmTSCMJcGhoERgqejwCXlKgfKnj8KeCHwJ8AnwF2FB7czPqAvqL3HEKkyhJBlO89nmRsYpo10eWtDTScSrNhTTc9KzvLFJ1I5YRJBG1A4VekCDAXpt7d35UvNLNPAL+c5/g3Ax8OGa9IxZxZamIkvexEkJ0xpPEBaQxhLg0dAgYKngfAkVL1ZrbazP6goDwCzLe8425gS9HP5SHiEimr+EB+t7LlzRyanZ3j4OiExgekYYTpETwAfMTMNgCTwFXAznyluyfNbMrMLnX3R4HrgPuBCeAWM/uhuz8O7AK+WXxwdx8DxgrLzGypv4/IkvX1dhHtWbHsO4yPHJtkZnZO4wPSMEr2CNz9MHAr8BBwANjr7k+Y2X4z25ZrtgO408yeAXqBu9x9Fvgt4HNm9nPgV4FbKvFLiJRDJBIhMbD8NYfyM4Y0dVQaRaj7CNx9L7C3qGx7weOnOHsAOV/+CPCGZcYoUjWJIMaDTx4kk8kseQ+B4dQ4kQhs1hpD0iB0Z7FIgUQQ5eT0DM+PnVzyMZKpNAPrVtHV2V7GyEQqR4lApED8zCY1Sx8nSKbGz6xdJNIIlAhECiSWuebQqdOzHDk2qaWnpaEoEYgU6O1ZwdrYyiWvOXT4+Qnm5jK6h0AaihKBSJFEEF3yvQQv70qmHoE0DiUCkSKJgRgHU2lm5xa/5tBwapyO9giD63srEJlIZSgRiBSJ90c5NTPH6AuTi35tciTN4IZeOjv0T0sahz6tIkWWs1uZ1hiSRqREIFIkfyPYYmcOnZyeYfT4Ca0xJA1HiUCkSHdXB/1rexbdIzg4qqUlpDEpEYjMIxHEFj1zKN+DSGjGkDQYJQKRecSDKIePTnB6Zq5045xkKs2Kjjb6166qYGQi5adEIDKPRBBldi7DkWMToV+THBlncxClvW1pi9WJ1IoSgcg88jOHhkfCjxMkU2nNGJKGpEQgMo9NG3ppa4uEHieYOHGK4+NTmjEkDUmJQGQeKzrbGVy/KnQiSGozGmlgSgQiC4gH0dBTSPMzhrQ9pTQiJQKRBSSCGKkXJpk+PVuybTKVprurgw193VWITKS8lAhEFpAIYmQyL98odj7DqTSJILrk7S1FakmJQGQB8ZCb1GQyGZ4b0a5k0riUCEQWMLh+FR3tbSRLTCEdm5gmfeKUxgekYSkRiCygvb2NoY29JWcO5e81SPSrRyCNqSNMIzO7FrgN6AR2u/vdRfVbgXuAGPAwcJO7zxTUvx54zN27yhW4SDUkghhPP/fCedvkE4V2JZNGVbJHYGabgDuAy4CtwE4zu7io2R5gl7tfCESAGwte3wP8JbCiXEGLVEtiIMrzL57kxNTpBdsMj6aJrVpBX6++50hjCnNp6ErgQXc/7u6TwD7g6nylmSWAbnd/LFd0L3BNwes/DewuT7gi1ZVfMmL4PPcTJEeym9FoxpA0qjCXhgaBkYLnI8AlJeqHAMzs14Eed99nZvMe3Mz6gL6i4qEQcYlUXH4AOJka56JXrD2nPpPJkEyluWLb5mqHJlI2YRJBG1C4i3cEmCtVb2YB2XGFK0sc/2bgwyHiEKm6jWt66FrRvuAdxs+PneTk9IxmDElDC3Np6BAwUPA8AI6EqH8nsA542MwOAJjZATMr/hezG9hS9HP5In4HkYppa4sQ748ueC/BsNYYkiYQpkfwAPARM9sATAJXATvzle6eNLMpM7vU3R8FrgPud/d7yM4kAsDMMu6+tfjg7j4GjBWWLXQZSaQWEkGMJ58ZnbfuzK5k6hFIAyvZI3D3w8CtwEPAAWCvuz9hZvvNbFuu2Q7gTjN7BugF7qpUwCLVlhiIMpae5qWJ6XPqkqk061avpLdHk+KkcYW6j8Dd9wJ7i8q2Fzx+irMHkOc7hqZUSEOK9788c+g1rzx7imgyNU68X70BaWy6s1ikhPxm9MV3GM/OZTiYSmuNIWl4SgQiJayNrWRVd+c59xKMvjDJqZk5jQ9Iw1MiECkhEomQCKLn9Ai0K5k0CyUCkRASQYxkKk0m8/ItM2d2JdMYgTQ4JQKREOJBlMmTpzk+PnWmLJlK07+2h5VdoeZciNQtJQKREPJrDhXuTZBMjZ8pF2lkSgQiIZzZrWw0ezno9Mwch49OnJlRJNLIlAhEQljd20VftOtMj+DIsQlm5zIaKJamoEQgElLhzKEzu5Jp6qg0ASUCkZDiQYzh0TRzcxmSqXHa2iIMbeytdVgiy6ZEIBJSIogyfWqWoy+eIJkaZ3D9Kjo72msdlsiyKRGIhFS4W9lwKq0ZQ9I0lAhEQsrPHPrXgy8y8sKkxgekaSgRiITUs7KTDWu6efSpI2QyENdic9IklAhEFiHeH+XQ0Ykzj0WagRKByCLkxwU62tsYXL+qxtGIlIcSgcgi5O8k3tzfS3u7/vlIc9AnWWQR8ncSa8aQNBMlApFF2NwfZVV3Jxe9Ym2tQxEpG62fK7IIXZ3tfOHWt2npaWkq+jSLLNKq7s5ahyBSVro0JCLS4kL1CMzsWuA2oBPY7e53F9VvBe4BYsDDwE3uPmNmlwO7gRXAs8D17v5iGeMXEZFlKtkjMLNNwB3AZcBWYKeZXVzUbA+wy90vBCLAjbnyLwHXuftrgKeBPyxX4CIiUh5hegRXAg+6+3EAM9sHXA18LPc8AXS7+2O59vcCHwU+B/yKu582s05gE/DT4oObWR/QV1Q8tPhfRUREliJMIhgERgqejwCXlKgfAsglgdcADwCngQ/Nc/ybgQ8vImYRESmjMIPFbUCm4HkEmAtb7+4/c/d+4OPA389z/N3AlqKfy8MELyIiyxemR3CIs/8wB8CRovqB4nozWwm83d3/IVe+B/h08cHdfQwYKywzs3aAVCoVIjwREYGz/mYuasekMIngAeAjZrYBmASuAnbmK909aWZTZnapuz8KXAfcT/ZS0N1mdtDdfwz8FvBPIeMaANixY0f430RERPIGgF+GbVwyEbj7YTO7FXiI7DTQe9z9CTPbD/x3d38S2AF83sxiwP8G7nL3WTN7N/A3uW/4h4EbQsb1I7K9kBFgNuwvU2VDwCNk4zxU41jOR3GWX6PEqjjLr95jbSebBH60mBdFMplM6VZyDjN7Bdl7I7a4+3O1jWZhirP8GiVWxVl+jRTrYujOYhGRFqdEICLS4pQIRERanBLB0o2RvYN6rFTDGlOc5dcosSrO8mukWEPTYLGISItTj0BEpMUpEYiItDjtUBaCmX2Y7J3RAPe5+y3z1L8XyO+18PniPRuqwcweAjaSvasb4H3u/nhB/ZXAXwDdwN+7+201iPEGYFdB0Rbgb919V0Gbmp7P3I2RPwTe6e7PhTlvZhYnu4zKRsCBHe4+UeU4dwK/T3btryfJ/v8/VfSa64E/A0ZzRfe5+62VjHOBWL9Edmn7yVyTj7r7N4teM+8+J9WKE7gY+JOC6k3A4+7+zqLX1OSclpMSQQm5PwK/Brye7D+w75rZu4o+tNuA33b3f65FjABmFgEuBBLz/WMxs27gi8C/Bw4C95nZO9z9/mrG6e73kP3HjZm9GvgH4CNFzWp2Ps3sTcDnyZ7LxZy3zwKfdfevmdntwO3AH1UxzgvJ7vfxq0Ca7HLwvwfcWfTSbcAH3P3vKhVbqVgL4niLu4/M/yogm1hvcPfHzOwLZPc5+Vy14nT3/cD+XF0APAr8wTwvrfo5LTddGiptBPigu59y99PAz4F4UZttwIfM7Kdm9pncgnvVZrn/fs/MnjKzXUX1lwC/cPdnc4liD3BNVSM81+eAD7n7saLyWp7PG8n+Ac0vrFjyvOX223gLsC9XdG9xmyrEOQ28393H3T0D/IxzP6cAbwSuN7OfmdkeM1tT4TjPidXMenKxfTH3//ijZnbW36IF9jmp9jkt9Engr9z9F/PU1eKclpUSQQnu/n/yH0YzexXZS0T78/Vm1gv8hOy3sTeQ3WTn9hqEugb4AfAu4ArgJjN7W0H9gvtG1EKup9Xt7l8vKq/p+XT3G9z9kYKiMOdtPTBe0BOr+LktjtPdk+7+fYDcApG7gG/N89IRskvCv5ZsD+czlYxzvljJrlD8INnLf/+W7Lo9v1v0sqp/XueJEzjz7/6twF0LvLTq57TcdGkopNxljPuAPyz8VpC7Dry9oN2nyV5KqOo1wtxllDOXUnJd6e3A93NFpfaVqLb3kb3ufpZ6OZ8Fwpy34jbM06YqclvL3g98wd3/sbje3d9V0PYTLGKFynJx9/9H9gtLPo6/BN5D9rJMXj19XneSvew3PV9lPZzT5VKPIAQzu5Tst+0/dvcvF9XFzey9BUURXh6srRozu8zMrjhPHPPuG1GN2IqZ2Qqy19z/1zx1dXE+C4Q5b0eB1fl9NHLtq35uzewisgOdX3b3j89Tv9rMCq9xR4CKDr7Ox8xeY2ZXFcVR/P+4bj6vwG8AX5uvol7O6XIpEZRgZpvJDmhe6+7zfRhOAp8wsy25AdvfA745T7tK6wOxGxluAAABW0lEQVQ+aWYrzSwKXF8Ux+OAmdkrc3+wriX7zbEWXgv8q7tPzlNXL+czr+R5y40dPQK8O1f0nuI2lZb7f/494DZ3P2cDqJwJ4JbcoChkLx/V4txGgN1mtiY3vrKzOA53TwJTuS9h8PI+J1VlZuvJXsJ8doEm9XJOl0WJoLT/BqwE/sLMDuR+bjKz/Wa2zd2fJ3uZ49tkpw1GmGcntkpz9++QvXT1E+DHwBfd/Z9z8Q66+xTwO8A3gKeBZ3h5cLPaLqBoLfd6O5955ztvZnaPmf16run7gZ1m9jTZa97Vnpp7A9APfLDgc/qxwjjdfZbsGNfnzOznZGcY3bLwISvD3X8K/CnZWThPAwfyM27yn4Nc0x3AnWb2DNDLwtfoK+mczyrU3zldLi0xISLS4tQjEBFpcUoEIiItTolARKTFKRGIiLQ4JQIRkRanRCAi0uKUCEREWpwSgYhIi/v/RDUmNN7rzxQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oRCIo362tvXY"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FYxNVl_voRI0"
   },
   "source": [
    "### Question 11: Read the data given in bc2.csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nNWNuBwDoRI1"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gLQK_kpOoRI3"
   },
   "source": [
    "### Question 12: Observe the no.of records in dataset and type of each feature "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yy33dG0uoRI3"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2GPh2IG2oRI5"
   },
   "source": [
    "### Question 13: Use summary statistics to check if missing values, outlier and encoding treament is necessary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-lrG5fUjoRI5"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "w7lD_BpMoRI7"
   },
   "source": [
    "### Check Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xXnH6R9joRI8"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "E2qDpCaroRI-"
   },
   "source": [
    "### Question 14: Check how many `?` there in Bare Nuclei feature (they are also unknown or missing values). Replace them with the top value of the describe function of Bare Nuclei feature.\n",
    "\n",
    "#### Check include='all' parameter in describe function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VW5-h6yroRI-"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "c4MOfL87oRJB"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "M8dxye0VoRJE"
   },
   "source": [
    "### Question 15: Find the distribution of target variable (Class) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yhJTUmaXoRJE"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "T8Lir02qoRJG"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L7pC43cFoRJI"
   },
   "source": [
    "#### Plot the distribution of target variable using histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MmjQ0-ZooRJJ"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WZuW3rD3oRJL"
   },
   "source": [
    "### convert the datatype of Bare Nuclei to `int`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qxzASZ_BoRJM"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OTRdmRpUoRJN"
   },
   "source": [
    "### Question 16: Standardization of Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M0Dm7q_EoRJN"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3OT52Jx0oRJP"
   },
   "source": [
    "### Question 17: Plot Scatter Matrix to understand the distribution of variables and check if any variables are collinear and drop one of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "upYNDff5oRJQ"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1gGXKkEVoRJa"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Hi9KOZeWoRJb"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5cuMLsTvoRJe"
   },
   "source": [
    "### Question 18: Divide the dataset into feature set and target set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zbcuQmsBoRJe"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ncqg5Z_xoRJg"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2lngfgvwoRJh"
   },
   "source": [
    "### Divide the Training and Test sets in 70:30 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5DA_oGaqoRJi"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "y9vsO7K0oRJl"
   },
   "source": [
    "## Question 19 - Finding Optimal value of k\n",
    "\n",
    "Run the KNN with no of neighbours to be 1,3,5..19 and *Find the **optimal number of neighbours** from the above list using the Mis classification error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4j6X_3kZoRJl"
   },
   "source": [
    "Hint:\n",
    "\n",
    "Misclassification error (MSE) = 1 - Test accuracy score. Calculated MSE for each model with neighbours = 1,3,5...19 and find the model with lowest MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
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    "### Question 20: Print the optimal number of neighbors"
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