fruite_dataset_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import neccessary library\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sbn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#csv file read and the give individual column name\n",
    "df = pd.read_csv(\"fruit_dataset_classification.csv\", names=['Class', 'Fruit','A','B','C','D','E'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Class</th>\n",
       "      <th>Fruit</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>192</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>180</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>176</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>86</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>84</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>80</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>80</td>\n",
       "      <td>5.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>76</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>172</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>166</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>172</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>154</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>164</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>152</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>156</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>156</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>168</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>162</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>162</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>156</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>140</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>170</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>spanish_jumbo</td>\n",
       "      <td>342</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>spanish_jumbo</td>\n",
       "      <td>356</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>spanish_jumbo</td>\n",
       "      <td>362</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>selected_seconds</td>\n",
       "      <td>204</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>selected_seconds</td>\n",
       "      <td>140</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>selected_seconds</td>\n",
       "      <td>160</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>selected_seconds</td>\n",
       "      <td>158</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>selected_seconds</td>\n",
       "      <td>210</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>selected_seconds</td>\n",
       "      <td>164</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>142</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>160</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>158</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>144</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>154</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>180</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>spanish_belsan</td>\n",
       "      <td>194</td>\n",
       "      <td>7.2</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>spanish_belsan</td>\n",
       "      <td>200</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>spanish_belsan</td>\n",
       "      <td>186</td>\n",
       "      <td>7.2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>spanish_belsan</td>\n",
       "      <td>216</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.2</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>spanish_belsan</td>\n",
       "      <td>196</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>spanish_belsan</td>\n",
       "      <td>174</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>132</td>\n",
       "      <td>5.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>116</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>118</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>120</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>116</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>116</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>152</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>118</td>\n",
       "      <td>6.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Class     Fruit                 A    B    C     D     E\n",
       "0       1     apple      granny_smith  192  8.4   7.3  0.55\n",
       "1       1     apple      granny_smith  180  8.0   6.8  0.59\n",
       "2       1     apple      granny_smith  176  7.4   7.2  0.60\n",
       "3       2  mandarin          mandarin   86  6.2   0.0  0.80\n",
       "4       2  mandarin          mandarin   84  6.0   4.6  0.79\n",
       "5       2  mandarin          mandarin   80  5.8   4.3  0.77\n",
       "6       2  mandarin          mandarin   80  5.9   4.3  0.81\n",
       "7       2  mandarin          mandarin   76  5.8   4.0  0.81\n",
       "8       1     apple          braeburn    0  7.1   7.8  0.00\n",
       "9       1     apple          braeburn  172  0.0   7.0  0.89\n",
       "10      1     apple          braeburn  166  6.9   7.3  0.93\n",
       "11      1     apple          braeburn  172  7.1   7.6  0.92\n",
       "12      1     apple          braeburn  154  7.0   7.1  0.00\n",
       "13      1     apple  golden_delicious  164  7.3   7.7  0.70\n",
       "14      1     apple  golden_delicious  152  7.6   7.3  0.69\n",
       "15      1     apple  golden_delicious  156  7.7   7.1  0.69\n",
       "16      1     apple  golden_delicious  156  7.6   0.0  0.67\n",
       "17      1     apple  golden_delicious  168  0.0   7.6  0.73\n",
       "18      1     apple       cripps_pink  162  7.5   7.1  0.00\n",
       "19      1     apple       cripps_pink  162  7.4   7.2  0.85\n",
       "20      1     apple       cripps_pink    0  7.5   7.5  0.86\n",
       "21      1     apple       cripps_pink  156  7.4   7.4  0.84\n",
       "22      1     apple       cripps_pink  140  7.3   7.1  0.87\n",
       "23      1     apple       cripps_pink  170  7.6   7.9  0.88\n",
       "24      3    orange     spanish_jumbo  342  0.0   9.4  0.75\n",
       "25      3    orange     spanish_jumbo  356  9.2   0.0  0.75\n",
       "26      3    orange     spanish_jumbo  362  9.6   9.2  0.74\n",
       "27      3    orange  selected_seconds  204  7.5   9.2  0.77\n",
       "28      3    orange  selected_seconds  140  6.7   7.1  0.72\n",
       "29      3    orange  selected_seconds  160  7.0   7.4  0.81\n",
       "30      3    orange  selected_seconds  158  7.1   7.5  0.79\n",
       "31      3    orange  selected_seconds  210  7.8   8.0  0.82\n",
       "32      3    orange  selected_seconds  164  7.2   7.0  0.80\n",
       "33      3    orange      turkey_navel    0  7.5   8.1  0.74\n",
       "34      3    orange      turkey_navel  142  7.6   7.8  0.75\n",
       "35      3    orange      turkey_navel  150  0.0   7.9  0.75\n",
       "36      3    orange      turkey_navel  160  7.1   7.6  0.76\n",
       "37      3    orange      turkey_navel    0  7.3   7.3  0.79\n",
       "38      3    orange      turkey_navel  158  7.2   7.8  0.00\n",
       "39      3    orange      turkey_navel  144  6.8   7.4  0.75\n",
       "40      3    orange      turkey_navel  154  7.1   7.5  0.78\n",
       "41      3    orange      turkey_navel  180  7.6   0.0  0.79\n",
       "42      3    orange      turkey_navel    0  7.2   7.2  0.82\n",
       "43      4     lemon    spanish_belsan  194  7.2  10.3  0.70\n",
       "44      4     lemon    spanish_belsan  200  7.3  10.5  0.72\n",
       "45      4     lemon    spanish_belsan  186  7.2   9.2  0.72\n",
       "46      4     lemon    spanish_belsan  216  7.3  10.2  0.71\n",
       "47      4     lemon    spanish_belsan  196  7.3   0.0  0.72\n",
       "48      4     lemon    spanish_belsan  174  7.3  10.1  0.72\n",
       "49      4     lemon           unknown  132  5.8   8.7  0.73\n",
       "50      4     lemon           unknown    0  6.0   8.2  0.71\n",
       "51      4     lemon           unknown  116  6.0   7.5  0.72\n",
       "52      4     lemon           unknown  118  5.9   8.0  0.72\n",
       "53      4     lemon           unknown  120  6.0   8.4  0.74\n",
       "54      4     lemon           unknown    0  6.1   8.5  0.00\n",
       "55      4     lemon           unknown  116  6.3   7.7  0.72\n",
       "56      4     lemon           unknown  116  5.9   8.1  0.73\n",
       "57      4     lemon           unknown  152  6.5   8.5  0.72\n",
       "58      4     lemon           unknown  118  6.1   8.1  0.70"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#unneccessary column name widrow from dataframe\n",
    "df = df.drop(['Fruit'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Class</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>192</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>180</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>176</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>86</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>84</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>80</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>80</td>\n",
       "      <td>5.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>76</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>172</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>166</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>172</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>154</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>164</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>152</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>156</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>156</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>168</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>162</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>162</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>156</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>140</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>170</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>spanish_jumbo</td>\n",
       "      <td>342</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>spanish_jumbo</td>\n",
       "      <td>356</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>spanish_jumbo</td>\n",
       "      <td>362</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>selected_seconds</td>\n",
       "      <td>204</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>selected_seconds</td>\n",
       "      <td>140</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>selected_seconds</td>\n",
       "      <td>160</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3</td>\n",
       "      <td>selected_seconds</td>\n",
       "      <td>158</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3</td>\n",
       "      <td>selected_seconds</td>\n",
       "      <td>210</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3</td>\n",
       "      <td>selected_seconds</td>\n",
       "      <td>164</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>142</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>160</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>3</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>158</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>144</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>3</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>154</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>3</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>180</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>3</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>4</td>\n",
       "      <td>spanish_belsan</td>\n",
       "      <td>194</td>\n",
       "      <td>7.2</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>4</td>\n",
       "      <td>spanish_belsan</td>\n",
       "      <td>200</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>4</td>\n",
       "      <td>spanish_belsan</td>\n",
       "      <td>186</td>\n",
       "      <td>7.2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>4</td>\n",
       "      <td>spanish_belsan</td>\n",
       "      <td>216</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.2</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4</td>\n",
       "      <td>spanish_belsan</td>\n",
       "      <td>196</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>4</td>\n",
       "      <td>spanish_belsan</td>\n",
       "      <td>174</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4</td>\n",
       "      <td>unknown</td>\n",
       "      <td>132</td>\n",
       "      <td>5.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>4</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>4</td>\n",
       "      <td>unknown</td>\n",
       "      <td>116</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>4</td>\n",
       "      <td>unknown</td>\n",
       "      <td>118</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>4</td>\n",
       "      <td>unknown</td>\n",
       "      <td>120</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>4</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>4</td>\n",
       "      <td>unknown</td>\n",
       "      <td>116</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>4</td>\n",
       "      <td>unknown</td>\n",
       "      <td>116</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4</td>\n",
       "      <td>unknown</td>\n",
       "      <td>152</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>4</td>\n",
       "      <td>unknown</td>\n",
       "      <td>118</td>\n",
       "      <td>6.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Class                 A    B    C     D     E\n",
       "0       1      granny_smith  192  8.4   7.3  0.55\n",
       "1       1      granny_smith  180  8.0   6.8  0.59\n",
       "2       1      granny_smith  176  7.4   7.2  0.60\n",
       "3       2          mandarin   86  6.2   0.0  0.80\n",
       "4       2          mandarin   84  6.0   4.6  0.79\n",
       "5       2          mandarin   80  5.8   4.3  0.77\n",
       "6       2          mandarin   80  5.9   4.3  0.81\n",
       "7       2          mandarin   76  5.8   4.0  0.81\n",
       "8       1          braeburn    0  7.1   7.8  0.00\n",
       "9       1          braeburn  172  0.0   7.0  0.89\n",
       "10      1          braeburn  166  6.9   7.3  0.93\n",
       "11      1          braeburn  172  7.1   7.6  0.92\n",
       "12      1          braeburn  154  7.0   7.1  0.00\n",
       "13      1  golden_delicious  164  7.3   7.7  0.70\n",
       "14      1  golden_delicious  152  7.6   7.3  0.69\n",
       "15      1  golden_delicious  156  7.7   7.1  0.69\n",
       "16      1  golden_delicious  156  7.6   0.0  0.67\n",
       "17      1  golden_delicious  168  0.0   7.6  0.73\n",
       "18      1       cripps_pink  162  7.5   7.1  0.00\n",
       "19      1       cripps_pink  162  7.4   7.2  0.85\n",
       "20      1       cripps_pink    0  7.5   7.5  0.86\n",
       "21      1       cripps_pink  156  7.4   7.4  0.84\n",
       "22      1       cripps_pink  140  7.3   7.1  0.87\n",
       "23      1       cripps_pink  170  7.6   7.9  0.88\n",
       "24      3     spanish_jumbo  342  0.0   9.4  0.75\n",
       "25      3     spanish_jumbo  356  9.2   0.0  0.75\n",
       "26      3     spanish_jumbo  362  9.6   9.2  0.74\n",
       "27      3  selected_seconds  204  7.5   9.2  0.77\n",
       "28      3  selected_seconds  140  6.7   7.1  0.72\n",
       "29      3  selected_seconds  160  7.0   7.4  0.81\n",
       "30      3  selected_seconds  158  7.1   7.5  0.79\n",
       "31      3  selected_seconds  210  7.8   8.0  0.82\n",
       "32      3  selected_seconds  164  7.2   7.0  0.80\n",
       "33      3      turkey_navel    0  7.5   8.1  0.74\n",
       "34      3      turkey_navel  142  7.6   7.8  0.75\n",
       "35      3      turkey_navel  150  0.0   7.9  0.75\n",
       "36      3      turkey_navel  160  7.1   7.6  0.76\n",
       "37      3      turkey_navel    0  7.3   7.3  0.79\n",
       "38      3      turkey_navel  158  7.2   7.8  0.00\n",
       "39      3      turkey_navel  144  6.8   7.4  0.75\n",
       "40      3      turkey_navel  154  7.1   7.5  0.78\n",
       "41      3      turkey_navel  180  7.6   0.0  0.79\n",
       "42      3      turkey_navel    0  7.2   7.2  0.82\n",
       "43      4    spanish_belsan  194  7.2  10.3  0.70\n",
       "44      4    spanish_belsan  200  7.3  10.5  0.72\n",
       "45      4    spanish_belsan  186  7.2   9.2  0.72\n",
       "46      4    spanish_belsan  216  7.3  10.2  0.71\n",
       "47      4    spanish_belsan  196  7.3   0.0  0.72\n",
       "48      4    spanish_belsan  174  7.3  10.1  0.72\n",
       "49      4           unknown  132  5.8   8.7  0.73\n",
       "50      4           unknown    0  6.0   8.2  0.71\n",
       "51      4           unknown  116  6.0   7.5  0.72\n",
       "52      4           unknown  118  5.9   8.0  0.72\n",
       "53      4           unknown  120  6.0   8.4  0.74\n",
       "54      4           unknown    0  6.1   8.5  0.00\n",
       "55      4           unknown  116  6.3   7.7  0.72\n",
       "56      4           unknown  116  5.9   8.1  0.73\n",
       "57      4           unknown  152  6.5   8.5  0.72\n",
       "58      4           unknown  118  6.1   8.1  0.70"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ৩য় কলামের ডাটাকে numerical ভ্যালুতে কনভার্ট করা"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['A'].replace({\"granny_smith\": 1 , \"mandarin\": 2, \"braeburn\": 3, \"golden_delicious\": 4, \"cripps_pink\": 5, \"spanish_jumbo\": 7, \"selected_seconds\": 8, \"turkey_navel\": 9, \"spanish_belsan\": 10, \"unknown\": 11 },inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Class</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>191</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>180</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>176</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>86</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>84</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>5.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>76</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>172</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>166</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>172</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>154</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>164</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>152</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>156</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>156</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>168</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>162</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>162</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>156</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>140</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>170</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>342</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>356</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>362</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>204</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>140</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>160</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>158</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>210</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>164</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>142</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>160</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>158</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>144</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>154</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>180</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>194</td>\n",
       "      <td>7.2</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>200</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>186</td>\n",
       "      <td>7.2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>216</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.2</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>196</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>174</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>132</td>\n",
       "      <td>5.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>116</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>118</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>120</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>116</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>116</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>152</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>118</td>\n",
       "      <td>6.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Class   A    B    C     D     E\n",
       "0       1   1  192  8.4   7.3  0.55\n",
       "1       1   1  180  8.0   6.8  0.59\n",
       "2       1   1  176  7.4   7.2  0.60\n",
       "3       2   2   86  6.2   0.0  0.80\n",
       "4       2   2   84  6.0   4.6  0.79\n",
       "5       2   2   80  5.8   4.3  0.77\n",
       "6       2   2   80  5.9   4.3  0.81\n",
       "7       2   2   76  5.8   4.0  0.81\n",
       "8       1   3    0  7.1   7.8  0.00\n",
       "9       1   3  172  0.0   7.0  0.89\n",
       "10      1   3  166  6.9   7.3  0.93\n",
       "11      1   3  172  7.1   7.6  0.92\n",
       "12      1   3  154  7.0   7.1  0.00\n",
       "13      1   4  164  7.3   7.7  0.70\n",
       "14      1   4  152  7.6   7.3  0.69\n",
       "15      1   4  156  7.7   7.1  0.69\n",
       "16      1   4  156  7.6   0.0  0.67\n",
       "17      1   4  168  0.0   7.6  0.73\n",
       "18      1   5  162  7.5   7.1  0.00\n",
       "19      1   5  162  7.4   7.2  0.85\n",
       "20      1   5    0  7.5   7.5  0.86\n",
       "21      1   5  156  7.4   7.4  0.84\n",
       "22      1   5  140  7.3   7.1  0.87\n",
       "23      1   5  170  7.6   7.9  0.88\n",
       "24      3   7  342  0.0   9.4  0.75\n",
       "25      3   7  356  9.2   0.0  0.75\n",
       "26      3   7  362  9.6   9.2  0.74\n",
       "27      3   8  204  7.5   9.2  0.77\n",
       "28      3   8  140  6.7   7.1  0.72\n",
       "29      3   8  160  7.0   7.4  0.81\n",
       "30      3   8  158  7.1   7.5  0.79\n",
       "31      3   8  210  7.8   8.0  0.82\n",
       "32      3   8  164  7.2   7.0  0.80\n",
       "33      3   9    0  7.5   8.1  0.74\n",
       "34      3   9  142  7.6   7.8  0.75\n",
       "35      3   9  150  0.0   7.9  0.75\n",
       "36      3   9  160  7.1   7.6  0.76\n",
       "37      3   9    0  7.3   7.3  0.79\n",
       "38      3   9  158  7.2   7.8  0.00\n",
       "39      3   9  144  6.8   7.4  0.75\n",
       "40      3   9  154  7.1   7.5  0.78\n",
       "41      3   9  180  7.6   0.0  0.79\n",
       "42      3   9    0  7.2   7.2  0.82\n",
       "43      4  10  194  7.2  10.3  0.70\n",
       "44      4  10  200  7.3  10.5  0.72\n",
       "45      4  10  186  7.2   9.2  0.72\n",
       "46      4  10  216  7.3  10.2  0.71\n",
       "47      4  10  196  7.3   0.0  0.72\n",
       "48      4  10  174  7.3  10.1  0.72\n",
       "49      4  11  132  5.8   8.7  0.73\n",
       "50      4  11    0  6.0   8.2  0.71\n",
       "51      4  11  116  6.0   7.5  0.72\n",
       "52      4  11  118  5.9   8.0  0.72\n",
       "53      4  11  120  6.0   8.4  0.74\n",
       "54      4  11    0  6.1   8.5  0.00\n",
       "55      4  11  116  6.3   7.7  0.72\n",
       "56      4  11  116  5.9   8.1  0.73\n",
       "57      4  11  152  6.5   8.5  0.72\n",
       "58      4  11  118  6.1   8.1  0.70"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class    0\n",
       "A        0\n",
       "B        0\n",
       "C        0\n",
       "D        0\n",
       "E        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# শেষ ৪টি কলামের ডাটাতে মিসিং ভ্যালু আছে কিনা চেক করা এবং থাকলে Imputation-এর মাধ্যমে replace করা"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['B','C','D','E']] = df[['B','C','D','E']].replace(0.00, nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Class</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>192.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>180.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>176.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>86.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>84.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>76.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>172.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>166.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>172.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>154.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>164.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>152.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>156.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>156.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>162.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>162.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>156.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>140.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>170.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>342.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>356.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>362.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>204.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>160.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>158.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>210.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>164.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>142.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>160.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>158.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>144.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>154.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>180.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>194.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>200.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>186.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>216.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.2</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>196.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>174.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>132.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>116.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>120.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>116.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>116.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>152.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>118.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Class   A      B    C     D     E\n",
       "0       1   1  192.0  8.4   7.3  0.55\n",
       "1       1   1  180.0  8.0   6.8  0.59\n",
       "2       1   1  176.0  7.4   7.2  0.60\n",
       "3       2   2   86.0  6.2   NaN  0.80\n",
       "4       2   2   84.0  6.0   4.6  0.79\n",
       "5       2   2   80.0  5.8   4.3  0.77\n",
       "6       2   2   80.0  5.9   4.3  0.81\n",
       "7       2   2   76.0  5.8   4.0  0.81\n",
       "8       1   3    NaN  7.1   7.8   NaN\n",
       "9       1   3  172.0  NaN   7.0  0.89\n",
       "10      1   3  166.0  6.9   7.3  0.93\n",
       "11      1   3  172.0  7.1   7.6  0.92\n",
       "12      1   3  154.0  7.0   7.1   NaN\n",
       "13      1   4  164.0  7.3   7.7  0.70\n",
       "14      1   4  152.0  7.6   7.3  0.69\n",
       "15      1   4  156.0  7.7   7.1  0.69\n",
       "16      1   4  156.0  7.6   NaN  0.67\n",
       "17      1   4  168.0  NaN   7.6  0.73\n",
       "18      1   5  162.0  7.5   7.1   NaN\n",
       "19      1   5  162.0  7.4   7.2  0.85\n",
       "20      1   5    NaN  7.5   7.5  0.86\n",
       "21      1   5  156.0  7.4   7.4  0.84\n",
       "22      1   5  140.0  7.3   7.1  0.87\n",
       "23      1   5  170.0  7.6   7.9  0.88\n",
       "24      3   7  342.0  NaN   9.4  0.75\n",
       "25      3   7  356.0  9.2   NaN  0.75\n",
       "26      3   7  362.0  9.6   9.2  0.74\n",
       "27      3   8  204.0  7.5   9.2  0.77\n",
       "28      3   8  140.0  6.7   7.1  0.72\n",
       "29      3   8  160.0  7.0   7.4  0.81\n",
       "30      3   8  158.0  7.1   7.5  0.79\n",
       "31      3   8  210.0  7.8   8.0  0.82\n",
       "32      3   8  164.0  7.2   7.0  0.80\n",
       "33      3   9    NaN  7.5   8.1  0.74\n",
       "34      3   9  142.0  7.6   7.8  0.75\n",
       "35      3   9  150.0  NaN   7.9  0.75\n",
       "36      3   9  160.0  7.1   7.6  0.76\n",
       "37      3   9    NaN  7.3   7.3  0.79\n",
       "38      3   9  158.0  7.2   7.8   NaN\n",
       "39      3   9  144.0  6.8   7.4  0.75\n",
       "40      3   9  154.0  7.1   7.5  0.78\n",
       "41      3   9  180.0  7.6   NaN  0.79\n",
       "42      3   9    NaN  7.2   7.2  0.82\n",
       "43      4  10  194.0  7.2  10.3  0.70\n",
       "44      4  10  200.0  7.3  10.5  0.72\n",
       "45      4  10  186.0  7.2   9.2  0.72\n",
       "46      4  10  216.0  7.3  10.2  0.71\n",
       "47      4  10  196.0  7.3   NaN  0.72\n",
       "48      4  10  174.0  7.3  10.1  0.72\n",
       "49      4  11  132.0  5.8   8.7  0.73\n",
       "50      4  11    NaN  6.0   8.2  0.71\n",
       "51      4  11  116.0  6.0   7.5  0.72\n",
       "52      4  11  118.0  5.9   8.0  0.72\n",
       "53      4  11  120.0  6.0   8.4  0.74\n",
       "54      4  11    NaN  6.1   8.5   NaN\n",
       "55      4  11  116.0  6.3   7.7  0.72\n",
       "56      4  11  116.0  5.9   8.1  0.73\n",
       "57      4  11  152.0  6.5   8.5  0.72\n",
       "58      4  11  118.0  6.1   8.1  0.70"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class    0\n",
      "A        0\n",
      "B        7\n",
      "C        4\n",
      "D        5\n",
      "E        5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna(df.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class    0\n",
      "A        0\n",
      "B        0\n",
      "C        0\n",
      "D        0\n",
      "E        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(['Class'],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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",
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       "\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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.590000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>7.677778</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>0.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>0.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>164.269231</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>0.757407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>7.058182</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>0.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>0.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.757407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>0.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>7.677778</td>\n",
       "      <td>0.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>7.058182</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>0.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.757407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5</td>\n",
       "      <td>164.269231</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.860000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>0.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>7.058182</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7</td>\n",
       "      <td>356.000000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>7.677778</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>7</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.740000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8</td>\n",
       "      <td>204.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>8</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>8</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>0.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>8</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>8</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>8</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>9</td>\n",
       "      <td>164.269231</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>0.740000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>9</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>9</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>7.058182</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>9</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>0.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>9</td>\n",
       "      <td>164.269231</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>0.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>9</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>0.757407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>9</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>9</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.780000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>9</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>7.677778</td>\n",
       "      <td>0.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>9</td>\n",
       "      <td>164.269231</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>10</td>\n",
       "      <td>194.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>10</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>10</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>10</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>0.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>10</td>\n",
       "      <td>196.000000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>7.677778</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>10</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>11</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>0.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>11</td>\n",
       "      <td>164.269231</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>0.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>11</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>11</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>11</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>0.740000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>11</td>\n",
       "      <td>164.269231</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>0.757407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>11</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>11</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>0.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>11</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>11</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A           B         C          D         E\n",
       "0    1  192.000000  8.400000   7.300000  0.550000\n",
       "1    1  180.000000  8.000000   6.800000  0.590000\n",
       "2    1  176.000000  7.400000   7.200000  0.600000\n",
       "3    2   86.000000  6.200000   7.677778  0.800000\n",
       "4    2   84.000000  6.000000   4.600000  0.790000\n",
       "5    2   80.000000  5.800000   4.300000  0.770000\n",
       "6    2   80.000000  5.900000   4.300000  0.810000\n",
       "7    2   76.000000  5.800000   4.000000  0.810000\n",
       "8    3  164.269231  7.100000   7.800000  0.757407\n",
       "9    3  172.000000  7.058182   7.000000  0.890000\n",
       "10   3  166.000000  6.900000   7.300000  0.930000\n",
       "11   3  172.000000  7.100000   7.600000  0.920000\n",
       "12   3  154.000000  7.000000   7.100000  0.757407\n",
       "13   4  164.000000  7.300000   7.700000  0.700000\n",
       "14   4  152.000000  7.600000   7.300000  0.690000\n",
       "15   4  156.000000  7.700000   7.100000  0.690000\n",
       "16   4  156.000000  7.600000   7.677778  0.670000\n",
       "17   4  168.000000  7.058182   7.600000  0.730000\n",
       "18   5  162.000000  7.500000   7.100000  0.757407\n",
       "19   5  162.000000  7.400000   7.200000  0.850000\n",
       "20   5  164.269231  7.500000   7.500000  0.860000\n",
       "21   5  156.000000  7.400000   7.400000  0.840000\n",
       "22   5  140.000000  7.300000   7.100000  0.870000\n",
       "23   5  170.000000  7.600000   7.900000  0.880000\n",
       "24   7  342.000000  7.058182   9.400000  0.750000\n",
       "25   7  356.000000  9.200000   7.677778  0.750000\n",
       "26   7  362.000000  9.600000   9.200000  0.740000\n",
       "27   8  204.000000  7.500000   9.200000  0.770000\n",
       "28   8  140.000000  6.700000   7.100000  0.720000\n",
       "29   8  160.000000  7.000000   7.400000  0.810000\n",
       "30   8  158.000000  7.100000   7.500000  0.790000\n",
       "31   8  210.000000  7.800000   8.000000  0.820000\n",
       "32   8  164.000000  7.200000   7.000000  0.800000\n",
       "33   9  164.269231  7.500000   8.100000  0.740000\n",
       "34   9  142.000000  7.600000   7.800000  0.750000\n",
       "35   9  150.000000  7.058182   7.900000  0.750000\n",
       "36   9  160.000000  7.100000   7.600000  0.760000\n",
       "37   9  164.269231  7.300000   7.300000  0.790000\n",
       "38   9  158.000000  7.200000   7.800000  0.757407\n",
       "39   9  144.000000  6.800000   7.400000  0.750000\n",
       "40   9  154.000000  7.100000   7.500000  0.780000\n",
       "41   9  180.000000  7.600000   7.677778  0.790000\n",
       "42   9  164.269231  7.200000   7.200000  0.820000\n",
       "43  10  194.000000  7.200000  10.300000  0.700000\n",
       "44  10  200.000000  7.300000  10.500000  0.720000\n",
       "45  10  186.000000  7.200000   9.200000  0.720000\n",
       "46  10  216.000000  7.300000  10.200000  0.710000\n",
       "47  10  196.000000  7.300000   7.677778  0.720000\n",
       "48  10  174.000000  7.300000  10.100000  0.720000\n",
       "49  11  132.000000  5.800000   8.700000  0.730000\n",
       "50  11  164.269231  6.000000   8.200000  0.710000\n",
       "51  11  116.000000  6.000000   7.500000  0.720000\n",
       "52  11  118.000000  5.900000   8.000000  0.720000\n",
       "53  11  120.000000  6.000000   8.400000  0.740000\n",
       "54  11  164.269231  6.100000   8.500000  0.757407\n",
       "55  11  116.000000  6.300000   7.700000  0.720000\n",
       "56  11  116.000000  5.900000   8.100000  0.730000\n",
       "57  11  152.000000  6.500000   8.500000  0.720000\n",
       "58  11  118.000000  6.100000   8.100000  0.700000"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1     1\n",
       "2     1\n",
       "3     2\n",
       "4     2\n",
       "5     2\n",
       "6     2\n",
       "7     2\n",
       "8     1\n",
       "9     1\n",
       "10    1\n",
       "11    1\n",
       "12    1\n",
       "13    1\n",
       "14    1\n",
       "15    1\n",
       "16    1\n",
       "17    1\n",
       "18    1\n",
       "19    1\n",
       "20    1\n",
       "21    1\n",
       "22    1\n",
       "23    1\n",
       "24    3\n",
       "25    3\n",
       "26    3\n",
       "27    3\n",
       "28    3\n",
       "29    3\n",
       "30    3\n",
       "31    3\n",
       "32    3\n",
       "33    3\n",
       "34    3\n",
       "35    3\n",
       "36    3\n",
       "37    3\n",
       "38    3\n",
       "39    3\n",
       "40    3\n",
       "41    3\n",
       "42    3\n",
       "43    4\n",
       "44    4\n",
       "45    4\n",
       "46    4\n",
       "47    4\n",
       "48    4\n",
       "49    4\n",
       "50    4\n",
       "51    4\n",
       "52    4\n",
       "53    4\n",
       "54    4\n",
       "55    4\n",
       "56    4\n",
       "57    4\n",
       "58    4\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ডাটাসেট ব্যালান্সড কিনা চেক করা"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    19\n",
       "1    19\n",
       "4    16\n",
       "2     5\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 61,
     "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": [
    "df['Class'].value_counts().plot.bar()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Class', ylabel='count'>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sbn.countplot(\n",
    "    x=\"Class\",\n",
    "    data=df\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_target = df.groupby(\"Class\").size()\n",
    "df_target\n",
    "plt.pie(df_target.values, labels = [\"Class 1\", \"Class 2\", \"Class 3\", \"Class 4\"], autopct='%1.1f%%', radius = 1.5, textprops = {\"fontsize\" : 16}) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x21df19313a0>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbn.pairplot(data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(12,12), layout=(5,3));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ডাটা স্কেলিং"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "MinMaxScaler = MinMaxScaler()\n",
    "columns_to_scale = ['A','B','C','D','E']\n",
    "df[columns_to_scale] = MinMaxScaler.fit_transform(df[columns_to_scale])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.405594</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.507692</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.430769</td>\n",
       "      <td>0.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.349650</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.492308</td>\n",
       "      <td>0.131579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.034965</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.565812</td>\n",
       "      <td>0.657895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.027972</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.092308</td>\n",
       "      <td>0.631579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.013986</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.046154</td>\n",
       "      <td>0.578947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.013986</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.046154</td>\n",
       "      <td>0.684211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.684211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.308634</td>\n",
       "      <td>0.342105</td>\n",
       "      <td>0.584615</td>\n",
       "      <td>0.545809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.335664</td>\n",
       "      <td>0.331100</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.894737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.314685</td>\n",
       "      <td>0.289474</td>\n",
       "      <td>0.507692</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.335664</td>\n",
       "      <td>0.342105</td>\n",
       "      <td>0.553846</td>\n",
       "      <td>0.973684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.476923</td>\n",
       "      <td>0.545809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.394737</td>\n",
       "      <td>0.569231</td>\n",
       "      <td>0.394737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.265734</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.507692</td>\n",
       "      <td>0.368421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.279720</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.476923</td>\n",
       "      <td>0.368421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.279720</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.565812</td>\n",
       "      <td>0.315789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.321678</td>\n",
       "      <td>0.331100</td>\n",
       "      <td>0.553846</td>\n",
       "      <td>0.473684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.300699</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.476923</td>\n",
       "      <td>0.545809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.300699</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.492308</td>\n",
       "      <td>0.789474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.308634</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.815789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.279720</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.523077</td>\n",
       "      <td>0.763158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.223776</td>\n",
       "      <td>0.394737</td>\n",
       "      <td>0.476923</td>\n",
       "      <td>0.842105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.328671</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.868421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.930070</td>\n",
       "      <td>0.331100</td>\n",
       "      <td>0.830769</td>\n",
       "      <td>0.526316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.979021</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.565812</td>\n",
       "      <td>0.526316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.447552</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.578947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.223776</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>0.476923</td>\n",
       "      <td>0.447368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.293706</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.523077</td>\n",
       "      <td>0.684211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.286713</td>\n",
       "      <td>0.342105</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.631579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.468531</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.710526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.657895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.308634</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.630769</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.584615</td>\n",
       "      <td>0.526316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.258741</td>\n",
       "      <td>0.331100</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.526316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.293706</td>\n",
       "      <td>0.342105</td>\n",
       "      <td>0.553846</td>\n",
       "      <td>0.552632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.308634</td>\n",
       "      <td>0.394737</td>\n",
       "      <td>0.507692</td>\n",
       "      <td>0.631579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.286713</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.584615</td>\n",
       "      <td>0.545809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.237762</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.523077</td>\n",
       "      <td>0.526316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.342105</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.605263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.565812</td>\n",
       "      <td>0.631579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.308634</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.492308</td>\n",
       "      <td>0.710526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.9</td>\n",
       "      <td>0.412587</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.969231</td>\n",
       "      <td>0.394737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.9</td>\n",
       "      <td>0.433566</td>\n",
       "      <td>0.394737</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.447368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.9</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.447368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.9</td>\n",
       "      <td>0.489510</td>\n",
       "      <td>0.394737</td>\n",
       "      <td>0.953846</td>\n",
       "      <td>0.421053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.9</td>\n",
       "      <td>0.419580</td>\n",
       "      <td>0.394737</td>\n",
       "      <td>0.565812</td>\n",
       "      <td>0.447368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.9</td>\n",
       "      <td>0.342657</td>\n",
       "      <td>0.394737</td>\n",
       "      <td>0.938462</td>\n",
       "      <td>0.447368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.195804</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.723077</td>\n",
       "      <td>0.473684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.308634</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.646154</td>\n",
       "      <td>0.421053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.139860</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.447368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.146853</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.447368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.676923</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.308634</td>\n",
       "      <td>0.078947</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.545809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.139860</td>\n",
       "      <td>0.131579</td>\n",
       "      <td>0.569231</td>\n",
       "      <td>0.447368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.139860</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.630769</td>\n",
       "      <td>0.473684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.265734</td>\n",
       "      <td>0.184211</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.447368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.146853</td>\n",
       "      <td>0.078947</td>\n",
       "      <td>0.630769</td>\n",
       "      <td>0.394737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A         B         C         D         E\n",
       "0   0.0  0.405594  0.684211  0.507692  0.000000\n",
       "1   0.0  0.363636  0.578947  0.430769  0.105263\n",
       "2   0.0  0.349650  0.421053  0.492308  0.131579\n",
       "3   0.1  0.034965  0.105263  0.565812  0.657895\n",
       "4   0.1  0.027972  0.052632  0.092308  0.631579\n",
       "5   0.1  0.013986  0.000000  0.046154  0.578947\n",
       "6   0.1  0.013986  0.026316  0.046154  0.684211\n",
       "7   0.1  0.000000  0.000000  0.000000  0.684211\n",
       "8   0.2  0.308634  0.342105  0.584615  0.545809\n",
       "9   0.2  0.335664  0.331100  0.461538  0.894737\n",
       "10  0.2  0.314685  0.289474  0.507692  1.000000\n",
       "11  0.2  0.335664  0.342105  0.553846  0.973684\n",
       "12  0.2  0.272727  0.315789  0.476923  0.545809\n",
       "13  0.3  0.307692  0.394737  0.569231  0.394737\n",
       "14  0.3  0.265734  0.473684  0.507692  0.368421\n",
       "15  0.3  0.279720  0.500000  0.476923  0.368421\n",
       "16  0.3  0.279720  0.473684  0.565812  0.315789\n",
       "17  0.3  0.321678  0.331100  0.553846  0.473684\n",
       "18  0.4  0.300699  0.447368  0.476923  0.545809\n",
       "19  0.4  0.300699  0.421053  0.492308  0.789474\n",
       "20  0.4  0.308634  0.447368  0.538462  0.815789\n",
       "21  0.4  0.279720  0.421053  0.523077  0.763158\n",
       "22  0.4  0.223776  0.394737  0.476923  0.842105\n",
       "23  0.4  0.328671  0.473684  0.600000  0.868421\n",
       "24  0.6  0.930070  0.331100  0.830769  0.526316\n",
       "25  0.6  0.979021  0.894737  0.565812  0.526316\n",
       "26  0.6  1.000000  1.000000  0.800000  0.500000\n",
       "27  0.7  0.447552  0.447368  0.800000  0.578947\n",
       "28  0.7  0.223776  0.236842  0.476923  0.447368\n",
       "29  0.7  0.293706  0.315789  0.523077  0.684211\n",
       "30  0.7  0.286713  0.342105  0.538462  0.631579\n",
       "31  0.7  0.468531  0.526316  0.615385  0.710526\n",
       "32  0.7  0.307692  0.368421  0.461538  0.657895\n",
       "33  0.8  0.308634  0.447368  0.630769  0.500000\n",
       "34  0.8  0.230769  0.473684  0.584615  0.526316\n",
       "35  0.8  0.258741  0.331100  0.600000  0.526316\n",
       "36  0.8  0.293706  0.342105  0.553846  0.552632\n",
       "37  0.8  0.308634  0.394737  0.507692  0.631579\n",
       "38  0.8  0.286713  0.368421  0.584615  0.545809\n",
       "39  0.8  0.237762  0.263158  0.523077  0.526316\n",
       "40  0.8  0.272727  0.342105  0.538462  0.605263\n",
       "41  0.8  0.363636  0.473684  0.565812  0.631579\n",
       "42  0.8  0.308634  0.368421  0.492308  0.710526\n",
       "43  0.9  0.412587  0.368421  0.969231  0.394737\n",
       "44  0.9  0.433566  0.394737  1.000000  0.447368\n",
       "45  0.9  0.384615  0.368421  0.800000  0.447368\n",
       "46  0.9  0.489510  0.394737  0.953846  0.421053\n",
       "47  0.9  0.419580  0.394737  0.565812  0.447368\n",
       "48  0.9  0.342657  0.394737  0.938462  0.447368\n",
       "49  1.0  0.195804  0.000000  0.723077  0.473684\n",
       "50  1.0  0.308634  0.052632  0.646154  0.421053\n",
       "51  1.0  0.139860  0.052632  0.538462  0.447368\n",
       "52  1.0  0.146853  0.026316  0.615385  0.447368\n",
       "53  1.0  0.153846  0.052632  0.676923  0.500000\n",
       "54  1.0  0.308634  0.078947  0.692308  0.545809\n",
       "55  1.0  0.139860  0.131579  0.569231  0.447368\n",
       "56  1.0  0.139860  0.026316  0.630769  0.473684\n",
       "57  1.0  0.265734  0.184211  0.692308  0.447368\n",
       "58  1.0  0.146853  0.078947  0.630769  0.394737"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[columns_to_scale]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Data\n",
    "ডাটাসেটকে ট্রেইনিং এবং টেস্টিং সেটে ভাগ করা - ৯০:১০ ratio রেখে।"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set(style='whitegrid', color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.10,random_state=20,stratify= y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>10</td>\n",
       "      <td>196.000000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>7.677778</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>9</td>\n",
       "      <td>164.269231</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>10</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>0.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>9</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>8</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7</td>\n",
       "      <td>356.000000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>7.677778</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>11</td>\n",
       "      <td>164.269231</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>0.757407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>0.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>0.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8</td>\n",
       "      <td>204.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.590000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>10</td>\n",
       "      <td>194.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>9</td>\n",
       "      <td>164.269231</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>0.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.757407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>7.058182</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>0.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.757407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>11</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>9</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>7.677778</td>\n",
       "      <td>0.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>8</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>11</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>0.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>0.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>0.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>10</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>9</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>0.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>11</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>9</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>0.757407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>9</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>7.058182</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>9</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>11</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>0.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>7.677778</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>9</td>\n",
       "      <td>164.269231</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>0.740000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>11</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>11</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>0.740000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>7.058182</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>8</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>11</td>\n",
       "      <td>164.269231</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>0.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>9</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.780000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>7.677778</td>\n",
       "      <td>0.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>8</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>10</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>0.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>0.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>7.058182</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>11</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A           B         C          D         E\n",
       "47  10  196.000000  7.300000   7.677778  0.720000\n",
       "42   9  164.269231  7.200000   7.200000  0.820000\n",
       "45  10  186.000000  7.200000   9.200000  0.720000\n",
       "15   4  156.000000  7.700000   7.100000  0.690000\n",
       "21   5  156.000000  7.400000   7.400000  0.840000\n",
       "39   9  144.000000  6.800000   7.400000  0.750000\n",
       "30   8  158.000000  7.100000   7.500000  0.790000\n",
       "25   7  356.000000  9.200000   7.677778  0.750000\n",
       "54  11  164.269231  6.100000   8.500000  0.757407\n",
       "10   3  166.000000  6.900000   7.300000  0.930000\n",
       "11   3  172.000000  7.100000   7.600000  0.920000\n",
       "27   8  204.000000  7.500000   9.200000  0.770000\n",
       "1    1  180.000000  8.000000   6.800000  0.590000\n",
       "43  10  194.000000  7.200000  10.300000  0.700000\n",
       "37   9  164.269231  7.300000   7.300000  0.790000\n",
       "12   3  154.000000  7.000000   7.100000  0.757407\n",
       "17   4  168.000000  7.058182   7.600000  0.730000\n",
       "18   5  162.000000  7.500000   7.100000  0.757407\n",
       "57  11  152.000000  6.500000   8.500000  0.720000\n",
       "41   9  180.000000  7.600000   7.677778  0.790000\n",
       "28   8  140.000000  6.700000   7.100000  0.720000\n",
       "56  11  116.000000  5.900000   8.100000  0.730000\n",
       "6    2   80.000000  5.900000   4.300000  0.810000\n",
       "14   4  152.000000  7.600000   7.300000  0.690000\n",
       "48  10  174.000000  7.300000  10.100000  0.720000\n",
       "36   9  160.000000  7.100000   7.600000  0.760000\n",
       "55  11  116.000000  6.300000   7.700000  0.720000\n",
       "4    2   84.000000  6.000000   4.600000  0.790000\n",
       "38   9  158.000000  7.200000   7.800000  0.757407\n",
       "35   9  150.000000  7.058182   7.900000  0.750000\n",
       "34   9  142.000000  7.600000   7.800000  0.750000\n",
       "49  11  132.000000  5.800000   8.700000  0.730000\n",
       "7    2   76.000000  5.800000   4.000000  0.810000\n",
       "3    2   86.000000  6.200000   7.677778  0.800000\n",
       "33   9  164.269231  7.500000   8.100000  0.740000\n",
       "0    1  192.000000  8.400000   7.300000  0.550000\n",
       "52  11  118.000000  5.900000   8.000000  0.720000\n",
       "53  11  120.000000  6.000000   8.400000  0.740000\n",
       "9    3  172.000000  7.058182   7.000000  0.890000\n",
       "31   8  210.000000  7.800000   8.000000  0.820000\n",
       "50  11  164.269231  6.000000   8.200000  0.710000\n",
       "13   4  164.000000  7.300000   7.700000  0.700000\n",
       "2    1  176.000000  7.400000   7.200000  0.600000\n",
       "40   9  154.000000  7.100000   7.500000  0.780000\n",
       "23   5  170.000000  7.600000   7.900000  0.880000\n",
       "16   4  156.000000  7.600000   7.677778  0.670000\n",
       "32   8  164.000000  7.200000   7.000000  0.800000\n",
       "46  10  216.000000  7.300000  10.200000  0.710000\n",
       "19   5  162.000000  7.400000   7.200000  0.850000\n",
       "5    2   80.000000  5.800000   4.300000  0.770000\n",
       "22   5  140.000000  7.300000   7.100000  0.870000\n",
       "24   7  342.000000  7.058182   9.400000  0.750000\n",
       "58  11  118.000000  6.100000   8.100000  0.700000"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>8</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5</td>\n",
       "      <td>164.269231</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.860000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>164.269231</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.757407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>10</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>7</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.740000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>11</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A           B    C     D         E\n",
       "29   8  160.000000  7.0   7.4  0.810000\n",
       "20   5  164.269231  7.5   7.5  0.860000\n",
       "8    3  164.269231  7.1   7.8  0.757407\n",
       "44  10  200.000000  7.3  10.5  0.720000\n",
       "26   7  362.000000  9.6   9.2  0.740000\n",
       "51  11  116.000000  6.0   7.5  0.720000"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47    4\n",
       "42    3\n",
       "45    4\n",
       "15    1\n",
       "21    1\n",
       "39    3\n",
       "30    3\n",
       "25    3\n",
       "54    4\n",
       "10    1\n",
       "11    1\n",
       "27    3\n",
       "1     1\n",
       "43    4\n",
       "37    3\n",
       "12    1\n",
       "17    1\n",
       "18    1\n",
       "57    4\n",
       "41    3\n",
       "28    3\n",
       "56    4\n",
       "6     2\n",
       "14    1\n",
       "48    4\n",
       "36    3\n",
       "55    4\n",
       "4     2\n",
       "38    3\n",
       "35    3\n",
       "34    3\n",
       "49    4\n",
       "7     2\n",
       "3     2\n",
       "33    3\n",
       "0     1\n",
       "52    4\n",
       "53    4\n",
       "9     1\n",
       "31    3\n",
       "50    4\n",
       "13    1\n",
       "2     1\n",
       "40    3\n",
       "23    1\n",
       "16    1\n",
       "32    3\n",
       "46    4\n",
       "19    1\n",
       "5     2\n",
       "22    1\n",
       "24    3\n",
       "58    4\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29    3\n",
       "20    1\n",
       "8     1\n",
       "44    4\n",
       "26    3\n",
       "51    4\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "import The MLP Classifier from sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MLP দিয়ে ক্লাসিফাই করা"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(max_iter=500)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp = MLPClassifier(max_iter = 500, activation = 'relu')\n",
    "mlp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\Anaconda\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MLPClassifier(max_iter=500)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 1, 4, 3, 4], dtype=int64)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = mlp.predict(x_test)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2 0 0]\n",
      " [0 2 0]\n",
      " [0 0 2]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "print(confusion_matrix(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00         2\n",
      "           3       1.00      1.00      1.00         2\n",
      "           4       1.00      1.00      1.00         2\n",
      "\n",
      "    accuracy                           1.00         6\n",
      "   macro avg       1.00      1.00      1.00         6\n",
      "weighted avg       1.00      1.00      1.00         6\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0, 0],\n",
       "       [0, 2, 0],\n",
       "       [0, 0, 2]], dtype=int64)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "rfc=RandomForestClassifier()\n",
    "model3 = rfc.fit(x_train, y_train)\n",
    "pred3 = model3.predict(x_test)\n",
    "confusion_matrix(y_test, pred3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, pred3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00         2\n",
      "           3       1.00      1.00      1.00         2\n",
      "           4       1.00      1.00      1.00         2\n",
      "\n",
      "    accuracy                           1.00         6\n",
      "   macro avg       1.00      1.00      1.00         6\n",
      "weighted avg       1.00      1.00      1.00         6\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtc = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc.fit(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc.fit(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = dtc.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0, 0],\n",
       "       [0, 2, 0],\n",
       "       [0, 0, 2]], dtype=int64)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred=model.predict(x_test)\n",
    "confusion_matrix(y_test,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test,pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr=LogisticRegression()\n",
    "\n",
    "model2=lr.fit(x_train,y_train)\n",
    "pred1=model2.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0, 0],\n",
       "       [0, 2, 0],\n",
       "       [0, 0, 2]], dtype=int64)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm1=confusion_matrix(y_test,pred1)\n",
    "cm1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "TP=cm1[0][0]\n",
    "TN=cm1[1][1]\n",
    "FN=cm1[1][0]\n",
    "FP=cm1[0][1]\n",
    "print('Testing Accuracy:',(TP+TN)/(TP+TN+FN+FP))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test,pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00         2\n",
      "           3       1.00      1.00      1.00         2\n",
      "           4       1.00      1.00      1.00         2\n",
      "\n",
      "    accuracy                           1.00         6\n",
      "   macro avg       1.00      1.00      1.00         6\n",
      "weighted avg       1.00      1.00      1.00         6\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes classifier (GaussianNB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0, 0],\n",
       "       [0, 2, 0],\n",
       "       [0, 0, 2]], dtype=int64)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "NB = GaussianNB()\n",
    "model4 = NB.fit(x_train, y_train)\n",
    "pred4 = model4.predict(x_test)\n",
    "confusion_matrix(y_test, pred4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, pred4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0, 0],\n",
       "       [0, 2, 0],\n",
       "       [0, 1, 1]], dtype=int64)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "KNN = KNeighborsClassifier()\n",
    "model5 = KNN.fit(x_train, y_train)\n",
    "pred5 = model5.predict(x_test)\n",
    "confusion_matrix(y_test, pred5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, pred5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support vector machine(SVC -Support Vector Classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0, 0],\n",
       "       [1, 1, 0],\n",
       "       [2, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svm=SVC()\n",
    "model6=svm.fit(x_train,y_train)\n",
    "pred6=model6.predict(x_test)\n",
    "confusion_matrix(y_test,pred6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, pred6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN : 0.8333333333333334\n",
      "lr : 1.0\n",
      "dtc : 1.0\n",
      "rfc : 1.0\n",
      "NB:  1.0\n",
      "SVC : 0.5\n"
     ]
    }
   ],
   "source": [
    "print('KNN :', accuracy_score(y_test, pred5))\n",
    "print('lr :', accuracy_score(y_test, pred1))\n",
    "print('dtc :', accuracy_score(y_test, pred))\n",
    "print('rfc :', accuracy_score(y_test, pred3))\n",
    "print('NB: ', accuracy_score(y_test, pred4))\n",
    "print('SVC :', accuracy_score(y_test,pred6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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