Overfit_&_Underfit.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#**How to prevent Overfitting and Underfitting**"
      ],
      "metadata": {
        "id": "MSg3Dvm_QYOP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "fWk3TesXFecm"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/fruit_dataset.csv')"
      ],
      "metadata": {
        "id": "6DkXU7bXFwSW"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(50)"
      ],
      "metadata": {
        "id": "xm_mfNBIG17A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4476dd94-c0a1-49b8-9cc7-0f4bc0c1e597"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    fruit_label fruit_name  mass  width  height  color_score\n",
              "0             1      apple   192    8.4     7.3         0.55\n",
              "1             1      apple   180    8.0     6.8         0.59\n",
              "2             1      apple   176    7.4     7.2         0.60\n",
              "3             2   mandarin    86    6.2     4.7         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      apple   178    7.1     7.8         0.92\n",
              "9             1      apple   172    7.4     7.0         0.89\n",
              "10            1      apple   166    6.9     7.3         0.93\n",
              "11            1      apple   172    7.1     7.6         0.92\n",
              "12            1      apple   154    7.0     7.1         0.88\n",
              "13            1      apple   164    7.3     7.7         0.70\n",
              "14            1      apple   152    7.6     7.3         0.69\n",
              "15            1      apple   156    7.7     7.1         0.69\n",
              "16            1      apple   156    7.6     7.5         0.67\n",
              "17            1      apple   168    7.5     7.6         0.73\n",
              "18            1      apple   162    7.5     7.1         0.83\n",
              "19            1      apple   162    7.4     7.2         0.85\n",
              "20            1      apple   160    7.5     7.5         0.86\n",
              "21            1      apple   156    7.4     7.4         0.84\n",
              "22            1      apple   140    7.3     7.1         0.87\n",
              "23            1      apple   170    7.6     7.9         0.88\n",
              "24            3     orange   342    9.0     9.4         0.75\n",
              "25            3     orange   356    9.2     9.2         0.75\n",
              "26            3     orange   362    9.6     9.2         0.74\n",
              "27            3     orange   204    7.5     9.2         0.77\n",
              "28            3     orange   140    6.7     7.1         0.72\n",
              "29            3     orange   160    7.0     7.4         0.81\n",
              "30            3     orange   158    7.1     7.5         0.79\n",
              "31            3     orange   210    7.8     8.0         0.82\n",
              "32            3     orange   164    7.2     7.0         0.80\n",
              "33            3     orange   190    7.5     8.1         0.74\n",
              "34            3     orange   142    7.6     7.8         0.75\n",
              "35            3     orange   150    7.1     7.9         0.75\n",
              "36            3     orange   160    7.1     7.6         0.76\n",
              "37            3     orange   154    7.3     7.3         0.79\n",
              "38            3     orange   158    7.2     7.8         0.77\n",
              "39            3     orange   144    6.8     7.4         0.75\n",
              "40            3     orange   154    7.1     7.5         0.78\n",
              "41            3     orange   180    7.6     8.2         0.79\n",
              "42            3     orange   154    7.2     7.2         0.82\n",
              "43            4      lemon   194    7.2    10.3         0.70\n",
              "44            4      lemon   200    7.3    10.5         0.72\n",
              "45            4      lemon   186    7.2     9.2         0.72\n",
              "46            4      lemon   216    7.3    10.2         0.71\n",
              "47            4      lemon   196    7.3     9.7         0.72\n",
              "48            4      lemon   174    7.3    10.1         0.72\n",
              "49            4      lemon   132    5.8     8.7         0.73"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eee3d0bb-feba-4a98-999d-188f154c5f44\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fruit_label</th>\n",
              "      <th>fruit_name</th>\n",
              "      <th>mass</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>color_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>apple</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>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>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>4.7</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>apple</td>\n",
              "      <td>178</td>\n",
              "      <td>7.1</td>\n",
              "      <td>7.8</td>\n",
              "      <td>0.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>apple</td>\n",
              "      <td>172</td>\n",
              "      <td>7.4</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>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>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>154</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.1</td>\n",
              "      <td>0.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1</td>\n",
              "      <td>apple</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>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>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>156</td>\n",
              "      <td>7.6</td>\n",
              "      <td>7.5</td>\n",
              "      <td>0.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1</td>\n",
              "      <td>apple</td>\n",
              "      <td>168</td>\n",
              "      <td>7.5</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>162</td>\n",
              "      <td>7.5</td>\n",
              "      <td>7.1</td>\n",
              "      <td>0.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1</td>\n",
              "      <td>apple</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>160</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>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>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>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>342</td>\n",
              "      <td>9.0</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>356</td>\n",
              "      <td>9.2</td>\n",
              "      <td>9.2</td>\n",
              "      <td>0.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>3</td>\n",
              "      <td>orange</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>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>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>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>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>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>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>190</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>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>150</td>\n",
              "      <td>7.1</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>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>154</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>158</td>\n",
              "      <td>7.2</td>\n",
              "      <td>7.8</td>\n",
              "      <td>0.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>3</td>\n",
              "      <td>orange</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>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>180</td>\n",
              "      <td>7.6</td>\n",
              "      <td>8.2</td>\n",
              "      <td>0.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>3</td>\n",
              "      <td>orange</td>\n",
              "      <td>154</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>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>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>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>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>196</td>\n",
              "      <td>7.3</td>\n",
              "      <td>9.7</td>\n",
              "      <td>0.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>4</td>\n",
              "      <td>lemon</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>132</td>\n",
              "      <td>5.8</td>\n",
              "      <td>8.7</td>\n",
              "      <td>0.73</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eee3d0bb-feba-4a98-999d-188f154c5f44')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-eee3d0bb-feba-4a98-999d-188f154c5f44 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-eee3d0bb-feba-4a98-999d-188f154c5f44');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['fruit_name'].value_counts()"
      ],
      "metadata": {
        "id": "AODeuN4KG3LU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dae2512d-e0d1-4457-e62e-09360b0c32cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "apple       19\n",
              "orange      19\n",
              "lemon       16\n",
              "mandarin     5\n",
              "mango        2\n",
              "Name: fruit_name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['fruit_label'].value_counts()"
      ],
      "metadata": {
        "id": "Hp9NFCsMHCiO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "06c14b6e-b14d-4ada-9276-eb8b7fe1e288"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    19\n",
              "3    19\n",
              "4    16\n",
              "2     5\n",
              "5     2\n",
              "Name: fruit_label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.iloc[:,2:5]\n",
        "y= df.iloc[:,0]"
      ],
      "metadata": {
        "id": "QvY_rbcKH-v_"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x)\n",
        "print(y)"
      ],
      "metadata": {
        "id": "Uc9vQfELIQcU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1429a310-2f3c-4c8b-cec3-901220dbcd33"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    mass  width  height\n",
            "0    192    8.4    7.30\n",
            "1    180    8.0    6.80\n",
            "2    176    7.4    7.20\n",
            "3     86    6.2    4.70\n",
            "4     84    6.0    4.60\n",
            "..   ...    ...     ...\n",
            "56   116    5.9    8.10\n",
            "57   152    6.5    8.50\n",
            "58   118    6.1    8.10\n",
            "59   201    7.7   10.10\n",
            "60   209    8.1   12.11\n",
            "\n",
            "[61 rows x 3 columns]\n",
            "0     1\n",
            "1     1\n",
            "2     1\n",
            "3     2\n",
            "4     2\n",
            "     ..\n",
            "56    4\n",
            "57    4\n",
            "58    4\n",
            "59    5\n",
            "60    5\n",
            "Name: fruit_label, Length: 61, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Preventing Overfitting:"
      ],
      "metadata": {
        "id": "LgXkUzKHRHK8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1. Using Cross validation:**\n",
        "\n",
        "Cross validation is a very powerful preventive measure against overfitting, with a clever idea. Create multiple mini train-validation splits within the original train data, and use these to tune your model.\n",
        "\n",
        "We have a standard way of doing so, called the “k-fold cross validation”. What we do here is, partition the dataset into k subsets which we call “folds”.\n",
        "\n",
        "Then we iteratively train our model on k – 1 folds. Why? We keep the last one as a validation dataset. The last fold is called the “holdout fold”.\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "\n",
        "\n",
        "Using cross validation, you can tune your hyperparameters only with your original training dataset. This way, you can keep the validation set as a completely unseen dataset.\n",
        "\n",
        "K-fold cross validation might not completely remove the overfitting, so we can change the folds every now and then, or use multiple k-fold cross validations together.\n",
        "\n",
        "**2. Train with more data:**\n",
        "\n",
        "The signal will be better detected by the algorithm if we train the model with more data. Although it doesn’t work every time, for example if we just add more noisy data, then this technique won’t help.\n",
        "\n",
        "**3. Remove Features**\n",
        "\n",
        "In algorithms that don’t have a built-in feature selection, their generalization can be manually improved by removing some irrelevant or unimportant features. Why is removing features helpful? Sometimes it may happen that the model may fail to generalize simply because the model missed the patterns that should have been detected, and the data was too complex for the same.\n",
        "\n",
        "**4. Add Regularization:**\n",
        "\n",
        "As we discussed earlier, that overfitting could be a consequence of the model being too complex. Can we forcefully make it simpler? Yes! Regularization is the term for a range of techniques that could be used to force your model into being simpler. The techniques used to regularize any model will depend on the model itself. For example, an option could be pruning a decision tree, on neural networks you could use a dropout, or, to the cost function in regression, a penalty parameter could be added. Confusing terms eh? Google them!\n",
        "\n"
      ],
      "metadata": {
        "id": "UqaZwIxNRa36"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "\n",
        "Identify Overfitting in Our Dataset"
      ],
      "metadata": {
        "id": "z-lL8r82QczW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size = .20, random_state=1)\n",
        "\n"
      ],
      "metadata": {
        "id": "LutaOZWFHHrl"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "lr = LogisticRegression()\n",
        "\n",
        "lr.fit(x_train,y_train)"
      ],
      "metadata": {
        "id": "gUgUP9hDIjgA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "449bd9b9-31cc-46b9-edb5-9eb81e6847ac"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: 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",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "\n",
        "print('training score: ',lr.score(x_train,y_train))\n",
        "print('test score: ',lr.score(x_test,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ISctB8dgJYzg",
        "outputId": "2639dcde-19f9-4623-e0fb-a6606aa7c446"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "training score:  0.7291666666666666\n",
            "test score:  0.46153846153846156\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['fruit_name'].value_counts()"
      ],
      "metadata": {
        "id": "wECMLamOKRM-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9e45cbd-cd3b-4325-9d89-6c5347198bd8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "apple       19\n",
              "orange      19\n",
              "lemon       16\n",
              "mandarin     5\n",
              "mango        2\n",
              "Name: fruit_name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mZ04SbfSO3ik"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Solving Overfitting in Our Dataset** : Train with more data"
      ],
      "metadata": {
        "id": "pC-AVupNQp-x"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Resampling Techniques"
      ],
      "metadata": {
        "id": "IldOQwc3U9I-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can change the dataset that we are using to build our predictive model to have more balanced data.\n",
        "\n",
        "This approach is normally referred to as sampling the dataset. There are two main methods that are typically used.\n",
        "\n",
        "\n",
        "1. **Upsampling/OverSampling**\n",
        "2. **DownSampling/UnderSampling**\n",
        "\n",
        "Since our dataset is small and we want to increase it we will use *Upsampling/OverSampling* techique:"
      ],
      "metadata": {
        "id": "-vXuJJz0Us8R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src= 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      ],
      "metadata": {
        "id": "JCCWeloqVG7V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Upsample Mandarin"
      ],
      "metadata": {
        "id": "wNBRppFRN1vN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# separate minority and majority classes\n",
        "from sklearn.utils import resample\n",
        "\n",
        "mandarin = df[df.fruit_label==2]\n",
        "apple = df[df.fruit_label==1]\n",
        "\n",
        "# upsample minority\n",
        "mandarin_data_upsampled = resample(mandarin,\n",
        "                          replace=True, # sample with replacement\n",
        "                          n_samples= len(apple)-5,    #len(apple), # match number in majority class\n",
        "                          random_state=67) # reproducible results"
      ],
      "metadata": {
        "id": "OuWmSXUlSXSc"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mandarin_data_upsampled"
      ],
      "metadata": {
        "id": "47nv3y2IV7_D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "cf340179-eece-46e8-eaf4-992e772357b8"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   fruit_label fruit_name  mass  width  height  color_score\n",
              "6            2   mandarin    80    5.9     4.3         0.81\n",
              "5            2   mandarin    80    5.8     4.3         0.77\n",
              "4            2   mandarin    84    6.0     4.6         0.79\n",
              "4            2   mandarin    84    6.0     4.6         0.79\n",
              "6            2   mandarin    80    5.9     4.3         0.81\n",
              "6            2   mandarin    80    5.9     4.3         0.81\n",
              "4            2   mandarin    84    6.0     4.6         0.79\n",
              "6            2   mandarin    80    5.9     4.3         0.81\n",
              "3            2   mandarin    86    6.2     4.7         0.80\n",
              "6            2   mandarin    80    5.9     4.3         0.81\n",
              "7            2   mandarin    76    5.8     4.0         0.81\n",
              "4            2   mandarin    84    6.0     4.6         0.79\n",
              "5            2   mandarin    80    5.8     4.3         0.77\n",
              "5            2   mandarin    80    5.8     4.3         0.77"
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        buttonEl.style.display =\n",
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              "\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mandarin_data_upsampled = mandarin_data_upsampled.reset_index(drop=True)"
      ],
      "metadata": {
        "id": "_1lQszreNhyv"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mandarin_data_upsampled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "R5vjSt51NuEj",
        "outputId": "029ba9bd-34aa-4f2c-c062-c842052d3bf6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    fruit_label fruit_name  mass  width  height  color_score\n",
              "0             2   mandarin    80    5.9     4.3         0.81\n",
              "1             2   mandarin    80    5.8     4.3         0.77\n",
              "2             2   mandarin    84    6.0     4.6         0.79\n",
              "3             2   mandarin    84    6.0     4.6         0.79\n",
              "4             2   mandarin    80    5.9     4.3         0.81\n",
              "5             2   mandarin    80    5.9     4.3         0.81\n",
              "6             2   mandarin    84    6.0     4.6         0.79\n",
              "7             2   mandarin    80    5.9     4.3         0.81\n",
              "8             2   mandarin    86    6.2     4.7         0.80\n",
              "9             2   mandarin    80    5.9     4.3         0.81\n",
              "10            2   mandarin    76    5.8     4.0         0.81\n",
              "11            2   mandarin    84    6.0     4.6         0.79\n",
              "12            2   mandarin    80    5.8     4.3         0.77\n",
              "13            2   mandarin    80    5.8     4.3         0.77"
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2</td>\n",
              "      <td>mandarin</td>\n",
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              "      <th>10</th>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bf383ba4-4b41-4288-91f0-d5604b7911b9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Frame =[df,mandarin_data_upsampled]\n",
        "\n",
        "df = pd.concat(Frame,axis = 0)\n",
        "\n",
        "df"
      ],
      "metadata": {
        "id": "_zooEqOvYqD7",
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          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "b13c2bce-b6ae-49d0-dba3-82cdf2ad472b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    fruit_label fruit_name  mass  width  height  color_score\n",
              "0             1      apple   192    8.4     7.3         0.55\n",
              "1             1      apple   180    8.0     6.8         0.59\n",
              "2             1      apple   176    7.4     7.2         0.60\n",
              "3             2   mandarin    86    6.2     4.7         0.80\n",
              "4             2   mandarin    84    6.0     4.6         0.79\n",
              "..          ...        ...   ...    ...     ...          ...\n",
              "9             2   mandarin    80    5.9     4.3         0.81\n",
              "10            2   mandarin    76    5.8     4.0         0.81\n",
              "11            2   mandarin    84    6.0     4.6         0.79\n",
              "12            2   mandarin    80    5.8     4.3         0.77\n",
              "13            2   mandarin    80    5.8     4.3         0.77\n",
              "\n",
              "[75 rows x 6 columns]"
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              "      <th>12</th>\n",
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              "      <td>80</td>\n",
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              "      <td>4.3</td>\n",
              "      <td>0.77</td>\n",
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              "      <th>13</th>\n",
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              "      <td>mandarin</td>\n",
              "      <td>80</td>\n",
              "      <td>5.8</td>\n",
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              "      <td>0.77</td>\n",
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              "<p>75 rows × 6 columns</p>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail(50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FMz5T8udOJj_",
        "outputId": "928a99a7-7d42-4422-db59-843498b26386"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    fruit_label fruit_name  mass  width  height  color_score\n",
              "25            3     orange   356    9.2    9.20         0.75\n",
              "26            3     orange   362    9.6    9.20         0.74\n",
              "27            3     orange   204    7.5    9.20         0.77\n",
              "28            3     orange   140    6.7    7.10         0.72\n",
              "29            3     orange   160    7.0    7.40         0.81\n",
              "30            3     orange   158    7.1    7.50         0.79\n",
              "31            3     orange   210    7.8    8.00         0.82\n",
              "32            3     orange   164    7.2    7.00         0.80\n",
              "33            3     orange   190    7.5    8.10         0.74\n",
              "34            3     orange   142    7.6    7.80         0.75\n",
              "35            3     orange   150    7.1    7.90         0.75\n",
              "36            3     orange   160    7.1    7.60         0.76\n",
              "37            3     orange   154    7.3    7.30         0.79\n",
              "38            3     orange   158    7.2    7.80         0.77\n",
              "39            3     orange   144    6.8    7.40         0.75\n",
              "40            3     orange   154    7.1    7.50         0.78\n",
              "41            3     orange   180    7.6    8.20         0.79\n",
              "42            3     orange   154    7.2    7.20         0.82\n",
              "43            4      lemon   194    7.2   10.30         0.70\n",
              "44            4      lemon   200    7.3   10.50         0.72\n",
              "45            4      lemon   186    7.2    9.20         0.72\n",
              "46            4      lemon   216    7.3   10.20         0.71\n",
              "47            4      lemon   196    7.3    9.70         0.72\n",
              "48            4      lemon   174    7.3   10.10         0.72\n",
              "49            4      lemon   132    5.8    8.70         0.73\n",
              "50            4      lemon   130    6.0    8.20         0.71\n",
              "51            4      lemon   116    6.0    7.50         0.72\n",
              "52            4      lemon   118    5.9    8.00         0.72\n",
              "53            4      lemon   120    6.0    8.40         0.74\n",
              "54            4      lemon   116    6.1    8.50         0.71\n",
              "55            4      lemon   116    6.3    7.70         0.72\n",
              "56            4      lemon   116    5.9    8.10         0.73\n",
              "57            4      lemon   152    6.5    8.50         0.72\n",
              "58            4      lemon   118    6.1    8.10         0.70\n",
              "59            5      mango   201    7.7   10.10         0.71\n",
              "60            5      mango   209    8.1   12.11         0.78\n",
              "0             2   mandarin    80    5.9    4.30         0.81\n",
              "1             2   mandarin    80    5.8    4.30         0.77\n",
              "2             2   mandarin    84    6.0    4.60         0.79\n",
              "3             2   mandarin    84    6.0    4.60         0.79\n",
              "4             2   mandarin    80    5.9    4.30         0.81\n",
              "5             2   mandarin    80    5.9    4.30         0.81\n",
              "6             2   mandarin    84    6.0    4.60         0.79\n",
              "7             2   mandarin    80    5.9    4.30         0.81\n",
              "8             2   mandarin    86    6.2    4.70         0.80\n",
              "9             2   mandarin    80    5.9    4.30         0.81\n",
              "10            2   mandarin    76    5.8    4.00         0.81\n",
              "11            2   mandarin    84    6.0    4.60         0.79\n",
              "12            2   mandarin    80    5.8    4.30         0.77\n",
              "13            2   mandarin    80    5.8    4.30         0.77"
            ],
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              "      <th>30</th>\n",
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              "      <td>7.50</td>\n",
              "      <td>0.79</td>\n",
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              "      <th>31</th>\n",
              "      <td>3</td>\n",
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              "      <td>210</td>\n",
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              "      <td>8.00</td>\n",
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              "      <th>32</th>\n",
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              "      <th>33</th>\n",
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              "      <td>7.5</td>\n",
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              "      <th>34</th>\n",
              "      <td>3</td>\n",
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              "      <td>142</td>\n",
              "      <td>7.6</td>\n",
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              "      <td>150</td>\n",
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              "      <td>7.90</td>\n",
              "      <td>0.75</td>\n",
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              "      <th>36</th>\n",
              "      <td>3</td>\n",
              "      <td>orange</td>\n",
              "      <td>160</td>\n",
              "      <td>7.1</td>\n",
              "      <td>7.60</td>\n",
              "      <td>0.76</td>\n",
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              "      <th>37</th>\n",
              "      <td>3</td>\n",
              "      <td>orange</td>\n",
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              "      <td>7.3</td>\n",
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              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>3</td>\n",
              "      <td>orange</td>\n",
              "      <td>158</td>\n",
              "      <td>7.2</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>3</td>\n",
              "      <td>orange</td>\n",
              "      <td>144</td>\n",
              "      <td>6.8</td>\n",
              "      <td>7.40</td>\n",
              "      <td>0.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>3</td>\n",
              "      <td>orange</td>\n",
              "      <td>154</td>\n",
              "      <td>7.1</td>\n",
              "      <td>7.50</td>\n",
              "      <td>0.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>3</td>\n",
              "      <td>orange</td>\n",
              "      <td>180</td>\n",
              "      <td>7.6</td>\n",
              "      <td>8.20</td>\n",
              "      <td>0.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>3</td>\n",
              "      <td>orange</td>\n",
              "      <td>154</td>\n",
              "      <td>7.2</td>\n",
              "      <td>7.20</td>\n",
              "      <td>0.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>4</td>\n",
              "      <td>lemon</td>\n",
              "      <td>194</td>\n",
              "      <td>7.2</td>\n",
              "      <td>10.30</td>\n",
              "      <td>0.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>4</td>\n",
              "      <td>lemon</td>\n",
              "      <td>200</td>\n",
              "      <td>7.3</td>\n",
              "      <td>10.50</td>\n",
              "      <td>0.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>4</td>\n",
              "      <td>lemon</td>\n",
              "      <td>186</td>\n",
              "      <td>7.2</td>\n",
              "      <td>9.20</td>\n",
              "      <td>0.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>4</td>\n",
              "      <td>lemon</td>\n",
              "      <td>216</td>\n",
              "      <td>7.3</td>\n",
              "      <td>10.20</td>\n",
              "      <td>0.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>4</td>\n",
              "      <td>lemon</td>\n",
              "      <td>196</td>\n",
              "      <td>7.3</td>\n",
              "      <td>9.70</td>\n",
              "      <td>0.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>4</td>\n",
              "      <td>lemon</td>\n",
              "      <td>174</td>\n",
              "      <td>7.3</td>\n",
              "      <td>10.10</td>\n",
              "      <td>0.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>4</td>\n",
              "      <td>lemon</td>\n",
              "      <td>132</td>\n",
              "      <td>5.8</td>\n",
              "      <td>8.70</td>\n",
              "      <td>0.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>4</td>\n",
              "      <td>lemon</td>\n",
              "      <td>130</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8.20</td>\n",
              "      <td>0.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>4</td>\n",
              "      <td>lemon</td>\n",
              "      <td>116</td>\n",
              "      <td>6.0</td>\n",
              "      <td>7.50</td>\n",
              "      <td>0.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>4</td>\n",
              "      <td>lemon</td>\n",
              "      <td>118</td>\n",
              "      <td>5.9</td>\n",
              "      <td>8.00</td>\n",
              "      <td>0.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>4</td>\n",
              "      <td>lemon</td>\n",
              "      <td>120</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8.40</td>\n",
              "      <td>0.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>4</td>\n",
              "      <td>lemon</td>\n",
              "      <td>116</td>\n",
              "      <td>6.1</td>\n",
              "      <td>8.50</td>\n",
              "      <td>0.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>4</td>\n",
              "      <td>lemon</td>\n",
              "      <td>116</td>\n",
              "      <td>6.3</td>\n",
              "      <td>7.70</td>\n",
              "      <td>0.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>4</td>\n",
              "      <td>lemon</td>\n",
              "      <td>116</td>\n",
              "      <td>5.9</td>\n",
              "      <td>8.10</td>\n",
              "      <td>0.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>4</td>\n",
              "      <td>lemon</td>\n",
              "      <td>152</td>\n",
              "      <td>6.5</td>\n",
              "      <td>8.50</td>\n",
              "      <td>0.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>4</td>\n",
              "      <td>lemon</td>\n",
              "      <td>118</td>\n",
              "      <td>6.1</td>\n",
              "      <td>8.10</td>\n",
              "      <td>0.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>5</td>\n",
              "      <td>mango</td>\n",
              "      <td>201</td>\n",
              "      <td>7.7</td>\n",
              "      <td>10.10</td>\n",
              "      <td>0.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>5</td>\n",
              "      <td>mango</td>\n",
              "      <td>209</td>\n",
              "      <td>8.1</td>\n",
              "      <td>12.11</td>\n",
              "      <td>0.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>mandarin</td>\n",
              "      <td>80</td>\n",
              "      <td>5.9</td>\n",
              "      <td>4.30</td>\n",
              "      <td>0.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>mandarin</td>\n",
              "      <td>80</td>\n",
              "      <td>5.8</td>\n",
              "      <td>4.30</td>\n",
              "      <td>0.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>mandarin</td>\n",
              "      <td>84</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.60</td>\n",
              "      <td>0.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>mandarin</td>\n",
              "      <td>84</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.60</td>\n",
              "      <td>0.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>mandarin</td>\n",
              "      <td>80</td>\n",
              "      <td>5.9</td>\n",
              "      <td>4.30</td>\n",
              "      <td>0.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2</td>\n",
              "      <td>mandarin</td>\n",
              "      <td>80</td>\n",
              "      <td>5.9</td>\n",
              "      <td>4.30</td>\n",
              "      <td>0.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2</td>\n",
              "      <td>mandarin</td>\n",
              "      <td>84</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.60</td>\n",
              "      <td>0.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2</td>\n",
              "      <td>mandarin</td>\n",
              "      <td>80</td>\n",
              "      <td>5.9</td>\n",
              "      <td>4.30</td>\n",
              "      <td>0.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2</td>\n",
              "      <td>mandarin</td>\n",
              "      <td>86</td>\n",
              "      <td>6.2</td>\n",
              "      <td>4.70</td>\n",
              "      <td>0.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2</td>\n",
              "      <td>mandarin</td>\n",
              "      <td>80</td>\n",
              "      <td>5.9</td>\n",
              "      <td>4.30</td>\n",
              "      <td>0.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2</td>\n",
              "      <td>mandarin</td>\n",
              "      <td>76</td>\n",
              "      <td>5.8</td>\n",
              "      <td>4.00</td>\n",
              "      <td>0.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2</td>\n",
              "      <td>mandarin</td>\n",
              "      <td>84</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.60</td>\n",
              "      <td>0.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2</td>\n",
              "      <td>mandarin</td>\n",
              "      <td>80</td>\n",
              "      <td>5.8</td>\n",
              "      <td>4.30</td>\n",
              "      <td>0.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2</td>\n",
              "      <td>mandarin</td>\n",
              "      <td>80</td>\n",
              "      <td>5.8</td>\n",
              "      <td>4.30</td>\n",
              "      <td>0.77</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['fruit_name'].value_counts()"
      ],
      "metadata": {
        "id": "acbEVMsAY30F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "57c59499-37f9-44d3-84ff-10b22b8a3aed"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "apple       19\n",
              "mandarin    19\n",
              "orange      19\n",
              "lemon       16\n",
              "mango        2\n",
              "Name: fruit_name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# separate minority and majority classes\n",
        "from sklearn.utils import resample\n",
        "\n",
        "mango = df[df.fruit_label==5]\n",
        "apple = df[df.fruit_label==1]\n",
        "\n",
        "# upsample minority\n",
        "mango_data_upsampled = resample(mango,\n",
        "                          replace=True, # sample with replacement\n",
        "                          n_samples= len(apple)-2,    #len(apple), # match number in majority class\n",
        "                          random_state=67) # reproducible results"
      ],
      "metadata": {
        "id": "2nkBtNftXHcD"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mango_data_upsampled"
      ],
      "metadata": {
        "id": "bY0gcc8eitrQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "outputId": "640f304a-6b08-4fea-846a-60003f18a8ff"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    fruit_label fruit_name  mass  width  height  color_score\n",
              "60            5      mango   209    8.1   12.11         0.78\n",
              "60            5      mango   209    8.1   12.11         0.78\n",
              "59            5      mango   201    7.7   10.10         0.71\n",
              "60            5      mango   209    8.1   12.11         0.78\n",
              "60            5      mango   209    8.1   12.11         0.78\n",
              "60            5      mango   209    8.1   12.11         0.78\n",
              "60            5      mango   209    8.1   12.11         0.78\n",
              "60            5      mango   209    8.1   12.11         0.78\n",
              "60            5      mango   209    8.1   12.11         0.78\n",
              "59            5      mango   201    7.7   10.10         0.71\n",
              "60            5      mango   209    8.1   12.11         0.78\n",
              "60            5      mango   209    8.1   12.11         0.78\n",
              "60            5      mango   209    8.1   12.11         0.78\n",
              "60            5      mango   209    8.1   12.11         0.78\n",
              "59            5      mango   201    7.7   10.10         0.71\n",
              "60            5      mango   209    8.1   12.11         0.78\n",
              "60            5      mango   209    8.1   12.11         0.78"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-99608925-520c-463a-acae-79b93605b461\">\n",
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              "      <th></th>\n",
              "      <th>fruit_label</th>\n",
              "      <th>fruit_name</th>\n",
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              "      <th>height</th>\n",
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              "  <tbody>\n",
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              "      <th>60</th>\n",
              "      <td>5</td>\n",
              "      <td>mango</td>\n",
              "      <td>209</td>\n",
              "      <td>8.1</td>\n",
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              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>5</td>\n",
              "      <td>mango</td>\n",
              "      <td>209</td>\n",
              "      <td>8.1</td>\n",
              "      <td>12.11</td>\n",
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              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>5</td>\n",
              "      <td>mango</td>\n",
              "      <td>201</td>\n",
              "      <td>7.7</td>\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mango_data_upsampled = mango_data_upsampled.reset_index(drop=True)"
      ],
      "metadata": {
        "id": "tWM4cFl-J0hf"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mango_data_upsampled"
      ],
      "metadata": {
        "id": "gqKvYIbvOvNi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "outputId": "f2480970-8dec-4f47-acbb-b8e5ddc996a4"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    fruit_label fruit_name  mass  width  height  color_score\n",
              "0             5      mango   209    8.1   12.11         0.78\n",
              "1             5      mango   209    8.1   12.11         0.78\n",
              "2             5      mango   201    7.7   10.10         0.71\n",
              "3             5      mango   209    8.1   12.11         0.78\n",
              "4             5      mango   209    8.1   12.11         0.78\n",
              "5             5      mango   209    8.1   12.11         0.78\n",
              "6             5      mango   209    8.1   12.11         0.78\n",
              "7             5      mango   209    8.1   12.11         0.78\n",
              "8             5      mango   209    8.1   12.11         0.78\n",
              "9             5      mango   201    7.7   10.10         0.71\n",
              "10            5      mango   209    8.1   12.11         0.78\n",
              "11            5      mango   209    8.1   12.11         0.78\n",
              "12            5      mango   209    8.1   12.11         0.78\n",
              "13            5      mango   209    8.1   12.11         0.78\n",
              "14            5      mango   201    7.7   10.10         0.71\n",
              "15            5      mango   209    8.1   12.11         0.78\n",
              "16            5      mango   209    8.1   12.11         0.78"
            ],
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Frame =[df,mango_data_upsampled]\n",
        "\n",
        "df = pd.concat(Frame,axis = 0)\n",
        "\n",
        "df"
      ],
      "metadata": {
        "id": "1Mu48JJOOyrO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "5f0e751b-3042-4db6-dd00-b55e52be1a1b"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    fruit_label fruit_name  mass  width  height  color_score\n",
              "0             1      apple   192    8.4    7.30         0.55\n",
              "1             1      apple   180    8.0    6.80         0.59\n",
              "2             1      apple   176    7.4    7.20         0.60\n",
              "3             2   mandarin    86    6.2    4.70         0.80\n",
              "4             2   mandarin    84    6.0    4.60         0.79\n",
              "..          ...        ...   ...    ...     ...          ...\n",
              "12            5      mango   209    8.1   12.11         0.78\n",
              "13            5      mango   209    8.1   12.11         0.78\n",
              "14            5      mango   201    7.7   10.10         0.71\n",
              "15            5      mango   209    8.1   12.11         0.78\n",
              "16            5      mango   209    8.1   12.11         0.78\n",
              "\n",
              "[92 rows x 6 columns]"
            ],
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              "      <th>fruit_label</th>\n",
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              "      <th>height</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>apple</td>\n",
              "      <td>192</td>\n",
              "      <td>8.4</td>\n",
              "      <td>7.30</td>\n",
              "      <td>0.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>apple</td>\n",
              "      <td>180</td>\n",
              "      <td>8.0</td>\n",
              "      <td>6.80</td>\n",
              "      <td>0.59</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>apple</td>\n",
              "      <td>176</td>\n",
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              "      <td>7.20</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",
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              "      <td>0.80</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>mandarin</td>\n",
              "      <td>84</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>5</td>\n",
              "      <td>mango</td>\n",
              "      <td>209</td>\n",
              "      <td>8.1</td>\n",
              "      <td>12.11</td>\n",
              "      <td>0.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>5</td>\n",
              "      <td>mango</td>\n",
              "      <td>209</td>\n",
              "      <td>8.1</td>\n",
              "      <td>12.11</td>\n",
              "      <td>0.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>5</td>\n",
              "      <td>mango</td>\n",
              "      <td>201</td>\n",
              "      <td>7.7</td>\n",
              "      <td>10.10</td>\n",
              "      <td>0.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>5</td>\n",
              "      <td>mango</td>\n",
              "      <td>209</td>\n",
              "      <td>8.1</td>\n",
              "      <td>12.11</td>\n",
              "      <td>0.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>5</td>\n",
              "      <td>mango</td>\n",
              "      <td>209</td>\n",
              "      <td>8.1</td>\n",
              "      <td>12.11</td>\n",
              "      <td>0.78</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>92 rows × 6 columns</p>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['fruit_name'].value_counts()"
      ],
      "metadata": {
        "id": "BS5V-xjBPA_b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b794e8af-0593-418a-d0ea-e194df3eb219"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "apple       19\n",
              "mandarin    19\n",
              "orange      19\n",
              "mango       19\n",
              "lemon       16\n",
              "Name: fruit_name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.iloc[:,2:5]\n",
        "y= df.iloc[:,0]"
      ],
      "metadata": {
        "id": "6o-WNmN3PDJJ"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size = .20, random_state=1)\n",
        "\n"
      ],
      "metadata": {
        "id": "yIqA63C3PLm6"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "lr = LogisticRegression()\n",
        "\n",
        "lr.fit(x_train,y_train)"
      ],
      "metadata": {
        "id": "YpchCwThPxni",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae3d2205-a102-41bb-ef3f-5420aacd9c98"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: 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",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "\n",
        "print('training score: ',lr.score(x_train,y_train))\n",
        "print('test score: ',lr.score(x_test,y_test))"
      ],
      "metadata": {
        "id": "uKFqpWaHPQIF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "694ef28b-ced6-4d62-95e2-658069efa84a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "training score:  0.7671232876712328\n",
            "test score:  0.7894736842105263\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "s6-vETxPP0fA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kfj9kzzxP-o6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KEvuwyfFQE-b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we can see from the output that the overfitting problem is solved."
      ],
      "metadata": {
        "id": "_-zS87SvRnsp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check how to reduce Overfitting with [Regularization and Feature Selection](https://colab.research.google.com/drive/1dfNPxPbom8XY_b1reGc1MMMf8Znn3DW5?usp=sharing)"
      ],
      "metadata": {
        "id": "u2nMCZw5l34T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "<br>\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#**Preventing Underfitting:**\n",
        "\n",
        "**1. Increasing the Complexity of the model:**\n",
        "\n",
        "A probable cause of underfitting could be the fact that the model is not complex enough to understand the underlying patterns in data. Making the switch to say, a non-linear model from a linear model or, say, by adding more hidden layers to your existing neural network could be ways to make the model more complex, and in turn could help in removal of underfitting.\n",
        "\n",
        "**2. Reducing Regularization:**\n",
        "\n",
        "Well, underfitting is somewhat close to the opposite of overfitting. So, as we read earlier, regularization could help solve the overfitting problem, then reducing it could solve the underfitting problem! Some of the algorithms you use by default include some regularization parameters meant to suppress overfitting. Sometimes, these could also cause hindrance in the learning of the algorithm. Decreasing their values for the most part, makes a difference. You must be thinking, that if underfitting is almost the opposite of overfitting, then maybe adding more features or data would help solve the problem? NO! If the dataset lacks features that are decisive, and important that could help your model in detecting patterns, you can multiply the training data set by 2 or 5, or even 10, but it will not help in making your algorithm better. It is a common notion to think that throwing more data would solve the problem, but as stated earlier, it might just jeopardize the project."
      ],
      "metadata": {
        "id": "RPZJvu7kWSn2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "weather_data_p = pd.read_csv('/content/WeatherDataP.csv')\n",
        "weather_data_p"
      ],
      "metadata": {
        "id": "DzLwyWAhQIsE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "outputId": "37ffdf25-9d5b-440b-96d3-f79d048b434e"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Pressure (millibars)  Humidity\n",
              "0                1014.40      0.62\n",
              "1                1014.20      0.66\n",
              "2                1014.47      0.79\n",
              "3                1014.45      0.82\n",
              "4                1014.49      0.83\n",
              "5                1014.52      0.85\n",
              "6                1014.16      0.83\n",
              "7                1014.24      0.78\n",
              "8                1014.25      0.72\n",
              "9                1013.96      0.61\n",
              "10               1013.85      0.52\n",
              "11               1013.04      0.46\n",
              "12               1012.22      0.40\n",
              "13               1011.44      0.40\n",
              "14               1010.52      0.37\n",
              "15               1009.83      0.40\n",
              "16               1009.26      0.36\n",
              "17               1008.76      0.43\n",
              "18               1008.36      0.50\n",
              "19               1008.11      0.53\n",
              "20               1008.15      0.55\n",
              "21               1007.85      0.58\n",
              "22               1007.89      0.59\n",
              "23               1007.36      0.60\n",
              "24               1007.26      0.63"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ffa24fee-bf97-4bd0-b673-aa2bdba93e13\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pressure (millibars)</th>\n",
              "      <th>Humidity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1014.40</td>\n",
              "      <td>0.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1014.20</td>\n",
              "      <td>0.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1014.47</td>\n",
              "      <td>0.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1014.45</td>\n",
              "      <td>0.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1014.49</td>\n",
              "      <td>0.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1014.52</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1014.16</td>\n",
              "      <td>0.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1014.24</td>\n",
              "      <td>0.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1014.25</td>\n",
              "      <td>0.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1013.96</td>\n",
              "      <td>0.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1013.85</td>\n",
              "      <td>0.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1013.04</td>\n",
              "      <td>0.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1012.22</td>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1011.44</td>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1010.52</td>\n",
              "      <td>0.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1009.83</td>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1009.26</td>\n",
              "      <td>0.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1008.76</td>\n",
              "      <td>0.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1008.36</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1008.11</td>\n",
              "      <td>0.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1008.15</td>\n",
              "      <td>0.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1007.85</td>\n",
              "      <td>0.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1007.89</td>\n",
              "      <td>0.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1007.36</td>\n",
              "      <td>0.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1007.26</td>\n",
              "      <td>0.63</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ffa24fee-bf97-4bd0-b673-aa2bdba93e13')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ffa24fee-bf97-4bd0-b673-aa2bdba93e13 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ffa24fee-bf97-4bd0-b673-aa2bdba93e13');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = weather_data_p[['Pressure (millibars)']]\n",
        "y = weather_data_p.Humidity"
      ],
      "metadata": {
        "id": "VgwPACQlb-SZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "x_train, x_test, y_train,y_test =train_test_split(x,y, test_size=.20, random_state=1)\n",
        "\n"
      ],
      "metadata": {
        "id": "kBQNafcHcW0r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "\n",
        "lr = LinearRegression()\n",
        "\n",
        "lr.fit(x_train,y_train)"
      ],
      "metadata": {
        "id": "pDfqYP2KcapV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e850f96c-54fe-4e29-d159-3bf0ccc0a2c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "print('training score: ',lr.score(x_train,y_train))\n",
        "print('test score: ',lr.score(x_test,y_test))"
      ],
      "metadata": {
        "id": "tCRsllCUck-o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f064ca8-aef5-429d-e7fb-6a0da901f29c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "training score:  0.29065760334760526\n",
            "test score:  0.14324706577023627\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.regplot(x,y,ci = None)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "BG9IpBL0dBi4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "cade26d6-cbf1-4cd7-db7b-5f237b4b9bc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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pyMPX43L3u4C7MpatTXvuwKfCn7K2uT3Jq739vNzdy7Qp1cydPpXp02rK7n5iiV56obe1PcmerpGF3tr0Hr0q9EoBZJsI/hxY7O5HJ9wyhlK1gfraoOZwdHCIvV29zJk+SE11dVndTyzR+FN33/BZ//279o9b6G1ZluCyM1XolcLKNhE8BcwCIr/PvxylagMz66YxdUo1+w8f4cjAEK8dGeQ7156r+kAMpffoHavQa6mhm9WjV0pAtolgFtBuZlsZWSMo+akqCyG9NjCjroYZdTW4O929/UoCMZJNj94V4Ry9VyxNcIoKvVIisk0EX4g0ijJXCWONSO7cnV1hoXfjOIXeJYmTgwlbliW48LQGFXqlJGU76FzZTlVZCJUw1ohkp69/kIdSPXrHKfResng2q5ar0CvlI9uexYc4NiNZLVADvObuM6IKrJxUwlgjMr6Xu3vZ1N5Ja/u+MXv0vm5GWo/eM2ePuDIUKQfZXhEMDzpnQUXrGuDiqIIqR+U+1ogck+rRm7q98+kxCr3nN84Ke/TOU6FXyl7Opy7hvf+/CjuYaapKqQjdvf1s2RkWesfp0bti6dzhoZtnnzy1SJGK5F+2TUPvSXtZBTQDfZFEJFIA7s6zncEcva3tSdrGK/SG0zReeFoDNSr0SoXK9orgXWnPB4DnCZqHRMpGX/8gf3juAK3b99G6I8nuA2MXelODuKnQK3GRbY3gw1EHIhKFiXr0pgq9LU0JLlOhV2Jqoqkqv8uxu4VGcfcb8x5RjGjugvwbHHL+uOcgrdvHL/Se1ziLVWGTz1mnzlChV2JvotOftrTnX0Idy/JGcxfkz0SF3unTpnCFCr0i45poqsofpZ6b2SfSX8uJ0dwFk5cq9LaG0zSOVeg9M3Hy8Fm/Cr0ix5dLg+gJTREpI2nugtykCr1Bj959owu91VVcHM7Rq0KvSG5UGSsSjU80sT9197Fpx7FCb8/RsQq9c2lpmqdCr8gJmKhYnD60RL2ZpSpvRtC3TENMTJLGJxotVehN9ejd9tL4PXpV6BXJn4lqBNOPt14mT+MTBbp7+7n3mU5a25Pcs6OTV8Yo9K5YOpeWZQlWLlOhVyQKupYuojiOT5Re6G1tT9L2fBcDYxR6UxO2NC9SoVckakoERRC3/gPZFnpblgXt/Qtnq04iUkhKBAUWl/4DExV6582YOnzWf9mZczhpqn4VRYpFf30FVqn9B7Ip9J7XOIuWZUGh9+zXq9ArUiqUCAqskvoPvNrXz70799MaztGrQq9IeVIiKLBy7j8QFHpfo7V937iF3jPmnsSq5fNoUY9eqWCVVudTIiiwcus/cGRgkD90HBi+y+fFAyOvXGqrq3jz4lPCHr0q9Erlq8Q6nxJBgZVD/4F9r/YNT86uQq/ISJVY59NfcBGUWv+BbAq9b1wwa3jCFhV6Jc4qqc6XokQQU6lC78b2fWP36J0aFHqvbAoKvXNU6BUByrvONx4lgphIFXpTnbrGK/SmxvF506JTVOgVGUO51fmyoURQwY4MDPLwcwfYuD3Jph1JXnhl7EJvqsnntNknFSlSkfJRDnW+XCkRVJhUobe1Pcl94xR6rww7db1FhV6RSSm1Ot+JivQoYGZXA98GqoHb3P0rGes/BHwN2Bsu+p673xZlTOUkm3uVh9ILvTuSPLV3dKH33AWzhidsUaFXRDJFlgjMrBq4BbgK2ANsNbMN7v50xqb/6u43RBVHuTrevcoXLGrgvmf2s3F7knt2Jtl/eHSh9/Klc2hpmqdCr4hMKMorgouAXe7eAWBmdwLXAJmJQMaQfq+yu1NtxsEj/dx452P0HB0cVehdPPek4Tl6VegVkVxEmQjmA7vTXu8B3jzGdu81sxXATuCT7r47cwMzux64HmDhwoURhFp6XjzwGjVVVbx0sJdDfQMcHRwasV6FXhHJl2JXCn8N/NTdj5jZGuBHQEvmRu5+K3ArQHNzs2eurxTJV4OhmzduT/Jydx8ZJ/1UVxmzT6rl5nefo0KviORNlEeSvUBj2usFHCsKA+Dur6S9vA34+wjjKTlDQ84Te7vDcXz2jSr0AkydUsXMuhpqqw0z4+ZrzqmouxVEpPiiTARbgSVmdjpBArgW+Mv0DczsVHd/OXy5GtgeYTwl4VBfP/c+c2zo5rEKvakevbVVxk+37q6Ye5VFpDRFlgjcfcDMbgDuJrh99HZ332Zm64A2d98A3Ghmq4EB4ADwoajiKRZ3p2P/a8P39j/83IGcevSuPn9+oUMWkZgx9/Jqcm9ubva2trZih3FcqR69re1JNrUneV49ekWkyMzsEXdvHmudqo15kir0trYnue+Z/byW0aN37vSptCxL0LJcPXpFpLToaDRJQ0POk3u72Rie9T+5t3vE+lSP3pZlCVYtT3DWqTOoqlKPXhEpPUoEOTjU1899YaF3045O9h8+MmL9yVOncPmSObQ0JVi5LMHc6erRKyKlT4lgAh2dh4enadz6/AH6B0f36G1ZFrT1Ny86hdop6tErIuVFiSDD0YGhYOjm9n1jFnprqo2LF8/myvDgv2iOCr0iUt6UCIDkoT42t3fS2p7k3mc6RxV6E9PThm5eMoeTVegVkQoSyyPa0JDz1EvdwxO2PLFHhV4Ria/YJIJsCr0rls7hymUq9IpIvMQiERwdGOLS/9nKoSMDI5YvnnPScKcuFXpFSl82kzVJ7mKRCGqnVHHhogbu37VfhV6RMnW8yZqUDE5MLBIBwM3XnEPDSbUq9IqUqfTJmgDqa6fQc3SA9Vs6lAhOUGyOio2n1Bc7BBE5Abu7ephVVzNiWV1NNXu6esZ5h2RLjeIiUhYaG+rp7R95a3dv/yALGnSSd6KUCESkLKxZsZj+Qafn6ADuwWP/oLNmxeJih1b2lAhEpCysbEqwbvXZJKZPo7u3n8T0aaxbfbbqA3kQmxqBiJS/lU0JHfgjoCsCEZGYUyIQEYk5JQIRkZhTIhARiTkViyucxmYRkYnoiqCCpcZmSR7qGzE2y+b2ZLFDE5ESokRQwdLHZjELHmuqjfVbOoodmoiUECWCCra7q4e6muoRyzQ2i4hkUiKoYBqbRUSyoURQwTQ2i4hkQ4mggmlsFhHJhm4frXAam0VEJqIrAhGRmFMiEBGJOSUCEZGYUyIQEYm5SIvFZnY18G2gGrjN3b8yznbvBX4OvMnd26KMSUqfxkcaqdjfR7E/X6IX2RWBmVUDtwBvA84CrjOzs8bYbjrwceAPUcUi5UPjI41U7O+j2J8vhRFl09BFwC5373D3o8CdwDVjbHcz8FWgL8JYpExofKSRiv19FPvzpTCiTATzgd1pr/eEy4aZ2QVAo7v/5ng7MrPrzazNzNo6OzvzH6mUDI2PNFKxv49if74URtGKxWZWBXwD+PRE27r7re7e7O7Nc+fOjT44KRqNjzRSsb+PYn++FEaUiWAv0Jj2ekG4LGU6cA6w2cyeBy4GNphZc4QxSYnT+EgjFfv7KPbnS2FEmQi2AkvM7HQzqwWuBTakVrp7t7vPcfdF7r4IeAhYrbuG4k3jI41U7O+j2J8vhWHuHt3Ozd4OfIvg9tHb3f3LZrYOaHP3DRnbbgY+M1EiaG5u9rY25QoRkVyY2SPuPmaLS6T9CNz9LuCujGVrx9l2ZZSxiIjI2NSzWEQk5pQIRERiTolARCTmNDGNCBpPR+JNVwQSexpPR+JOiUBiT+PpSNwpEUjsaTwdiTvVCCRnldae3thQT/JQH/W1x/4cNJ6OxImuCCQnldiervF0JO6UCCQnldiervF0JO7UNCQ52d3Vw6y6mhHLKqE9fWVTQgd+iS1dEUhOND69SOVRIpCcqD1dpPIoEUhO1J4uUnlUI5CcqT1dpLLoikBEJOaUCEREYk6JQEQk5pQIRERiTolARCTmzN2LHUNOzKwTeCFt0Rxgf5HCyYXizC/FmV+KM79KMc7T3H3uWCvKLhFkMrM2d28udhwTUZz5pTjzS3HmV7nEmaKmIRGRmFMiEBGJuUpIBLcWO4AsKc78Upz5pTjzq1ziBCqgRiAiIiemEq4IRETkBCgRiIjEXEkmAjO73cySZvZU2rJTzOx3ZvZM+NgQLjcz+46Z7TKzJ8zsgnD5lWb2eNpPn5m9u9TiDNf9vZltM7Pt4TZWonF+1cyeCn/+Ip8xTiLOJjN70MyOmNlnMvZztZntCP8NN5VwnKP2U0oxmlmjmW0ys6fD38+Pl2ic08zsYTP7Yxjnl0oxzrT3VZvZY2b2b/mOc7JKMhEAdwBXZyy7Cdjo7kuAjeFrgLcBS8Kf64F/BHD3Te5+nrufB7QAPcBvSy1OM7sUuAw4FzgHeBNwRQnG+Q7gAuA84M3AZ8xsRhHjPADcCHw9fWMzqwZuCf8dZwHXmdlZpRbncfaTL2PtO9cYB4BPu/tZwMXA35Tod3kEaHH3NxL8fl5tZheXYJwpHwe25zm+E1KSicDdtxB8memuAX4UPv8R8O605f/kgYeAWWZ2asZ73wf8u7vndWLdPMXpwDSgFpgK1AD7SjDOs4At7j7g7q8BT5Dng1gucbp70t23Av0Z218E7HL3Dnc/CtwZ7qPU4hxvPyUTo7u/7O6Phs8PERy85pdgnO7uh8OXNeFPXu+Cydf/uZktAN4B3JbP+E5USSaCccxz95fD538C5oXP5wO707bbw+hf1muBn0Yb3rCc4nT3B4FNwMvhz93uXoizhVy/zz8SnGnVm9kc4EqgsYhxjieb34co5BpnMUw6RjNbBJwP/CH/YY2Sc5xhc8vjQBL4nbuXZJzAt4D/DgxFFtUklFMiGObBPa9ZZfzwbPYNwN2RBjWGbOI0szOB5cACggNWi5ldXoDwhmUTp7v/FrgLeIAgqT4IDB7vPfmWy/97MZVDnDn+DZ0M/AL4hLu/GmlgGbKN090Hw2bgBcBFZnZO5MGN/Pxs/tbfCSTd/ZHCRJW9ckoE+1JNPuFjMly+l5FnpgvCZSnvB37p7qMu0yKSa5x/Bjzk7ofDy9t/By4pwThx9y+HdZerAAN2FjHO8Uz0+xCVXOMshpxjNLMagiTwE3f/vxHHlzLp79LdDxJcYUdVe0mXa5yXAavN7HmCJssWM/txtCFmp5wSwQbgr8LnfwX8v7TlH7TAxUB32uUawHUUrlkoFU8ucb4IXGFmU8I/uisoTCEppzjDS+/ZAGZ2LkFxO9/F91ziHM9WYImZnW5mtQTNghsijC8l1ziLIacYzcyAHwDb3f0bEceWLtc455rZrPB5HXAV0B5phIGc4nT3z7n7AndfRPB72eruH4g2xCy5e8n9EBy4XyYotuwBPgrMJqjMPwP8Hjgl3NYI7hJ5FngSaE7bzyKCs8GqUo0TqAbWExz8nwa+UaJxTgvjexp4CDivyHG+LtzmVeBg+HxGuO7tBFcrzwJ/V8JxjtpPKcUIvIWgueMJ4PHw5+2l9l0SnJQ8Fsb5FLC2VP/P0/a3Evi3fMc52R8NMSEiEnPl1DQkIiIRUCIQEYk5JQIRkZhTIhARiTklAhGRmFMikJJiZoMWjBb7lJn9HzOrL3ZM2TCzU3MdTdLM1pnZfwqfbzaz5vD58+EwHpjZA+HjyihGqzSzd5rZunzvV8qLEoGUml4Pei+fAxwF/mv6SjObUqhAcvysTwHfz2X/7r7W3X8/wTaX5rLP8YSjso7lN8C7yiXhSjSUCKSU3QucGZ4N32tmG4Cnw17OXzOzrRbMmbAGhs/Kt6RdUVwebntH+PpJM/tkuG36GficsNs/ZvYhM9tgZq3ARjM7yYKx6B8Ox5AfbyTT9wL/kbaPX1kwRv3zZnaDmX0qfP9DZnZKuN0dZva+430BZnY47eUMM/uNBXMt/G8zqwq3+Ucza7OMsfjDz/6qmT0K/LmZ3WjB3AJPmNmdMDxGzmbgnTn8v0iFKdjZlUguwrPxtxEeXAnmQjjH3Z8zs+sJhr54k5lNBe43s98C7yEYvfXL4RlwPcH49PPDKwxSQxFM4ALgXHc/YGb/g2AogI+E733YzH7vwVDcqVhPB7rc/UjaPs4hGK1zGrAL+Ky7n29m3wQ+SDAKZa4uIhgO/IXwe3kP8HOC3tMHwn/zRjM7192fCN/zirunJmt6CTjd3Y9kfA9twOXAzyYRk1QAXRFIqamzYDjhNgXy6j0AAAJESURBVIJxmH4QLn/Y3Z8Ln7+VYDykxwmGRZ5NMJHOVuDDZvZF4A0ejKHfASw2s++a2dUE3f4n8jt3T409/1bgpvCzNhMc2BdmbH8q0JmxbJO7H3L3TqAb+HW4/EmCoU8m42EP5lkYJBjy4C3h8veHZ/2PAWcTJIuUf017/gTwEzP7AMGkMylJ4PWTjEkqgK4IpNT0ejCc8LBg7DNeS18EfMzdRw0tbmYrCCb+uMPMvuHu/2RmbwT+M0G94f3ARwgOhKkToWkZu8n8rPe6+47jxTzGPtKvDobSXg8x+b+7zPFgPLwa+QzwJnfvMrM7MmJJ/7e8A1gBvAv4OzN7g7sPhNv3TjImqQC6IpBydDfw38LRWjGzpWFb/mnAPnf/PsEMUBeEd99UufsvgM8TNPsAPA9cGD4/Xjv93cDHwpE4MbPzx9hmJ5M/y8/FReGoqlXAXwD3EQy69hrQbWbzCJrTRgnf0+jum4DPAjOBk8PVSwkGa5OY0hWBlKPbCA68j4YH6E6CaQJXAn9rZv3AYYK2+PnAD1OFVeBz4ePXgZ+F9YbfHOezbiZoz38i3MdzZBRW3f01M3vWzM509115+PeNZyvwPeBMgjH3f+nuQ2b2GMGwy7uB+8d5bzXwYzObSXCV8x0Pxu6HYLa5z43zPokBjT4qkgdm9mfAhe7++WLHkovwKuJf3H1VsWOR4tEVgUgeuPsvLZy4p8wsBD5d7CCkuHRFICIScyoWi4jEnBKBiEjMKRGIiMScEoGISMwpEYiIxNz/B7WmBXMaL8DNAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Fzx7bS9Sd_bj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "  our dataset is not linear so linear regression is not fitting well."
      ],
      "metadata": {
        "id": "dE5_4pLleCFG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Increase the model Complexity\n",
        "\n",
        "We will move from just linear features to Polynomial features."
      ],
      "metadata": {
        "id": "itzVh2FUdbVi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "ZYp6JZsrg9rX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Notice that there is a hidden column of 1’s which can be thought of as the variable associated with θ₀. Since θ₀ × 1 = θ₀ this is often left out."
      ],
      "metadata": {
        "id": "QP_jL7wWhGwB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "\n",
        "pre_process = PolynomialFeatures(degree = 2)\n",
        "\n",
        "x_poly = pre_process.fit_transform(x)\n",
        "\n",
        "x_poly"
      ],
      "metadata": {
        "id": "1GSQzAVAdECo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f87d060e-f4d4-4435-ea47-d7c94dae3a48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.00000000e+00, 1.01440000e+03, 1.02900736e+06],\n",
              "       [1.00000000e+00, 1.01420000e+03, 1.02860164e+06],\n",
              "       [1.00000000e+00, 1.01447000e+03, 1.02914938e+06],\n",
              "       [1.00000000e+00, 1.01445000e+03, 1.02910880e+06],\n",
              "       [1.00000000e+00, 1.01449000e+03, 1.02918996e+06],\n",
              "       [1.00000000e+00, 1.01452000e+03, 1.02925083e+06],\n",
              "       [1.00000000e+00, 1.01416000e+03, 1.02852051e+06],\n",
              "       [1.00000000e+00, 1.01424000e+03, 1.02868278e+06],\n",
              "       [1.00000000e+00, 1.01425000e+03, 1.02870306e+06],\n",
              "       [1.00000000e+00, 1.01396000e+03, 1.02811488e+06],\n",
              "       [1.00000000e+00, 1.01385000e+03, 1.02789182e+06],\n",
              "       [1.00000000e+00, 1.01304000e+03, 1.02625004e+06],\n",
              "       [1.00000000e+00, 1.01222000e+03, 1.02458933e+06],\n",
              "       [1.00000000e+00, 1.01144000e+03, 1.02301087e+06],\n",
              "       [1.00000000e+00, 1.01052000e+03, 1.02115067e+06],\n",
              "       [1.00000000e+00, 1.00983000e+03, 1.01975663e+06],\n",
              "       [1.00000000e+00, 1.00926000e+03, 1.01860575e+06],\n",
              "       [1.00000000e+00, 1.00876000e+03, 1.01759674e+06],\n",
              "       [1.00000000e+00, 1.00836000e+03, 1.01678989e+06],\n",
              "       [1.00000000e+00, 1.00811000e+03, 1.01628577e+06],\n",
              "       [1.00000000e+00, 1.00815000e+03, 1.01636642e+06],\n",
              "       [1.00000000e+00, 1.00785000e+03, 1.01576162e+06],\n",
              "       [1.00000000e+00, 1.00789000e+03, 1.01584225e+06],\n",
              "       [1.00000000e+00, 1.00736000e+03, 1.01477417e+06],\n",
              "       [1.00000000e+00, 1.00726000e+03, 1.01457271e+06]])"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_x_poly = pd.DataFrame(x_poly)\n",
        "df_x_poly.columns = ['1','x','x_squared']\n",
        "df_x_poly"
      ],
      "metadata": {
        "id": "9gkq2flYho47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "outputId": "624ab1d9-5361-4c0d-bf96-097574de04de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      1        x     x_squared\n",
              "0   1.0  1014.40  1.029007e+06\n",
              "1   1.0  1014.20  1.028602e+06\n",
              "2   1.0  1014.47  1.029149e+06\n",
              "3   1.0  1014.45  1.029109e+06\n",
              "4   1.0  1014.49  1.029190e+06\n",
              "5   1.0  1014.52  1.029251e+06\n",
              "6   1.0  1014.16  1.028521e+06\n",
              "7   1.0  1014.24  1.028683e+06\n",
              "8   1.0  1014.25  1.028703e+06\n",
              "9   1.0  1013.96  1.028115e+06\n",
              "10  1.0  1013.85  1.027892e+06\n",
              "11  1.0  1013.04  1.026250e+06\n",
              "12  1.0  1012.22  1.024589e+06\n",
              "13  1.0  1011.44  1.023011e+06\n",
              "14  1.0  1010.52  1.021151e+06\n",
              "15  1.0  1009.83  1.019757e+06\n",
              "16  1.0  1009.26  1.018606e+06\n",
              "17  1.0  1008.76  1.017597e+06\n",
              "18  1.0  1008.36  1.016790e+06\n",
              "19  1.0  1008.11  1.016286e+06\n",
              "20  1.0  1008.15  1.016366e+06\n",
              "21  1.0  1007.85  1.015762e+06\n",
              "22  1.0  1007.89  1.015842e+06\n",
              "23  1.0  1007.36  1.014774e+06\n",
              "24  1.0  1007.26  1.014573e+06"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-974403a1-d25e-4bc7-ab12-0a2a92a404f2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>1</th>\n",
              "      <th>x</th>\n",
              "      <th>x_squared</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1014.40</td>\n",
              "      <td>1.029007e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1014.20</td>\n",
              "      <td>1.028602e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1014.47</td>\n",
              "      <td>1.029149e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1014.45</td>\n",
              "      <td>1.029109e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1014.49</td>\n",
              "      <td>1.029190e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1014.52</td>\n",
              "      <td>1.029251e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1014.16</td>\n",
              "      <td>1.028521e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1014.24</td>\n",
              "      <td>1.028683e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1014.25</td>\n",
              "      <td>1.028703e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1013.96</td>\n",
              "      <td>1.028115e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1013.85</td>\n",
              "      <td>1.027892e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1013.04</td>\n",
              "      <td>1.026250e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1012.22</td>\n",
              "      <td>1.024589e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1011.44</td>\n",
              "      <td>1.023011e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1010.52</td>\n",
              "      <td>1.021151e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1009.83</td>\n",
              "      <td>1.019757e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1009.26</td>\n",
              "      <td>1.018606e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1008.76</td>\n",
              "      <td>1.017597e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1008.36</td>\n",
              "      <td>1.016790e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1008.11</td>\n",
              "      <td>1.016286e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1008.15</td>\n",
              "      <td>1.016366e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1007.85</td>\n",
              "      <td>1.015762e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1007.89</td>\n",
              "      <td>1.015842e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1007.36</td>\n",
              "      <td>1.014774e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1007.26</td>\n",
              "      <td>1.014573e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-974403a1-d25e-4bc7-ab12-0a2a92a404f2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-974403a1-d25e-4bc7-ab12-0a2a92a404f2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-974403a1-d25e-4bc7-ab12-0a2a92a404f2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Implementing Polynomial Regression**"
      ],
      "metadata": {
        "id": "Am0pMxnNhSMp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The method here remains the same as multiple linear regression in python, but here we are fitting our regression model on the polynomial features."
      ],
      "metadata": {
        "id": "wb3U8pm5haG-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "x_train, x_test, y_train,y_test =train_test_split(x_poly,y, test_size=.20, random_state=1)\n"
      ],
      "metadata": {
        "id": "xT4ixjfLjAPx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "\n",
        "lr = LinearRegression()\n",
        "\n",
        "lr.fit(x_train,y_train)"
      ],
      "metadata": {
        "id": "g-EEN2Tgj2ol",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9568541-baf0-40b0-ce0e-2f622c93a196"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = lr.predict(x_poly)"
      ],
      "metadata": {
        "id": "3bXjflSWgnxg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "print('training score: ',lr.score(x_train,y_train))\n",
        "print('test score: ',lr.score(x_test,y_test))"
      ],
      "metadata": {
        "id": "wNpQW2SziPR8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3111b31-ee12-4452-8c33-08aaf6dc22d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "training score:  0.8212622197151398\n",
            "test score:  0.9427391324681367\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.regplot(x,y,fit_reg=True, ci = None)\n",
        "plt.plot(x,y_pred)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "VYxowd4GkqkN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "d48a8756-e32c-41dc-9bcf-c5549e4113ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0ROuC9ZWa-XF"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}