musk_non_musk.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "musk_non-musk.ipynb",
      "provenance": [],
      "mount_file_id": "1t3W103cZay25p3u5gqF4F4XuRod8FbI3",
      "authorship_tag": "ABX9TyPArYhJMdk1G826f2wTXJt3"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "qsPvZTdIsgmG"
      },
      "source": [
        "import os"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gknmVkUesyz4"
      },
      "source": [
        "os.chdir('/content/drive/MyDrive/Musk Non-musk')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J40Dc9uzs_Wm",
        "outputId": "391cb502-d970-4955-824b-d78108f160fa"
      },
      "source": [
        "!ls"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "musk_csv.csv  musk_non-musk.ipynb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LkJfVnl1tAw4"
      },
      "source": [
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import sklearn\r\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NWj8nHJJt3lR"
      },
      "source": [
        "data = pd.read_csv(\"musk_csv.csv\")"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "qdVB0ha7uEoW",
        "outputId": "1445ce01-ad70-4a0f-f8f5-a0b227be9b26"
      },
      "source": [
        "data.head(5)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>f147</th>\n",
              "      <th>f148</th>\n",
              "      <th>f149</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>MUSK-211</td>\n",
              "      <td>211_1+1</td>\n",
              "      <td>46</td>\n",
              "      <td>-108</td>\n",
              "      <td>-60</td>\n",
              "      <td>-69</td>\n",
              "      <td>-117</td>\n",
              "      <td>49</td>\n",
              "      <td>38</td>\n",
              "      <td>-161</td>\n",
              "      <td>-8</td>\n",
              "      <td>5</td>\n",
              "      <td>-323</td>\n",
              "      <td>-220</td>\n",
              "      <td>-113</td>\n",
              "      <td>-299</td>\n",
              "      <td>-283</td>\n",
              "      <td>-307</td>\n",
              "      <td>-31</td>\n",
              "      <td>-106</td>\n",
              "      <td>-227</td>\n",
              "      <td>-42</td>\n",
              "      <td>-59</td>\n",
              "      <td>-22</td>\n",
              "      <td>-67</td>\n",
              "      <td>189</td>\n",
              "      <td>81</td>\n",
              "      <td>17</td>\n",
              "      <td>-27</td>\n",
              "      <td>-89</td>\n",
              "      <td>-67</td>\n",
              "      <td>105</td>\n",
              "      <td>-116</td>\n",
              "      <td>124</td>\n",
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              "      <td>5</td>\n",
              "      <td>-120</td>\n",
              "      <td>63</td>\n",
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              "      <td>81</td>\n",
              "      <td>-114</td>\n",
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              "      <td>45</td>\n",
              "      <td>-118</td>\n",
              "      <td>-75</td>\n",
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              "      <td>-234</td>\n",
              "      <td>-19</td>\n",
              "      <td>12</td>\n",
              "      <td>-13</td>\n",
              "      <td>-41</td>\n",
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              "      <td>-149</td>\n",
              "      <td>70</td>\n",
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              "      <td>-177</td>\n",
              "      <td>-101</td>\n",
              "      <td>-116</td>\n",
              "      <td>-14</td>\n",
              "      <td>-50</td>\n",
              "      <td>24</td>\n",
              "      <td>-81</td>\n",
              "      <td>-125</td>\n",
              "      <td>-114</td>\n",
              "      <td>-44</td>\n",
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              "      <td>96</td>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>MUSK-211</td>\n",
              "      <td>211_1+10</td>\n",
              "      <td>41</td>\n",
              "      <td>-188</td>\n",
              "      <td>-145</td>\n",
              "      <td>22</td>\n",
              "      <td>-117</td>\n",
              "      <td>-6</td>\n",
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              "      <td>49</td>\n",
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              "      <td>32</td>\n",
              "      <td>95</td>\n",
              "      <td>-116</td>\n",
              "      <td>85</td>\n",
              "      <td>-23</td>\n",
              "      <td>42</td>\n",
              "      <td>-58</td>\n",
              "      <td>61</td>\n",
              "      <td>-171</td>\n",
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              "      <td>88</td>\n",
              "      <td>-21</td>\n",
              "      <td>-32</td>\n",
              "      <td>32</td>\n",
              "      <td>-128</td>\n",
              "      <td>-72</td>\n",
              "      <td>-124</td>\n",
              "      <td>-218</td>\n",
              "      <td>-94</td>\n",
              "      <td>53</td>\n",
              "      <td>-79</td>\n",
              "      <td>-20</td>\n",
              "      <td>-35</td>\n",
              "      <td>-26</td>\n",
              "      <td>4</td>\n",
              "      <td>50</td>\n",
              "      <td>17</td>\n",
              "      <td>-177</td>\n",
              "      <td>-102</td>\n",
              "      <td>-121</td>\n",
              "      <td>-66</td>\n",
              "      <td>-77</td>\n",
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              "      <td>-41</td>\n",
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              "      <td>-32</td>\n",
              "      <td>-63</td>\n",
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              "      <td>-59</td>\n",
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              "      <td>52</td>\n",
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              "      <td>136</td>\n",
              "      <td>169</td>\n",
              "      <td>-61</td>\n",
              "      <td>-136</td>\n",
              "      <td>79</td>\n",
              "      <td>1</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>MUSK-211</td>\n",
              "      <td>211_1+11</td>\n",
              "      <td>46</td>\n",
              "      <td>-194</td>\n",
              "      <td>-145</td>\n",
              "      <td>28</td>\n",
              "      <td>-117</td>\n",
              "      <td>73</td>\n",
              "      <td>57</td>\n",
              "      <td>-168</td>\n",
              "      <td>-39</td>\n",
              "      <td>-22</td>\n",
              "      <td>-319</td>\n",
              "      <td>-111</td>\n",
              "      <td>-104</td>\n",
              "      <td>-283</td>\n",
              "      <td>-282</td>\n",
              "      <td>-303</td>\n",
              "      <td>52</td>\n",
              "      <td>-152</td>\n",
              "      <td>-97</td>\n",
              "      <td>-225</td>\n",
              "      <td>-28</td>\n",
              "      <td>-22</td>\n",
              "      <td>2</td>\n",
              "      <td>179</td>\n",
              "      <td>49</td>\n",
              "      <td>-33</td>\n",
              "      <td>46</td>\n",
              "      <td>-88</td>\n",
              "      <td>22</td>\n",
              "      <td>79</td>\n",
              "      <td>-116</td>\n",
              "      <td>19</td>\n",
              "      <td>-11</td>\n",
              "      <td>6</td>\n",
              "      <td>-38</td>\n",
              "      <td>71</td>\n",
              "      <td>-175</td>\n",
              "      <td>...</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>-23</td>\n",
              "      <td>-15</td>\n",
              "      <td>-129</td>\n",
              "      <td>-74</td>\n",
              "      <td>-125</td>\n",
              "      <td>-221</td>\n",
              "      <td>-93</td>\n",
              "      <td>53</td>\n",
              "      <td>-72</td>\n",
              "      <td>-19</td>\n",
              "      <td>-33</td>\n",
              "      <td>-26</td>\n",
              "      <td>3</td>\n",
              "      <td>49</td>\n",
              "      <td>17</td>\n",
              "      <td>-177</td>\n",
              "      <td>-102</td>\n",
              "      <td>-119</td>\n",
              "      <td>-66</td>\n",
              "      <td>-81</td>\n",
              "      <td>51</td>\n",
              "      <td>-41</td>\n",
              "      <td>-27</td>\n",
              "      <td>-41</td>\n",
              "      <td>-140</td>\n",
              "      <td>77</td>\n",
              "      <td>-163</td>\n",
              "      <td>-238</td>\n",
              "      <td>-134</td>\n",
              "      <td>-154</td>\n",
              "      <td>57</td>\n",
              "      <td>143</td>\n",
              "      <td>142</td>\n",
              "      <td>165</td>\n",
              "      <td>-67</td>\n",
              "      <td>-145</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>MUSK-211</td>\n",
              "      <td>211_1+12</td>\n",
              "      <td>41</td>\n",
              "      <td>-188</td>\n",
              "      <td>-145</td>\n",
              "      <td>22</td>\n",
              "      <td>-117</td>\n",
              "      <td>-7</td>\n",
              "      <td>57</td>\n",
              "      <td>-170</td>\n",
              "      <td>-39</td>\n",
              "      <td>-99</td>\n",
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              "      <td>-111</td>\n",
              "      <td>-228</td>\n",
              "      <td>-282</td>\n",
              "      <td>-281</td>\n",
              "      <td>-301</td>\n",
              "      <td>54</td>\n",
              "      <td>-150</td>\n",
              "      <td>-98</td>\n",
              "      <td>-196</td>\n",
              "      <td>-28</td>\n",
              "      <td>-22</td>\n",
              "      <td>2</td>\n",
              "      <td>77</td>\n",
              "      <td>48</td>\n",
              "      <td>-34</td>\n",
              "      <td>46</td>\n",
              "      <td>-91</td>\n",
              "      <td>32</td>\n",
              "      <td>94</td>\n",
              "      <td>-116</td>\n",
              "      <td>84</td>\n",
              "      <td>-23</td>\n",
              "      <td>41</td>\n",
              "      <td>-58</td>\n",
              "      <td>62</td>\n",
              "      <td>-171</td>\n",
              "      <td>...</td>\n",
              "      <td>88</td>\n",
              "      <td>-20</td>\n",
              "      <td>-32</td>\n",
              "      <td>32</td>\n",
              "      <td>-128</td>\n",
              "      <td>-73</td>\n",
              "      <td>-125</td>\n",
              "      <td>-220</td>\n",
              "      <td>-93</td>\n",
              "      <td>53</td>\n",
              "      <td>-78</td>\n",
              "      <td>-19</td>\n",
              "      <td>-34</td>\n",
              "      <td>-26</td>\n",
              "      <td>4</td>\n",
              "      <td>50</td>\n",
              "      <td>17</td>\n",
              "      <td>-177</td>\n",
              "      <td>-101</td>\n",
              "      <td>-121</td>\n",
              "      <td>-65</td>\n",
              "      <td>-77</td>\n",
              "      <td>52</td>\n",
              "      <td>-41</td>\n",
              "      <td>-34</td>\n",
              "      <td>-32</td>\n",
              "      <td>-66</td>\n",
              "      <td>115</td>\n",
              "      <td>-7</td>\n",
              "      <td>-236</td>\n",
              "      <td>-60</td>\n",
              "      <td>-4</td>\n",
              "      <td>52</td>\n",
              "      <td>104</td>\n",
              "      <td>136</td>\n",
              "      <td>168</td>\n",
              "      <td>-60</td>\n",
              "      <td>-135</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>MUSK-211</td>\n",
              "      <td>211_1+13</td>\n",
              "      <td>41</td>\n",
              "      <td>-188</td>\n",
              "      <td>-145</td>\n",
              "      <td>22</td>\n",
              "      <td>-117</td>\n",
              "      <td>-7</td>\n",
              "      <td>57</td>\n",
              "      <td>-170</td>\n",
              "      <td>-39</td>\n",
              "      <td>-99</td>\n",
              "      <td>-319</td>\n",
              "      <td>-111</td>\n",
              "      <td>-228</td>\n",
              "      <td>-282</td>\n",
              "      <td>-281</td>\n",
              "      <td>-301</td>\n",
              "      <td>54</td>\n",
              "      <td>-150</td>\n",
              "      <td>-98</td>\n",
              "      <td>-196</td>\n",
              "      <td>-28</td>\n",
              "      <td>-22</td>\n",
              "      <td>2</td>\n",
              "      <td>78</td>\n",
              "      <td>48</td>\n",
              "      <td>-34</td>\n",
              "      <td>46</td>\n",
              "      <td>-91</td>\n",
              "      <td>31</td>\n",
              "      <td>94</td>\n",
              "      <td>-116</td>\n",
              "      <td>84</td>\n",
              "      <td>-23</td>\n",
              "      <td>41</td>\n",
              "      <td>-58</td>\n",
              "      <td>62</td>\n",
              "      <td>-171</td>\n",
              "      <td>...</td>\n",
              "      <td>88</td>\n",
              "      <td>-20</td>\n",
              "      <td>-32</td>\n",
              "      <td>32</td>\n",
              "      <td>-128</td>\n",
              "      <td>-73</td>\n",
              "      <td>-125</td>\n",
              "      <td>-220</td>\n",
              "      <td>-93</td>\n",
              "      <td>53</td>\n",
              "      <td>-78</td>\n",
              "      <td>-19</td>\n",
              "      <td>-34</td>\n",
              "      <td>-26</td>\n",
              "      <td>4</td>\n",
              "      <td>50</td>\n",
              "      <td>17</td>\n",
              "      <td>-177</td>\n",
              "      <td>-101</td>\n",
              "      <td>-121</td>\n",
              "      <td>-65</td>\n",
              "      <td>-76</td>\n",
              "      <td>52</td>\n",
              "      <td>-41</td>\n",
              "      <td>-34</td>\n",
              "      <td>-32</td>\n",
              "      <td>-66</td>\n",
              "      <td>115</td>\n",
              "      <td>-8</td>\n",
              "      <td>-236</td>\n",
              "      <td>-60</td>\n",
              "      <td>-4</td>\n",
              "      <td>52</td>\n",
              "      <td>104</td>\n",
              "      <td>137</td>\n",
              "      <td>168</td>\n",
              "      <td>-60</td>\n",
              "      <td>-135</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 170 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   ID molecule_name conformation_name  f1   f2  ...  f163  f164  f165  f166  class\n",
              "0   1      MUSK-211           211_1+1  46 -108  ...   156   -50  -112    96      1\n",
              "1   2      MUSK-211          211_1+10  41 -188  ...   169   -61  -136    79      1\n",
              "2   3      MUSK-211          211_1+11  46 -194  ...   165   -67  -145    39      1\n",
              "3   4      MUSK-211          211_1+12  41 -188  ...   168   -60  -135    80      1\n",
              "4   5      MUSK-211          211_1+13  41 -188  ...   168   -60  -135    80      1\n",
              "\n",
              "[5 rows x 170 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "xXLjt3nluIqR",
        "outputId": "c5a142ca-b959-40f5-c4c7-66bae78c1823"
      },
      "source": [
        "data.tail(5)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>molecule_name</th>\n",
              "      <th>conformation_name</th>\n",
              "      <th>f1</th>\n",
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              "      <th>f33</th>\n",
              "      <th>f34</th>\n",
              "      <th>f35</th>\n",
              "      <th>f36</th>\n",
              "      <th>f37</th>\n",
              "      <th>...</th>\n",
              "      <th>f128</th>\n",
              "      <th>f129</th>\n",
              "      <th>f130</th>\n",
              "      <th>f131</th>\n",
              "      <th>f132</th>\n",
              "      <th>f133</th>\n",
              "      <th>f134</th>\n",
              "      <th>f135</th>\n",
              "      <th>f136</th>\n",
              "      <th>f137</th>\n",
              "      <th>f138</th>\n",
              "      <th>f139</th>\n",
              "      <th>f140</th>\n",
              "      <th>f141</th>\n",
              "      <th>f142</th>\n",
              "      <th>f143</th>\n",
              "      <th>f144</th>\n",
              "      <th>f145</th>\n",
              "      <th>f146</th>\n",
              "      <th>f147</th>\n",
              "      <th>f148</th>\n",
              "      <th>f149</th>\n",
              "      <th>f150</th>\n",
              "      <th>f151</th>\n",
              "      <th>f152</th>\n",
              "      <th>f153</th>\n",
              "      <th>f154</th>\n",
              "      <th>f155</th>\n",
              "      <th>f156</th>\n",
              "      <th>f157</th>\n",
              "      <th>f158</th>\n",
              "      <th>f159</th>\n",
              "      <th>f160</th>\n",
              "      <th>f161</th>\n",
              "      <th>f162</th>\n",
              "      <th>f163</th>\n",
              "      <th>f164</th>\n",
              "      <th>f165</th>\n",
              "      <th>f166</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6593</th>\n",
              "      <td>6594</td>\n",
              "      <td>NON-MUSK-jp13</td>\n",
              "      <td>jp13_2+5</td>\n",
              "      <td>51</td>\n",
              "      <td>-123</td>\n",
              "      <td>-23</td>\n",
              "      <td>-108</td>\n",
              "      <td>-117</td>\n",
              "      <td>134</td>\n",
              "      <td>-160</td>\n",
              "      <td>82</td>\n",
              "      <td>-230</td>\n",
              "      <td>-28</td>\n",
              "      <td>-150</td>\n",
              "      <td>-63</td>\n",
              "      <td>-55</td>\n",
              "      <td>-77</td>\n",
              "      <td>-108</td>\n",
              "      <td>-308</td>\n",
              "      <td>-221</td>\n",
              "      <td>-41</td>\n",
              "      <td>-57</td>\n",
              "      <td>12</td>\n",
              "      <td>-112</td>\n",
              "      <td>-183</td>\n",
              "      <td>-198</td>\n",
              "      <td>-35</td>\n",
              "      <td>137</td>\n",
              "      <td>-198</td>\n",
              "      <td>-168</td>\n",
              "      <td>-153</td>\n",
              "      <td>-51</td>\n",
              "      <td>252</td>\n",
              "      <td>-108</td>\n",
              "      <td>135</td>\n",
              "      <td>-121</td>\n",
              "      <td>119</td>\n",
              "      <td>-120</td>\n",
              "      <td>73</td>\n",
              "      <td>28</td>\n",
              "      <td>...</td>\n",
              "      <td>224</td>\n",
              "      <td>-18</td>\n",
              "      <td>-96</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>-76</td>\n",
              "      <td>-53</td>\n",
              "      <td>-38</td>\n",
              "      <td>-23</td>\n",
              "      <td>-176</td>\n",
              "      <td>-32</td>\n",
              "      <td>-205</td>\n",
              "      <td>-95</td>\n",
              "      <td>-124</td>\n",
              "      <td>-139</td>\n",
              "      <td>-153</td>\n",
              "      <td>-170</td>\n",
              "      <td>-178</td>\n",
              "      <td>-102</td>\n",
              "      <td>-113</td>\n",
              "      <td>-42</td>\n",
              "      <td>-67</td>\n",
              "      <td>-141</td>\n",
              "      <td>-96</td>\n",
              "      <td>-112</td>\n",
              "      <td>-113</td>\n",
              "      <td>-47</td>\n",
              "      <td>124</td>\n",
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              "      <td>-44</td>\n",
              "      <td>-115</td>\n",
              "      <td>118</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6594</th>\n",
              "      <td>6595</td>\n",
              "      <td>NON-MUSK-jp13</td>\n",
              "      <td>jp13_2+6</td>\n",
              "      <td>44</td>\n",
              "      <td>-104</td>\n",
              "      <td>-19</td>\n",
              "      <td>-105</td>\n",
              "      <td>-117</td>\n",
              "      <td>142</td>\n",
              "      <td>-165</td>\n",
              "      <td>68</td>\n",
              "      <td>-225</td>\n",
              "      <td>-32</td>\n",
              "      <td>-124</td>\n",
              "      <td>-77</td>\n",
              "      <td>-43</td>\n",
              "      <td>-74</td>\n",
              "      <td>-100</td>\n",
              "      <td>-291</td>\n",
              "      <td>-215</td>\n",
              "      <td>-25</td>\n",
              "      <td>-49</td>\n",
              "      <td>6</td>\n",
              "      <td>-121</td>\n",
              "      <td>-183</td>\n",
              "      <td>-201</td>\n",
              "      <td>-54</td>\n",
              "      <td>127</td>\n",
              "      <td>-204</td>\n",
              "      <td>-164</td>\n",
              "      <td>-160</td>\n",
              "      <td>-41</td>\n",
              "      <td>255</td>\n",
              "      <td>-117</td>\n",
              "      <td>-104</td>\n",
              "      <td>-14</td>\n",
              "      <td>133</td>\n",
              "      <td>-157</td>\n",
              "      <td>141</td>\n",
              "      <td>46</td>\n",
              "      <td>...</td>\n",
              "      <td>232</td>\n",
              "      <td>-6</td>\n",
              "      <td>-146</td>\n",
              "      <td>-88</td>\n",
              "      <td>-75</td>\n",
              "      <td>-74</td>\n",
              "      <td>-55</td>\n",
              "      <td>-30</td>\n",
              "      <td>-28</td>\n",
              "      <td>-175</td>\n",
              "      <td>-48</td>\n",
              "      <td>-202</td>\n",
              "      <td>-86</td>\n",
              "      <td>-125</td>\n",
              "      <td>-138</td>\n",
              "      <td>-154</td>\n",
              "      <td>-169</td>\n",
              "      <td>-178</td>\n",
              "      <td>-102</td>\n",
              "      <td>-123</td>\n",
              "      <td>-32</td>\n",
              "      <td>-52</td>\n",
              "      <td>-136</td>\n",
              "      <td>-98</td>\n",
              "      <td>-109</td>\n",
              "      <td>-112</td>\n",
              "      <td>-56</td>\n",
              "      <td>-49</td>\n",
              "      <td>-85</td>\n",
              "      <td>60</td>\n",
              "      <td>-51</td>\n",
              "      <td>166</td>\n",
              "      <td>-9</td>\n",
              "      <td>150</td>\n",
              "      <td>129</td>\n",
              "      <td>158</td>\n",
              "      <td>-66</td>\n",
              "      <td>-144</td>\n",
              "      <td>-5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6595</th>\n",
              "      <td>6596</td>\n",
              "      <td>NON-MUSK-jp13</td>\n",
              "      <td>jp13_2+7</td>\n",
              "      <td>44</td>\n",
              "      <td>-102</td>\n",
              "      <td>-19</td>\n",
              "      <td>-104</td>\n",
              "      <td>-117</td>\n",
              "      <td>72</td>\n",
              "      <td>-165</td>\n",
              "      <td>65</td>\n",
              "      <td>-219</td>\n",
              "      <td>-12</td>\n",
              "      <td>-107</td>\n",
              "      <td>-66</td>\n",
              "      <td>-58</td>\n",
              "      <td>-82</td>\n",
              "      <td>-77</td>\n",
              "      <td>-288</td>\n",
              "      <td>-214</td>\n",
              "      <td>-21</td>\n",
              "      <td>-58</td>\n",
              "      <td>-32</td>\n",
              "      <td>-93</td>\n",
              "      <td>-183</td>\n",
              "      <td>-197</td>\n",
              "      <td>-71</td>\n",
              "      <td>218</td>\n",
              "      <td>-217</td>\n",
              "      <td>-164</td>\n",
              "      <td>-161</td>\n",
              "      <td>-59</td>\n",
              "      <td>243</td>\n",
              "      <td>-117</td>\n",
              "      <td>-103</td>\n",
              "      <td>-14</td>\n",
              "      <td>118</td>\n",
              "      <td>-157</td>\n",
              "      <td>140</td>\n",
              "      <td>-150</td>\n",
              "      <td>...</td>\n",
              "      <td>167</td>\n",
              "      <td>-13</td>\n",
              "      <td>-188</td>\n",
              "      <td>-89</td>\n",
              "      <td>-117</td>\n",
              "      <td>-57</td>\n",
              "      <td>-47</td>\n",
              "      <td>-33</td>\n",
              "      <td>-28</td>\n",
              "      <td>-175</td>\n",
              "      <td>-50</td>\n",
              "      <td>-216</td>\n",
              "      <td>-129</td>\n",
              "      <td>-130</td>\n",
              "      <td>-138</td>\n",
              "      <td>-155</td>\n",
              "      <td>-169</td>\n",
              "      <td>-178</td>\n",
              "      <td>-102</td>\n",
              "      <td>-124</td>\n",
              "      <td>-32</td>\n",
              "      <td>-51</td>\n",
              "      <td>-136</td>\n",
              "      <td>-98</td>\n",
              "      <td>-123</td>\n",
              "      <td>-112</td>\n",
              "      <td>-55</td>\n",
              "      <td>-47</td>\n",
              "      <td>-84</td>\n",
              "      <td>-226</td>\n",
              "      <td>90</td>\n",
              "      <td>117</td>\n",
              "      <td>-8</td>\n",
              "      <td>150</td>\n",
              "      <td>130</td>\n",
              "      <td>159</td>\n",
              "      <td>-66</td>\n",
              "      <td>-144</td>\n",
              "      <td>-6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6596</th>\n",
              "      <td>6597</td>\n",
              "      <td>NON-MUSK-jp13</td>\n",
              "      <td>jp13_2+8</td>\n",
              "      <td>51</td>\n",
              "      <td>-121</td>\n",
              "      <td>-23</td>\n",
              "      <td>-106</td>\n",
              "      <td>-117</td>\n",
              "      <td>63</td>\n",
              "      <td>-161</td>\n",
              "      <td>79</td>\n",
              "      <td>-224</td>\n",
              "      <td>-30</td>\n",
              "      <td>-129</td>\n",
              "      <td>-54</td>\n",
              "      <td>-60</td>\n",
              "      <td>-86</td>\n",
              "      <td>-88</td>\n",
              "      <td>-307</td>\n",
              "      <td>-221</td>\n",
              "      <td>-39</td>\n",
              "      <td>-58</td>\n",
              "      <td>-12</td>\n",
              "      <td>-86</td>\n",
              "      <td>-184</td>\n",
              "      <td>-180</td>\n",
              "      <td>-61</td>\n",
              "      <td>221</td>\n",
              "      <td>-211</td>\n",
              "      <td>-168</td>\n",
              "      <td>-154</td>\n",
              "      <td>-63</td>\n",
              "      <td>243</td>\n",
              "      <td>-107</td>\n",
              "      <td>136</td>\n",
              "      <td>-121</td>\n",
              "      <td>116</td>\n",
              "      <td>-122</td>\n",
              "      <td>73</td>\n",
              "      <td>-155</td>\n",
              "      <td>...</td>\n",
              "      <td>167</td>\n",
              "      <td>7</td>\n",
              "      <td>-170</td>\n",
              "      <td>43</td>\n",
              "      <td>-6</td>\n",
              "      <td>-61</td>\n",
              "      <td>-47</td>\n",
              "      <td>-43</td>\n",
              "      <td>-23</td>\n",
              "      <td>-176</td>\n",
              "      <td>-33</td>\n",
              "      <td>-212</td>\n",
              "      <td>-131</td>\n",
              "      <td>-113</td>\n",
              "      <td>-139</td>\n",
              "      <td>-153</td>\n",
              "      <td>-170</td>\n",
              "      <td>-178</td>\n",
              "      <td>-103</td>\n",
              "      <td>-114</td>\n",
              "      <td>-42</td>\n",
              "      <td>-66</td>\n",
              "      <td>-141</td>\n",
              "      <td>-96</td>\n",
              "      <td>-123</td>\n",
              "      <td>-113</td>\n",
              "      <td>-46</td>\n",
              "      <td>124</td>\n",
              "      <td>-11</td>\n",
              "      <td>-238</td>\n",
              "      <td>86</td>\n",
              "      <td>99</td>\n",
              "      <td>-14</td>\n",
              "      <td>-31</td>\n",
              "      <td>106</td>\n",
              "      <td>171</td>\n",
              "      <td>-44</td>\n",
              "      <td>-116</td>\n",
              "      <td>117</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6597</th>\n",
              "      <td>6598</td>\n",
              "      <td>NON-MUSK-jp13</td>\n",
              "      <td>jp13_2+9</td>\n",
              "      <td>51</td>\n",
              "      <td>-122</td>\n",
              "      <td>-23</td>\n",
              "      <td>-106</td>\n",
              "      <td>-117</td>\n",
              "      <td>190</td>\n",
              "      <td>-161</td>\n",
              "      <td>80</td>\n",
              "      <td>-227</td>\n",
              "      <td>-52</td>\n",
              "      <td>-139</td>\n",
              "      <td>-63</td>\n",
              "      <td>-51</td>\n",
              "      <td>-79</td>\n",
              "      <td>-99</td>\n",
              "      <td>-122</td>\n",
              "      <td>-220</td>\n",
              "      <td>-35</td>\n",
              "      <td>-57</td>\n",
              "      <td>6</td>\n",
              "      <td>-104</td>\n",
              "      <td>-183</td>\n",
              "      <td>-196</td>\n",
              "      <td>-51</td>\n",
              "      <td>63</td>\n",
              "      <td>-204</td>\n",
              "      <td>-169</td>\n",
              "      <td>-154</td>\n",
              "      <td>63</td>\n",
              "      <td>128</td>\n",
              "      <td>-108</td>\n",
              "      <td>135</td>\n",
              "      <td>-121</td>\n",
              "      <td>42</td>\n",
              "      <td>-122</td>\n",
              "      <td>73</td>\n",
              "      <td>-90</td>\n",
              "      <td>...</td>\n",
              "      <td>98</td>\n",
              "      <td>-57</td>\n",
              "      <td>-152</td>\n",
              "      <td>43</td>\n",
              "      <td>-4</td>\n",
              "      <td>-69</td>\n",
              "      <td>-51</td>\n",
              "      <td>-37</td>\n",
              "      <td>-23</td>\n",
              "      <td>-175</td>\n",
              "      <td>-33</td>\n",
              "      <td>-209</td>\n",
              "      <td>-108</td>\n",
              "      <td>-125</td>\n",
              "      <td>-139</td>\n",
              "      <td>-153</td>\n",
              "      <td>-170</td>\n",
              "      <td>-178</td>\n",
              "      <td>-102</td>\n",
              "      <td>-115</td>\n",
              "      <td>-42</td>\n",
              "      <td>-66</td>\n",
              "      <td>-141</td>\n",
              "      <td>-96</td>\n",
              "      <td>-110</td>\n",
              "      <td>-113</td>\n",
              "      <td>-46</td>\n",
              "      <td>124</td>\n",
              "      <td>-11</td>\n",
              "      <td>95</td>\n",
              "      <td>40</td>\n",
              "      <td>124</td>\n",
              "      <td>-14</td>\n",
              "      <td>-30</td>\n",
              "      <td>107</td>\n",
              "      <td>171</td>\n",
              "      <td>-44</td>\n",
              "      <td>-115</td>\n",
              "      <td>118</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 170 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        ID  molecule_name conformation_name  f1  ...  f164  f165  f166  class\n",
              "6593  6594  NON-MUSK-jp13          jp13_2+5  51  ...   -44  -115   118      0\n",
              "6594  6595  NON-MUSK-jp13          jp13_2+6  44  ...   -66  -144    -5      0\n",
              "6595  6596  NON-MUSK-jp13          jp13_2+7  44  ...   -66  -144    -6      0\n",
              "6596  6597  NON-MUSK-jp13          jp13_2+8  51  ...   -44  -116   117      0\n",
              "6597  6598  NON-MUSK-jp13          jp13_2+9  51  ...   -44  -115   118      0\n",
              "\n",
              "[5 rows x 170 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "roL5VVW9uLyl",
        "outputId": "d1928dfc-7f31-43a1-e49c-7d156348f8c6"
      },
      "source": [
        "data.info"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.info of         ID  molecule_name conformation_name  f1  ...  f164  f165  f166  class\n",
              "0        1       MUSK-211           211_1+1  46  ...   -50  -112    96      1\n",
              "1        2       MUSK-211          211_1+10  41  ...   -61  -136    79      1\n",
              "2        3       MUSK-211          211_1+11  46  ...   -67  -145    39      1\n",
              "3        4       MUSK-211          211_1+12  41  ...   -60  -135    80      1\n",
              "4        5       MUSK-211          211_1+13  41  ...   -60  -135    80      1\n",
              "...    ...            ...               ...  ..  ...   ...   ...   ...    ...\n",
              "6593  6594  NON-MUSK-jp13          jp13_2+5  51  ...   -44  -115   118      0\n",
              "6594  6595  NON-MUSK-jp13          jp13_2+6  44  ...   -66  -144    -5      0\n",
              "6595  6596  NON-MUSK-jp13          jp13_2+7  44  ...   -66  -144    -6      0\n",
              "6596  6597  NON-MUSK-jp13          jp13_2+8  51  ...   -44  -116   117      0\n",
              "6597  6598  NON-MUSK-jp13          jp13_2+9  51  ...   -44  -115   118      0\n",
              "\n",
              "[6598 rows x 170 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TcipbEkyuN09",
        "outputId": "9ebf41b6-c280-4927-98ae-387089f4de21"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6598, 170)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xDnGOErVubfm",
        "outputId": "830fa85d-f89b-4935-c29a-ecc58820c3b6"
      },
      "source": [
        "data['class'].value_counts()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    5581\n",
              "1    1017\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QTWzut29uoPh",
        "outputId": "d65dbf5c-4342-4591-ab29-d9b2771fceda"
      },
      "source": [
        "data['molecule_name'].value_counts()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NON-MUSK-j146    1044\n",
              "NON-MUSK-252     1010\n",
              "NON-MUSK-j147     911\n",
              "NON-MUSK-f146     383\n",
              "NON-MUSK-288      344\n",
              "                 ... \n",
              "NON-MUSK-308        2\n",
              "NON-MUSK-295        2\n",
              "NON-MUSK-358        2\n",
              "NON-MUSK-j97        1\n",
              "NON-MUSK-j96        1\n",
              "Name: molecule_name, Length: 102, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "whaGdDk4uxwl"
      },
      "source": [
        "First, perform a multi-class classification of 102 types of moelcules in the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O1xgJkcqvCrO",
        "outputId": "cc174abd-eaec-49ff-9e03-8f93c6455e18"
      },
      "source": [
        "data.isnull().values.any()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mQGbaa-dvaYf",
        "outputId": "6c1e8324-15d9-47c2-e529-076196517de5"
      },
      "source": [
        "data.nunique()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                   6598\n",
              "molecule_name         102\n",
              "conformation_name    6598\n",
              "f1                    202\n",
              "f2                    260\n",
              "                     ... \n",
              "f163                  292\n",
              "f164                  172\n",
              "f165                  352\n",
              "f166                  385\n",
              "class                   2\n",
              "Length: 170, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DYRClBbwvw5X"
      },
      "source": [
        "X = data.iloc[:,3:-1].values"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uhng8vnbwZgy",
        "outputId": "8f838203-cf7a-41e9-e1e6-cfae2fb79376"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6598, 166)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G1_M6bCswbJ2"
      },
      "source": [
        "Y = data.iloc[:, 1].values"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E86gif_Fwk6R",
        "outputId": "d4c69b3b-e154-4eb7-824b-23f9dd99dce6"
      },
      "source": [
        "Y.shape"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6598,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7rNne5rtwmi8",
        "outputId": "1e39be39-72fe-4ec1-ffe9-4dd65a5260f8"
      },
      "source": [
        "np.unique(Y).shape"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(102,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kMB-gVoXww4v"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, f1_score, recall_score, precision_score"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lGHPMg_5xcIo"
      },
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\r\n",
        "Y = OneHotEncoder().fit_transform(Y.reshape(-1,1)).toarray()"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJICs0bMyDrq"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\r\n",
        "X = StandardScaler().fit_transform(X)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3o1aFa2ryaHm"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\r\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size = 0.8, test_size = 0.2)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UOyF0nMCy70S"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H9RjH4xOzDBi"
      },
      "source": [
        "import keras\r\n",
        "from keras.layers import Dense"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vCeAzDFszLUg"
      },
      "source": [
        "model = keras.Sequential()\r\n",
        "model.add(Dense(300, activation= 'softmax'))\r\n",
        "model.add(Dense(200, activation= 'relu'))\r\n",
        "model.add(Dense(102, activation= 'sigmoid'))\r\n",
        "model.compile(optimizer = 'adam', loss = 'mse', metrics= ['accuracy'])"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ww699mTi0c_W",
        "outputId": "fb2e6ae7-5f0f-44c3-e00e-de4bbd658509"
      },
      "source": [
        "model.fit(X_train, Y_train, verbose=1, validation_data=(X_test, Y_test), epochs=100)\r\n",
        "model.save('model_musk.h5')"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "165/165 [==============================] - 2s 6ms/step - loss: 0.1474 - accuracy: 0.0314 - val_loss: 0.0092 - val_accuracy: 0.1621\n",
            "Epoch 2/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0091 - accuracy: 0.1667 - val_loss: 0.0084 - val_accuracy: 0.2674\n",
            "Epoch 3/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0082 - accuracy: 0.3430 - val_loss: 0.0069 - val_accuracy: 0.4311\n",
            "Epoch 4/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0067 - accuracy: 0.4476 - val_loss: 0.0057 - val_accuracy: 0.5492\n",
            "Epoch 5/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0057 - accuracy: 0.5400 - val_loss: 0.0049 - val_accuracy: 0.6523\n",
            "Epoch 6/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0048 - accuracy: 0.6346 - val_loss: 0.0042 - val_accuracy: 0.7227\n",
            "Epoch 7/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0042 - accuracy: 0.7104 - val_loss: 0.0036 - val_accuracy: 0.7409\n",
            "Epoch 8/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0037 - accuracy: 0.7315 - val_loss: 0.0032 - val_accuracy: 0.7591\n",
            "Epoch 9/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0033 - accuracy: 0.7456 - val_loss: 0.0028 - val_accuracy: 0.7621\n",
            "Epoch 10/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0030 - accuracy: 0.7521 - val_loss: 0.0027 - val_accuracy: 0.7712\n",
            "Epoch 11/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0029 - accuracy: 0.7583 - val_loss: 0.0026 - val_accuracy: 0.7811\n",
            "Epoch 12/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0027 - accuracy: 0.7742 - val_loss: 0.0025 - val_accuracy: 0.7947\n",
            "Epoch 13/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0027 - accuracy: 0.7893 - val_loss: 0.0024 - val_accuracy: 0.8076\n",
            "Epoch 14/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0027 - accuracy: 0.7886 - val_loss: 0.0023 - val_accuracy: 0.8129\n",
            "Epoch 15/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0024 - accuracy: 0.8145 - val_loss: 0.0022 - val_accuracy: 0.8303\n",
            "Epoch 16/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0024 - accuracy: 0.8079 - val_loss: 0.0021 - val_accuracy: 0.8341\n",
            "Epoch 17/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0023 - accuracy: 0.8179 - val_loss: 0.0021 - val_accuracy: 0.8439\n",
            "Epoch 18/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0021 - accuracy: 0.8308 - val_loss: 0.0020 - val_accuracy: 0.8485\n",
            "Epoch 19/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0022 - accuracy: 0.8275 - val_loss: 0.0020 - val_accuracy: 0.8447\n",
            "Epoch 20/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0021 - accuracy: 0.8349 - val_loss: 0.0019 - val_accuracy: 0.8500\n",
            "Epoch 21/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0021 - accuracy: 0.8414 - val_loss: 0.0019 - val_accuracy: 0.8553\n",
            "Epoch 22/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0019 - accuracy: 0.8499 - val_loss: 0.0019 - val_accuracy: 0.8538\n",
            "Epoch 23/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0020 - accuracy: 0.8466 - val_loss: 0.0018 - val_accuracy: 0.8553\n",
            "Epoch 24/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0019 - accuracy: 0.8575 - val_loss: 0.0018 - val_accuracy: 0.8689\n",
            "Epoch 25/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0019 - accuracy: 0.8619 - val_loss: 0.0018 - val_accuracy: 0.8682\n",
            "Epoch 26/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0019 - accuracy: 0.8553 - val_loss: 0.0017 - val_accuracy: 0.8682\n",
            "Epoch 27/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0018 - accuracy: 0.8623 - val_loss: 0.0017 - val_accuracy: 0.8712\n",
            "Epoch 28/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0017 - accuracy: 0.8758 - val_loss: 0.0017 - val_accuracy: 0.8788\n",
            "Epoch 29/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0017 - accuracy: 0.8766 - val_loss: 0.0016 - val_accuracy: 0.8803\n",
            "Epoch 30/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0017 - accuracy: 0.8776 - val_loss: 0.0016 - val_accuracy: 0.8848\n",
            "Epoch 31/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0016 - accuracy: 0.8865 - val_loss: 0.0015 - val_accuracy: 0.8856\n",
            "Epoch 32/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0016 - accuracy: 0.8829 - val_loss: 0.0015 - val_accuracy: 0.8894\n",
            "Epoch 33/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0016 - accuracy: 0.8828 - val_loss: 0.0015 - val_accuracy: 0.8841\n",
            "Epoch 34/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0016 - accuracy: 0.8859 - val_loss: 0.0015 - val_accuracy: 0.8879\n",
            "Epoch 35/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0015 - accuracy: 0.8885 - val_loss: 0.0014 - val_accuracy: 0.8924\n",
            "Epoch 36/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0014 - accuracy: 0.9000 - val_loss: 0.0014 - val_accuracy: 0.8977\n",
            "Epoch 37/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0014 - accuracy: 0.8973 - val_loss: 0.0014 - val_accuracy: 0.8977\n",
            "Epoch 38/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0013 - accuracy: 0.9086 - val_loss: 0.0013 - val_accuracy: 0.9008\n",
            "Epoch 39/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0013 - accuracy: 0.9055 - val_loss: 0.0013 - val_accuracy: 0.9030\n",
            "Epoch 40/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0013 - accuracy: 0.9094 - val_loss: 0.0012 - val_accuracy: 0.9083\n",
            "Epoch 41/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0012 - accuracy: 0.9084 - val_loss: 0.0012 - val_accuracy: 0.9083\n",
            "Epoch 42/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0011 - accuracy: 0.9170 - val_loss: 0.0012 - val_accuracy: 0.9136\n",
            "Epoch 43/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0011 - accuracy: 0.9236 - val_loss: 0.0012 - val_accuracy: 0.9129\n",
            "Epoch 44/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0011 - accuracy: 0.9265 - val_loss: 0.0011 - val_accuracy: 0.9174\n",
            "Epoch 45/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 9.8848e-04 - accuracy: 0.9361 - val_loss: 0.0011 - val_accuracy: 0.9159\n",
            "Epoch 46/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0010 - accuracy: 0.9277 - val_loss: 0.0010 - val_accuracy: 0.9227\n",
            "Epoch 47/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 9.9106e-04 - accuracy: 0.9346 - val_loss: 0.0010 - val_accuracy: 0.9182\n",
            "Epoch 48/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 9.1232e-04 - accuracy: 0.9377 - val_loss: 0.0010 - val_accuracy: 0.9220\n",
            "Epoch 49/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 9.2974e-04 - accuracy: 0.9356 - val_loss: 9.7649e-04 - val_accuracy: 0.9273\n",
            "Epoch 50/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 8.4191e-04 - accuracy: 0.9427 - val_loss: 9.6001e-04 - val_accuracy: 0.9265\n",
            "Epoch 51/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 8.5686e-04 - accuracy: 0.9413 - val_loss: 9.6674e-04 - val_accuracy: 0.9265\n",
            "Epoch 52/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 8.2001e-04 - accuracy: 0.9444 - val_loss: 9.3588e-04 - val_accuracy: 0.9295\n",
            "Epoch 53/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 8.4463e-04 - accuracy: 0.9430 - val_loss: 9.1081e-04 - val_accuracy: 0.9318\n",
            "Epoch 54/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 8.0122e-04 - accuracy: 0.9474 - val_loss: 8.9852e-04 - val_accuracy: 0.9295\n",
            "Epoch 55/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 8.3078e-04 - accuracy: 0.9441 - val_loss: 8.8289e-04 - val_accuracy: 0.9280\n",
            "Epoch 56/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 7.7370e-04 - accuracy: 0.9445 - val_loss: 8.9849e-04 - val_accuracy: 0.9311\n",
            "Epoch 57/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 7.2748e-04 - accuracy: 0.9497 - val_loss: 8.5058e-04 - val_accuracy: 0.9348\n",
            "Epoch 58/100\n",
            "165/165 [==============================] - 1s 5ms/step - loss: 7.3976e-04 - accuracy: 0.9463 - val_loss: 8.4078e-04 - val_accuracy: 0.9348\n",
            "Epoch 59/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 7.5930e-04 - accuracy: 0.9475 - val_loss: 8.3106e-04 - val_accuracy: 0.9311\n",
            "Epoch 60/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 6.6475e-04 - accuracy: 0.9533 - val_loss: 8.3512e-04 - val_accuracy: 0.9371\n",
            "Epoch 61/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 6.9521e-04 - accuracy: 0.9492 - val_loss: 8.9164e-04 - val_accuracy: 0.9348\n",
            "Epoch 62/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 7.6150e-04 - accuracy: 0.9445 - val_loss: 7.9794e-04 - val_accuracy: 0.9379\n",
            "Epoch 63/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 6.8642e-04 - accuracy: 0.9457 - val_loss: 7.8097e-04 - val_accuracy: 0.9417\n",
            "Epoch 64/100\n",
            "165/165 [==============================] - 1s 5ms/step - loss: 6.2266e-04 - accuracy: 0.9538 - val_loss: 7.8541e-04 - val_accuracy: 0.9379\n",
            "Epoch 65/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 6.5457e-04 - accuracy: 0.9504 - val_loss: 7.5636e-04 - val_accuracy: 0.9432\n",
            "Epoch 66/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 6.0811e-04 - accuracy: 0.9590 - val_loss: 7.4017e-04 - val_accuracy: 0.9432\n",
            "Epoch 67/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 6.1214e-04 - accuracy: 0.9575 - val_loss: 8.5319e-04 - val_accuracy: 0.9341\n",
            "Epoch 68/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 6.7402e-04 - accuracy: 0.9516 - val_loss: 7.4035e-04 - val_accuracy: 0.9417\n",
            "Epoch 69/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 5.8983e-04 - accuracy: 0.9559 - val_loss: 7.5654e-04 - val_accuracy: 0.9424\n",
            "Epoch 70/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 5.7603e-04 - accuracy: 0.9593 - val_loss: 7.2052e-04 - val_accuracy: 0.9470\n",
            "Epoch 71/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 5.3842e-04 - accuracy: 0.9595 - val_loss: 7.0088e-04 - val_accuracy: 0.9492\n",
            "Epoch 72/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 5.7108e-04 - accuracy: 0.9590 - val_loss: 6.9576e-04 - val_accuracy: 0.9417\n",
            "Epoch 73/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 4.8403e-04 - accuracy: 0.9659 - val_loss: 6.8414e-04 - val_accuracy: 0.9492\n",
            "Epoch 74/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 5.6089e-04 - accuracy: 0.9617 - val_loss: 7.6568e-04 - val_accuracy: 0.9477\n",
            "Epoch 75/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 5.6615e-04 - accuracy: 0.9582 - val_loss: 6.9197e-04 - val_accuracy: 0.9515\n",
            "Epoch 76/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 5.3729e-04 - accuracy: 0.9614 - val_loss: 6.4070e-04 - val_accuracy: 0.9568\n",
            "Epoch 77/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 5.3042e-04 - accuracy: 0.9647 - val_loss: 6.5363e-04 - val_accuracy: 0.9576\n",
            "Epoch 78/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 5.5444e-04 - accuracy: 0.9609 - val_loss: 6.3188e-04 - val_accuracy: 0.9530\n",
            "Epoch 79/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 4.9744e-04 - accuracy: 0.9654 - val_loss: 5.9667e-04 - val_accuracy: 0.9583\n",
            "Epoch 80/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 5.1050e-04 - accuracy: 0.9638 - val_loss: 6.1995e-04 - val_accuracy: 0.9576\n",
            "Epoch 81/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 4.6901e-04 - accuracy: 0.9674 - val_loss: 6.0773e-04 - val_accuracy: 0.9583\n",
            "Epoch 82/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 5.1370e-04 - accuracy: 0.9607 - val_loss: 5.6911e-04 - val_accuracy: 0.9614\n",
            "Epoch 83/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 4.6607e-04 - accuracy: 0.9684 - val_loss: 5.7670e-04 - val_accuracy: 0.9598\n",
            "Epoch 84/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 4.8236e-04 - accuracy: 0.9669 - val_loss: 5.7697e-04 - val_accuracy: 0.9606\n",
            "Epoch 85/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 4.1810e-04 - accuracy: 0.9691 - val_loss: 6.0163e-04 - val_accuracy: 0.9629\n",
            "Epoch 86/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 5.1017e-04 - accuracy: 0.9655 - val_loss: 6.3784e-04 - val_accuracy: 0.9568\n",
            "Epoch 87/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 4.2450e-04 - accuracy: 0.9707 - val_loss: 5.5804e-04 - val_accuracy: 0.9652\n",
            "Epoch 88/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 4.3032e-04 - accuracy: 0.9695 - val_loss: 5.5453e-04 - val_accuracy: 0.9652\n",
            "Epoch 89/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 4.3098e-04 - accuracy: 0.9723 - val_loss: 5.9133e-04 - val_accuracy: 0.9614\n",
            "Epoch 90/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 4.3376e-04 - accuracy: 0.9709 - val_loss: 5.7052e-04 - val_accuracy: 0.9614\n",
            "Epoch 91/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 5.0986e-04 - accuracy: 0.9663 - val_loss: 5.7914e-04 - val_accuracy: 0.9614\n",
            "Epoch 92/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 4.5341e-04 - accuracy: 0.9678 - val_loss: 5.6722e-04 - val_accuracy: 0.9644\n",
            "Epoch 93/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 4.3542e-04 - accuracy: 0.9679 - val_loss: 5.5177e-04 - val_accuracy: 0.9629\n",
            "Epoch 94/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 4.0385e-04 - accuracy: 0.9729 - val_loss: 5.2177e-04 - val_accuracy: 0.9636\n",
            "Epoch 95/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 4.0223e-04 - accuracy: 0.9711 - val_loss: 5.1368e-04 - val_accuracy: 0.9636\n",
            "Epoch 96/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 3.6496e-04 - accuracy: 0.9738 - val_loss: 5.0438e-04 - val_accuracy: 0.9674\n",
            "Epoch 97/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 3.6730e-04 - accuracy: 0.9725 - val_loss: 5.1902e-04 - val_accuracy: 0.9667\n",
            "Epoch 98/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 3.7433e-04 - accuracy: 0.9736 - val_loss: 5.2651e-04 - val_accuracy: 0.9667\n",
            "Epoch 99/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 4.3564e-04 - accuracy: 0.9687 - val_loss: 5.0278e-04 - val_accuracy: 0.9659\n",
            "Epoch 100/100\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 3.7479e-04 - accuracy: 0.9746 - val_loss: 5.2846e-04 - val_accuracy: 0.9644\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TUTzL1YR1vyK"
      },
      "source": [
        "train_accuracy = model.history.history['accuracy']\r\n",
        "validation_accuracy = model.history.history['val_accuracy']\r\n",
        "\r\n",
        "train_loss = model.history.history['loss']\r\n",
        "validation_loss = model.history.history['val_loss']"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "XUh--sCS20XV",
        "outputId": "efb51544-ffaa-409e-df97-87547e26e3f7"
      },
      "source": [
        "plt.plot(train_accuracy, label = \"training_accuracy\")\r\n",
        "plt.plot(validation_accuracy, label = \"validation_accuracy\")\r\n",
        "plt.legend()\r\n",
        "plt.show()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "zezzh6uN3Ryo",
        "outputId": "a84d2168-e322-4255-a430-11a2991eef68"
      },
      "source": [
        "plt.plot(train_loss, label = \"training_loss\")\r\n",
        "plt.plot(validation_loss, label = \"validation_loss\")\r\n",
        "plt.legend()\r\n",
        "plt.show()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MzKqSA8DpbN6336nUohcR2VKHffRJn/s1xKEdAve7+wIzmwTMdvepwH3AQ2a2EFhFfDDA3Veb2W3EBwsHprn7b7toW3TPWBGRdpQ1jt7dpxF3u7RedkOr53lg7Fbe+zPiIZZdLhOo60ZEpK1UTYGgrhsRkS2lK+h1c3ARkS2kK+h1c3ARkS2kK+hDI3KI1KoXEWmRrqAP4omZ1H0jIrJJuoI+jDdHM1iKiGySrqBPWvQFTWwmItIilUFf0sRmIiItUhX0YdJ1oxa9iMgmqQr6bPPJWLXoRURapCrow+auG52MFRFpkaqgzzZ33eiiKRGRFqkKerXoRUS2lKqgz4bJ8Er10YuItEhV0IeBLpgSEWkrVUGfCXXBlIhIW+kKevXRi4hsIWVBr1E3IiJtpSvoQ7XoRUTaKivozWyUmb1hZgvNbGI76yvN7NFk/YtmNiRZPsTMGsxsXvK4q3Orv7mMrowVEdlChzcHN7MQmAx8ClgKzDKzqe7+aqtiVwCr3f1gM7sYuAUYl6x7292Hd3K929V8wZTmoxcR2aScFv0IYKG7L3L3JuARYHSbMqOBB5LnU4CRZmadV83yhC0tevXRi4g0KyfoBwFLWr1emixrt4y7F4E1QHWybqiZ/c3M/svMTm3vC8xsgpnNNrPZdXV127UBrTVfMKUWvYjIJl19MnYFcKC7Hwt8HXjYzPZpW8jd73b3Wnevramp2eEva75gqqhx9CIiLcoJ+mXAAa1eD06WtVvGzDJAH6De3RvdvR7A3ecAbwOH7mylt0YnY0VEtlRO0M8CDjGzoWZWAVwMTG1TZipwafJ8DPCcu7uZ1SQnczGzg4BDgEWdU/UtZdR1IyKyhQ5H3bh70cyuAaYDIXC/uy8ws0nAbHefCtwHPGRmC4FVxAcDgNOASWZWACLgK+6+qis2BDZdMKWgFxHZpMOgB3D3acC0NstuaPU8D4xt531PAE/sZB3LltGoGxGRLejKWBGRlEtX0LfMdaOgFxFplq6gD9V1IyLSVrqCPtCoGxGRtlIV9GZGGJgumBIRaSVVQQ8kQa8WvYhIs9QFfTYwXRkrItJK6oI+DEzDK0VEWkld0GfDQLcSFBFpJXVBrxa9iMjmUhf0cYteQS8i0ix1QR+36NV1IyLSLHVBnwmNgrpuRERapC/oA6OkrhsRkRYpDPpAV8aKiLSSuqDPhroyVkSktdQFfagrY0VENpO6oM+E6roREWktfUGvFr2IyGbKCnozG2Vmb5jZQjOb2M76SjN7NFn/opkNabP+QDNbb2bf6Jxqb13colfQi4g06zDozSwEJgNnA8OAS8xsWJtiVwCr3f1g4HbgljbrbwOe3vnqdiyj+ehFRDZTTot+BLDQ3Re5exPwCDC6TZnRwAPJ8ynASDMzADM7H3gHWNA5Vd42dd2IiGyunKAfBCxp9XppsqzdMu5eBNYA1WbWC/ifwE3b+gIzm2Bms81sdl1dXbl1b1dGwytFRDbT1SdjbwRud/f12yrk7ne7e62719bU1OzUF2aCQDcHFxFpJVNGmWXAAa1eD06WtVdmqZllgD5APXAiMMbMvgf0BSIzy7v7j3a65luR0a0ERUQ2U07QzwIOMbOhxIF+MfC5NmWmApcCLwBjgOfc3YFTmwuY2Y3A+q4MeUi6btRHLyLSosOgd/eimV0DTAdC4H53X2Bmk4DZ7j4VuA94yMwWAquIDwbdIgw0vFJEpLVyWvS4+zRgWptlN7R6ngfGdvAZN+5A/bZbPNeN+uhFRJql7srYUNMUi4hsJnVBnw0DCmrRi4i0SF3Q6+bgIiKbS13QZwOjUHLiQT8iIpK6oA+DeJPUqBcRiaUu6DOhAVDQ1bEiIkAagz6Ig1799CIisfQFfRhvkq6OFRGJpS/okxa9LpoSEYmlL+jD5qBXi15EBFIY9Nlk1I2CXkQklrqgD5u7bjTqRkQESGHQq+tGRGRz6Qv6QKNuRERaS1/Q64IpEZHNpC/odcGUiMhm0hf0zRdMaRy9iAiQxqBvGXWjFr2ICKQ56NV1IyIClBn0ZjbKzN4ws4VmNrGd9ZVm9miy/kUzG5IsH2Fm85LHfDO7oHOrvyUNrxQR2VyHQW9mITAZOBsYBlxiZsPaFLsCWO3uBwO3A7cky18Bat19ODAK+ImZlXVD8h21aXil+uhFRKC8Fv0IYKG7L3L3JuARYHSbMqOBB5LnU4CRZmbuvtHdi8nyHNDlzexQXTciIpspJ+gHAUtavV6aLGu3TBLsa4BqADM70cwWAC8DX2kV/C3MbIKZzTaz2XV1ddu/Fa1kNU2xiMhmuvxkrLu/6O5HAicA3zKzXDtl7nb3Wnevramp2anvCzVNsYjIZsoJ+mXAAa1eD06WtVsm6YPvA9S3LuDurwHrgaN2tLLlyIYaXiki0lo5QT8LOMTMhppZBXAxMLVNmanApcnzMcBz7u7JezIAZvZ3wOHA4k6p+VaEujJWRGQzHY6AcfeimV0DTAdC4H53X2Bmk4DZ7j4VuA94yMwWAquIDwYAnwAmmlkBiIB/cveVXbEhzZr76AvquhERAcoIegB3nwZMa7PshlbP88DYdt73EPDQTtZxu6hFLyKyudRdGdt8h6mC+uhFRIAUBn0YNrfo1XUjIgIpDPrmuW7UohcRiaU26NVHLyISS13Q6+bgIiKbS13QmxnZ0CioRS8iAqQw6CFu1avrRkQklsqgzwaBbg4uIpJIZdCHoVr0IiLNUhn0mSDQ8EoRkURKg950wZSISCKdQR+apikWEUmkM+gD060ERUQS6Qz6MNAdpkREEukM+kBdNyIizdIZ9KG6bkREmqUy6MMgUNCLiCRSGfTZwDSpmYhIIpVBH2rUjYhIi7KC3sxGmdkbZrbQzCa2s77SzB5N1r9oZkOS5Z8yszlm9nLy88zOrX77smGgFr2ISKLDoDezEJgMnA0MAy4xs2Ftil0BrHb3g4HbgVuS5SuBz7j7x4BL2UU3CtfslSIim5TToh8BLHT3Re7eBDwCjG5TZjTwQPJ8CjDSzMzd/+buy5PlC4AeZlbZGRXflmxomutGRCRRTtAPApa0er00WdZuGXcvAmuA6jZlPgvMdffGtl9gZhPMbLaZza6rqyu37lulFr2IyCa75GSsmR1J3J3zj+2td/e73b3W3Wtramp2+vsyYUBBV8aKiADlBf0y4IBWrwcny9otY2YZoA9Qn7weDDwFfMnd397ZCpcjoxa9iEiLcoJ+FnCImQ01swrgYmBqmzJTiU+2AowBnnN3N7O+wG+Bie7+p86qdEcyQaApEEREEh0GfdLnfg0wHXgNeMzdF5jZJDM7Lyl2H1BtZguBrwPNQzCvAQ4GbjCzecnjI52+FW3EJ2PVdSMiApApp5C7TwOmtVl2Q6vneWBsO++7Gbh5J+u43XQyVkRkk1ReGZsNdXNwEZFmqQx6tehFRDZJZdBnQqOgoBcRAdIa9GrRi4i0SGnQB5Qix11hLyKS0qA3AE1VLCJCWoM+jDdLF02JiKQ16Fta9BpiKSKSzqAPk6BXi15EJKVBrz56EZEW6Qz65j56dd2IiKQz6MNAXTciIs1SGfTZUF03IiLNUhn0YRBvVkldNyIi6Qz6bNJ1oxuEi4ikNOib++g1342ISEqDPpuMutGc9CIiKQ36UOPoRURapDLodWWsiMgmZQW9mY0yszfMbKGZTWxnfaWZPZqsf9HMhiTLq81shpmtN7MfdW7Vty4T6IIpEZFmHQa9mYXAZOBsYBhwiZkNa1PsCmC1ux8M3A7ckizPA/8GfKPTalyGjMbRi4i0KKdFPwJY6O6L3L0JeAQY3abMaOCB5PkUYKSZmbtvcPfniQN/l8kGmqZYRKRZOUE/CFjS6vXSZFm7Zdy9CKwBqsuthJlNMLPZZja7rq6u3Ldt1abhleq6ERHZLU7Guvvd7l7r7rU1NTU79iHFRvjP/wXr61qmQNAFUyIi5QX9MuCAVq8HJ8vaLWNmGaAPUN8ZFSzb0tkw6x6482R6L/4dAI1FtehFRMoJ+lnAIWY21MwqgIuBqW3KTAUuTZ6PAZ7zXX1n7iGnwIT/gt4DGfj05fwgdw8/fHoer61Yu0urISKyu+kw6JM+92uA6cBrwGPuvsDMJpnZeUmx+4BqM1sIfB1oGYJpZouB24DxZra0nRE7nWfAMLjyOTj1G4y2/+Jfop9w0U/+zKzFq7rsK0VEdneZcgq5+zRgWptlN7R6ngfGbuW9Q3aiftsvUwEj/w3L5Bg142ZeqjyGL9zr3D/+BE45uP8urYqIyO5gtzgZ2yVO/ToMPY3rS/dyat+V/NPP5/Je/cburpWIyC6X3qAPQrjwHqyiJz+u/BGV3siEh2azobHY3TUTEdml0hv0AL0HwoV3U1H/Or898GcsfH8N10+Zz64+Tywi0p3SHfQAB4+Es75DzXv/ya8Omsq0l1fwnd++RqTpEURkL1HWydg93sevgfXvc+Sf7+D+oftw+fPGB+sa+f7Yo6nMhN1dOxGRLrV3BD3AJ2+CDXWcOf9efnFEjkvmn8wH6/L85Iu19OmR7e7aiYh0mfR33TQLAjjvP+DICzn5nR/xp4Me4PV3l3Puf/yROe+u7u7aiYh0mb0n6AHCLIy5Hz71vxm04hle6P9/GFJ6l4t+8gI/eOZNirr1oIik0N4V9ABmcMq18KVf0aPwIQ8WvsF9A5/i3mfmM3ryn3hx0a6dokdEpKvtfUHfbOhpcPWL2PDPc8aqx5ndZyIfX/s0l9z9Z6762RwWr9zQ3TUUEekUe2/QA/TsD+fdAV9+jlzNUP6lOJm/7vtt/I3pnPl/Z3DNw3N5Zdma7q6liMhOsd3t4qHa2lqfPXv2rv9id3j1l/DsJFi1iGW9juIH687kl4211B40gLG1gxl11ECqKvaegUoisucwsznuXtvuOgV9G6UCzH0AXvgxrHqbjdlqnvLTeWzDcN7OHsInh+3HiQdVc8KQffloTU/MrPvqKiKSUNDviCiCRc/BX+/B3/o95iXWZPozs3QUC5oGssj3o75iEFX9BtC3eiD77dub6p4V7NuzgupeFfStqqBfVQX9qrLsk8sSBDogiEjXUdDvrI2r4K3fweu/wZf8FVv//hZF1nhPVnkvPqQ3q70X66hinfeIf9KTQnYfSpV9KFR9hFKv/Qj22Z999+lFda9K+veqZJ8eGXpVZuidy7BPjyx9e1RQkdm7T6GISPm2FfTqcC5H1b5wzMVwzMUYQH4N1C+E1e/CxnrYWM8+G1ZStb6egetX4htXQeMygqZ1ZAvrCL0ADuSTR3IflLVexSrvzYf0Yq1XsZwerPMqNtCDDVRSDKqgogeWzWHZHnhFL4oVfYhy/fCKXni2Csv2IFtZRVVllp6VIbls8sgE5LIhPSpCeiTLKjNB8oiX60AisndQ0O+IXB8YdHz8SBiQTR6bcYdiHho+hIZVsG4FrF0B61bQa8NKKtbW0X/9SsivxZpWEjatIyhsJFvaiOFQJH40bLtKBQ9pIkMheTSSpdGz5KmggUo+9CwFMhTJ0ERIiZASGaIwSymooBTmKAU5okwOMjk804NStidU9IRsT7KVFeSyFVRWVBBW9oTKXgS5XmQqq8hW5KjMZsll44NILhtQVZGhVy5DVTZUt5VIN1PQdzUzyPaIH/vsBwOObFkVALmtvc8dCg3xQaL5Z+Pa5ICxGpo2xMsLG/FCA1ZoImhqIGxqxIqNZAqNVBUa8EIDFPJQbMBKBYjyWFTAoiJEBYKoQBg1kS02UuGN0Lhjm1nwEAcCnIxFNHlInkpWUkGeShqtkkbL0RjkKAQ5ikGOKKyEIIOFGQgrsEwllu0RH2iCLFGQIQorCSt7kanqTTbXi1w2S64iS64yS2VVL3I9+9CjZx8ylVWQqYSwIv6di0gLBf3uygwqquJHR0WJd+RO78zmvz4KDfGjaQM0rY9/RgWKxSL5xiaKjRuI8uso5dcRFRooNTUSFRopRU7BoRhBsVjAmzZC00as2EBQbKCi1EBVqYFMVE+21Eim0IRRIvAiGS9SQRM5Cju7FTSSpUiGomVxi2cnNaAxrGJjpg/5bF+iTBUWZrAgm/wMsCCDhVksU0mQzWGZimRdBgtCQosIADMjyFZgmVxStoIgU0mYyWJBiORCA/4AAAkGSURBVAUhQZAhyGQIwgxhGGLuEBXBI8jk4v2a7QlhBiwAC5OfySPeIZv2SdN6aFwHQRZ6VkPPmvivrR36Ba2LGwtV/cv69yV7vrKywcxGAT8EQuBed/9um/WVwIPA8UA9MM7dFyfrvgVcAZSAa919eqfVXjpX678+2pEBenV1HaKIUiGPl5qIigVKTQ00bFhHw/o1NG5cR0NTgcZCgXxTgVI+PuB44zooNuLFPBQb4+elRig2EUURJQePSlSWNtKrcS29G5aT8yZCL5KxEiERIRGGU0GRCgpUUCSw3WugQlslAiILicjgZnFXX6JoFZSCLJFliSzELYNRomdTPZXRpltq5sNebMjsC2aElAhwoiDuzisGlXiQhSCMf1oAWPzvxAwsxC0kLDVQ0bSaiqb44sJCRZ/4XFJFb6JsTzzbE8IsARHmpbh+hBQIKVmWMJsjU9mDTLaSwIsEURHzYstBkyAT/7WW7REfWM3IBBAaBGYEBobHn11qwotNRIU8USFPqZDHMjnCnvsS9tw3/ouxeRuiIhTyRIUGPCrGB/kwCxZSikpEUYR7RBhkCDPxwR5L6hNk4vtThxUQViYHaIt/Bq0O2lEJvJQc5D0+0LvHZcJsfOD2CEpN8dDuHn2h5rBO/7fSYdCbWQhMBj4FLAVmmdlUd3+1VbErgNXufrCZXQzcAowzs2HAxcCRwP7AM2Z2qHuyt0XaCgLCyipgU0uzRxfd093dyRcimooRhSiiUIrYUIzIFyLyTUUKxSaKxVJ8wCkViYBiZJSiiKgQH0hKxTxeLODFRqJiIf5P7SW8VMK9BKUiUVQiwih5SAnwQiMUNmCFjVhUAo8D0N0xj8BLlCKnGDmFCApUkA96kA96EHiJXqUP6V1cTWW0EY+KcZhEJWiJeicTFQhLBcKoQEBE6CUiYJUdRX2wL+utN31YS01pFf2KayhFUMSICMhSJEeBSprIkie0EllKGBGWfEuAExIRENFABR96b1bzdwD0YQN9rI7evEuVNVJFngqKRASUkovxc8SfWUGBsJMPqEbcIg0B95AK23bctDckobu6Ohb0+yRHXvdEp39uOdszAljo7osAzOwRYDTQOuhHAzcmz6cAP7L4SqLRwCPu3gi8Y2YLk897oXOqL7LjzCwelVShm88AFEoR+UKp5e8Cd4iSA04xioiS15F73DgFSpGzj0FfM4YaRA7FUkRTKaKh5KyLnGIpohQ5kUMpGc7dPCosMGNjPk9jwwby+TxFi8/NlDwg8hIeRVAqEpTyBKVGKOUplqAYOY0lJ4ogAiJ3IssQWZaSZeJutYocmTDESwUsv4YwvxpKjbhHcf0tgEyPuJUfhFhUxKL4YB2GGYIgjBv+pSKlYpFSKXkUi0SlIkHURBA1EUaFpLVeig+1HsUHbDw+wMeHWrAwafQHVAZOZRCRC0oU3dhYCthQDBg4eChHbmX/7Ixygn4QsKTV66XAiVsr4+5FM1sDVCfL/9LmvYPafoGZTQAmABx44IHl1l1EOlE2DMiG3THktk83fOfeZbcYSO3ud7t7rbvX1tTUdHd1RERSpZygXwYc0Or14GRZu2XMLEN8iK4v870iItKFygn6WcAhZjbUzCqIT65ObVNmKnBp8nwM8JzHcytMBS42s0ozGwocAvy1c6ouIiLl6LCPPulzvwaYTnwi+353X2Bmk4DZ7j4VuA94KDnZuor4YEBS7jHiE7dF4GqNuBER2bU0qZmISApsa1Kz3eJkrIiIdB0FvYhIyinoRURSbrfrozezOuDdnfiI/sDKTqrOnmJv3GbYO7db27z32N7t/jt3b/dCpN0u6HeWmc3e2gmJtNobtxn2zu3WNu89OnO71XUjIpJyCnoRkZRLY9Df3d0V6AZ74zbD3rnd2ua9R6dtd+r66EVEZHNpbNGLiEgrCnoRkZRLTdCb2Sgze8PMFprZxO6uT1cwswPMbIaZvWpmC8zsumT5vmb2ezN7K/nZr7vr2hXMLDSzv5nZb5LXQ83sxWSfP5rMrpoaZtbXzKaY2etm9pqZnbw37Gsz+1ry7/sVM/uFmeXSuK/N7H4z+8DMXmm1rN39a7E7ku1/ycyO257vSkXQt7qv7dnAMOCS5H61aVME/oe7DwNOAq5OtnMi8Ky7HwI8m7xOo+uA11q9vgW43d0PBlYT37s4TX4I/Ke7Hw4cQ7ztqd7XZjYIuBaodfejiGfMbb4Pddr29f8DRrVZtrX9ezbxNO+HEN+N787t+aJUBD2t7mvr7k1A831tU8XdV7j73OT5OuL/+IOIt/WBpNgDwPndU8OuY2aDgU8D9yavDTiT+B7FkLLtNrM+wGnEU4Dj7k3u/iF7wb4mnj69R3IToypgBSnc1+4+k3ha99a2tn9HAw967C9AXzPbr9zvSkvQt3df2y3uTZsmZjYEOBZ4ERjg7iuSVf8NDOimanWlHwDfJL4XNMT3JP7Q3YvJ67Tt86FAHfDTpLvqXjPrScr3tbsvA24F3iMO+DXAHNK9r1vb2v7dqYxLS9DvVcysF/AE8FV3X9t6XXJnr1SNmTWzc4EP3H1Od9dlF8oAxwF3uvuxwAbadNOkdF/3I269DgX2B3qyZffGXqEz929agn6vuTetmWWJQ/7n7v5ksvj95j/jkp8fdFf9usgpwHlmtpi4W+5M4v7rvsmf95C+fb4UWOruLyavpxAHf9r39SeBd9y9zt0LwJPE+z/N+7q1re3fncq4tAR9Ofe13eMl/dL3Aa+5+22tVrW+Z++lwK92dd26krt/y90Hu/sQ4n37nLt/HphBfI9iSNl2u/t/A0vM7LBk0UjiW3Kmel8Td9mcZGZVyb/35u1O7b5uY2v7dyrwpWT0zUnAmlZdPB1z91Q8gHOAN4G3gX/p7vp00TZ+gvhPuZeAecnjHOL+6meBt4BngH27u65d+Ds4A/hN8vwg4pvNLwQeByq7u36dvK3DgdnJ/v4l0G9v2NfATcDrwCvAQ0BlGvc18Avi8xAF4r/grtja/gWMeGTh28DLxKOSyv4uTYEgIpJyaem6ERGRrVDQi4iknIJeRCTlFPQiIimnoBcRSTkFvYhIyinoRURS7v8D3eE6EkzPDaEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rKJHNFRZ31fH"
      },
      "source": [
        "Testing model metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PgKOEILU37I_",
        "outputId": "1c142902-706d-4730-b245-df68edbb9434"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 300)               50100     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 200)               60200     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 102)               20502     \n",
            "=================================================================\n",
            "Total params: 130,802\n",
            "Trainable params: 130,802\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "34KBbe6V4XNH"
      },
      "source": [
        "y_test_model = model.predict(X_test)"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L9voocoP4-SB"
      },
      "source": [
        "y_test_model_i = []\r\n",
        "Y_test_i = []\r\n",
        "for i in range(len(Y_test)):\r\n",
        "  y_test_model_i.append(np.argmax(y_test_model[i]))\r\n",
        "  Y_test_i.append(np.argmax(Y_test[i]))\r\n",
        "  "
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rwPY6cOq5oXg"
      },
      "source": [
        "y_test_model_i = np.array(y_test_model_i)\r\n",
        "Y_test_i = np.array(Y_test_i)"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GJu3KYwK52Zc",
        "outputId": "8543eb1a-92ad-487d-bf62-89e9f508f82d"
      },
      "source": [
        "print(\"Accuracy = \")\r\n",
        "sum((y_test_model_i == Y_test_i).astype(int)/len(Y_test_i))\r\n"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy = \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9643939393939189"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Py61pbu6S5h"
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BNWfEefD6fza",
        "outputId": "3445f828-724c-4adf-b43a-f7366cb26a5e"
      },
      "source": [
        "print(classification_report(Y_test_i,y_test_model_i))"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00         1\n",
            "           1       1.00      1.00      1.00         6\n",
            "           2       1.00      1.00      1.00        12\n",
            "           3       1.00      1.00      1.00         7\n",
            "           4       1.00      1.00      1.00        15\n",
            "           5       1.00      1.00      1.00        44\n",
            "           6       1.00      1.00      1.00         1\n",
            "           8       1.00      1.00      1.00         6\n",
            "           9       0.00      0.00      0.00         1\n",
            "          10       0.87      1.00      0.93        13\n",
            "          11       1.00      1.00      1.00         7\n",
            "          12       1.00      1.00      1.00         3\n",
            "          13       1.00      1.00      1.00         3\n",
            "          14       1.00      1.00      1.00        13\n",
            "          15       1.00      1.00      1.00         1\n",
            "          16       0.00      0.00      0.00         1\n",
            "          18       1.00      1.00      1.00         4\n",
            "          21       1.00      1.00      1.00         2\n",
            "          22       0.75      1.00      0.86         3\n",
            "          24       1.00      1.00      1.00         2\n",
            "          25       0.00      0.00      0.00         0\n",
            "          26       1.00      1.00      1.00         9\n",
            "          27       1.00      1.00      1.00        12\n",
            "          29       1.00      1.00      1.00         4\n",
            "          30       0.00      0.00      0.00         1\n",
            "          31       0.00      0.00      0.00         2\n",
            "          32       0.00      0.00      0.00         1\n",
            "          33       0.83      1.00      0.91         5\n",
            "          34       0.33      0.20      0.25         5\n",
            "          35       0.30      0.60      0.40         5\n",
            "          36       0.00      0.00      0.00         0\n",
            "          37       1.00      1.00      1.00         4\n",
            "          38       0.00      0.00      0.00         3\n",
            "          39       1.00      1.00      1.00        51\n",
            "          40       1.00      1.00      1.00        43\n",
            "          41       1.00      1.00      1.00         2\n",
            "          42       0.00      0.00      0.00         2\n",
            "          43       1.00      1.00      1.00         6\n",
            "          44       0.00      0.00      0.00         5\n",
            "          45       1.00      1.00      1.00         1\n",
            "          46       1.00      1.00      1.00        27\n",
            "          47       0.23      1.00      0.38         7\n",
            "          48       0.00      0.00      0.00         1\n",
            "          49       1.00      0.90      0.95        10\n",
            "          50       1.00      1.00      1.00        23\n",
            "          51       1.00      1.00      1.00         6\n",
            "          52       1.00      1.00      1.00         3\n",
            "          53       1.00      1.00      1.00        22\n",
            "          54       1.00      1.00      1.00       218\n",
            "          56       1.00      1.00      1.00        21\n",
            "          57       0.00      0.00      0.00         1\n",
            "          58       1.00      1.00      1.00         9\n",
            "          59       1.00      1.00      1.00        64\n",
            "          60       0.00      0.00      0.00         5\n",
            "          61       0.00      0.00      0.00         1\n",
            "          62       0.00      0.00      0.00         1\n",
            "          63       0.00      0.00      0.00         2\n",
            "          64       1.00      1.00      1.00         1\n",
            "          66       0.00      0.00      0.00         1\n",
            "          68       0.00      0.00      0.00         1\n",
            "          69       1.00      1.00      1.00         2\n",
            "          70       1.00      1.00      1.00         1\n",
            "          71       1.00      1.00      1.00        35\n",
            "          72       1.00      1.00      1.00         3\n",
            "          73       1.00      1.00      1.00         2\n",
            "          74       0.50      1.00      0.67         1\n",
            "          75       0.00      0.00      0.00         2\n",
            "          78       1.00      1.00      1.00         1\n",
            "          79       1.00      1.00      1.00         4\n",
            "          80       1.00      1.00      1.00         2\n",
            "          81       1.00      1.00      1.00        75\n",
            "          82       1.00      1.00      1.00         2\n",
            "          83       1.00      1.00      1.00         1\n",
            "          84       1.00      1.00      1.00         3\n",
            "          85       0.75      0.92      0.83        13\n",
            "          86       1.00      1.00      1.00         5\n",
            "          87       1.00      1.00      1.00         1\n",
            "          89       1.00      1.00      1.00       214\n",
            "          90       1.00      1.00      1.00       202\n",
            "          91       0.00      0.00      0.00         5\n",
            "          94       0.00      0.00      0.00         1\n",
            "          95       1.00      0.67      0.80         6\n",
            "         101       1.00      1.00      1.00         9\n",
            "\n",
            "    accuracy                           0.96      1320\n",
            "   macro avg       0.71      0.73      0.71      1320\n",
            "weighted avg       0.96      0.96      0.96      1320\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OisYQvQI6qaA"
      },
      "source": [
        "con_matrix = confusion_matrix(Y_test_i, y_test_model_i)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "G4qNYyIs7OCE",
        "outputId": "e4b8393d-3cb8-4adc-aa56-49ca0cfa3c0c"
      },
      "source": [
        "plt.figure(figsize=(10,10))\r\n",
        "plt.title(\"confusion Matrix\")\r\n",
        "plt.imshow(con_matrix, cmap= 'Paired', interpolation= 'nearest')\r\n",
        "plt.show()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j13Fo1iK7yCC"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b1H_JM6u73Hv"
      },
      "source": [
        "Now, using Binary classification to finally classify as Musk or Non-Musk"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUvGgMp97_we"
      },
      "source": [
        "Y1 = data.iloc[:, -1].values"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t-EaGwG88LN-",
        "outputId": "0cda74dc-1bf9-45fa-a679-33bdd7a25e85"
      },
      "source": [
        "Y1.shape"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6598,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gSF5Kbky8R1z"
      },
      "source": [
        "tranfer_model = model"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jCl8CTin8bVr",
        "outputId": "a5511578-6e8d-4106-cb1c-b2ed21a91fa0"
      },
      "source": [
        "tranfer_model.summary()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 300)               50100     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 200)               60200     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 102)               20502     \n",
            "=================================================================\n",
            "Total params: 130,802\n",
            "Trainable params: 130,802\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vt3YclG8eNZ"
      },
      "source": [
        "\r\n",
        "tranfer_model.add(Dense(50, activation='relu'))\r\n",
        "tranfer_model.add(Dense(1, activation='sigmoid'))\r\n",
        "tranfer_model.compile(optimizer='adam', loss= 'mse', metrics=['accuracy'])"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "41jCxmZ39nHr",
        "outputId": "c3c0bdf2-c3b4-49bf-9e73-04d27ceb32b0"
      },
      "source": [
        "tranfer_model.summary()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 300)               50100     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 200)               60200     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 102)               20502     \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 50)                5150      \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 1)                 51        \n",
            "=================================================================\n",
            "Total params: 136,003\n",
            "Trainable params: 136,003\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VkCZ-VKT9pv0"
      },
      "source": [
        "X_train1, X_test1, Y_train1, Y_test1 = train_test_split(X, Y1, train_size = 0.8, test_size = 0.2)"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 880
        },
        "id": "71lpoYQJ94SC",
        "outputId": "c0fc1e1c-1f84-4e32-be1d-4cc736564f22"
      },
      "source": [
        "tranfer_model.fit(X_train1, Y_train1, verbose=1, validation_data=(X_test1, Y_test1), epochs=20)\r\n"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.1596 - accuracy: 0.8615 - val_loss: 0.0308 - val_accuracy: 0.9583\n",
            "Epoch 2/20\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0242 - accuracy: 0.9646 - val_loss: 0.0168 - val_accuracy: 0.9773\n",
            "Epoch 3/20\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0124 - accuracy: 0.9840 - val_loss: 0.0072 - val_accuracy: 0.9955\n",
            "Epoch 4/20\n",
            "165/165 [==============================] - 1s 3ms/step - loss: 0.0058 - accuracy: 0.9947 - val_loss: 0.0044 - val_accuracy: 0.9970\n",
            "Epoch 5/20\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0021 - accuracy: 0.9988 - val_loss: 0.0032 - val_accuracy: 0.9962\n",
            "Epoch 6/20\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0016 - accuracy: 0.9987 - val_loss: 0.0026 - val_accuracy: 0.9970\n",
            "Epoch 7/20\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0017 - accuracy: 0.9983 - val_loss: 0.0028 - val_accuracy: 0.9970\n",
            "Epoch 8/20\n",
            "165/165 [==============================] - 1s 3ms/step - loss: 9.6775e-04 - accuracy: 0.9991 - val_loss: 0.0027 - val_accuracy: 0.9970\n",
            "Epoch 9/20\n",
            "165/165 [==============================] - 1s 3ms/step - loss: 8.6372e-04 - accuracy: 0.9992 - val_loss: 0.0037 - val_accuracy: 0.9955\n",
            "Epoch 10/20\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0059 - accuracy: 0.9937 - val_loss: 0.0026 - val_accuracy: 0.9977\n",
            "Epoch 11/20\n",
            "165/165 [==============================] - 1s 3ms/step - loss: 0.0034 - accuracy: 0.9957 - val_loss: 0.0238 - val_accuracy: 0.9697\n",
            "Epoch 12/20\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0104 - accuracy: 0.9874 - val_loss: 0.0051 - val_accuracy: 0.9932\n",
            "Epoch 13/20\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 0.0026 - accuracy: 0.9972 - val_loss: 0.0085 - val_accuracy: 0.9894\n",
            "Epoch 14/20\n",
            "165/165 [==============================] - 1s 3ms/step - loss: 0.0014 - accuracy: 0.9987 - val_loss: 0.0028 - val_accuracy: 0.9970\n",
            "Epoch 15/20\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 8.8467e-04 - accuracy: 0.9991 - val_loss: 0.0011 - val_accuracy: 0.9992\n",
            "Epoch 16/20\n",
            "165/165 [==============================] - 1s 3ms/step - loss: 5.6638e-04 - accuracy: 0.9995 - val_loss: 0.0016 - val_accuracy: 0.9970\n",
            "Epoch 17/20\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 2.4086e-04 - accuracy: 0.9998 - val_loss: 0.0019 - val_accuracy: 0.9970\n",
            "Epoch 18/20\n",
            "165/165 [==============================] - 1s 3ms/step - loss: 5.6003e-04 - accuracy: 0.9995 - val_loss: 0.0014 - val_accuracy: 0.9985\n",
            "Epoch 19/20\n",
            "165/165 [==============================] - 1s 4ms/step - loss: 6.9274e-04 - accuracy: 0.9993 - val_loss: 0.0015 - val_accuracy: 0.9970\n",
            "Epoch 20/20\n",
            "165/165 [==============================] - 1s 3ms/step - loss: 4.5621e-04 - accuracy: 0.9996 - val_loss: 0.0015 - val_accuracy: 0.9970\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-62-0328e9244a9e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtranfer_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_test1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtransfer_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"model_musk_added_transfer.h5\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'transfer_model' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "idoqmYNc-tRX"
      },
      "source": [
        "tranfer_model.save(\"model_muskAfterTransfer.h5\")"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RyxDuOp0-_UO"
      },
      "source": [
        "new_accuracy = tranfer_model.history.history['accuracy']\r\n",
        "new_validation = tranfer_model.history.history['val_accuracy']\r\n",
        "\r\n",
        "new_train_loss = tranfer_model.history.history['loss']\r\n",
        "new_val_loss = tranfer_model.history.history['val_loss']"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "RfhMvAfY_xYI",
        "outputId": "ccd9df4d-dd16-4c63-8cc6-6f283cebda08"
      },
      "source": [
        "plt.plot(new_accuracy, label = \"training_accuracy\")\r\n",
        "plt.plot(new_validation, label = \"validation_accuracy\")\r\n",
        "plt.legend()\r\n",
        "plt.show()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "E5Cy-DtyAFcK",
        "outputId": "8ae44089-6cda-433d-d5a4-5b5cb18a6ac1"
      },
      "source": [
        "plt.plot(new_train_loss, label = \"training_loss\")\r\n",
        "plt.plot(new_val_loss, label = \"validation_loss\")\r\n",
        "plt.legend()\r\n",
        "plt.show()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEILnMcNAcRY"
      },
      "source": [
        "new_y = tranfer_model.predict(X_test1)"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Z8ZX4A8A6I-"
      },
      "source": [
        "new_y = (new_y[:]>(0.5)).astype(int).flatten()"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HV6h-CP_Bgo7",
        "outputId": "57d06615-db76-4193-b333-736fc38d4ec7"
      },
      "source": [
        "print(classification_report(Y_test1, new_y))"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      1109\n",
            "           1       1.00      0.98      0.99       211\n",
            "\n",
            "    accuracy                           1.00      1320\n",
            "   macro avg       1.00      0.99      0.99      1320\n",
            "weighted avg       1.00      1.00      1.00      1320\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oBdbSwM_CTD9",
        "outputId": "0bf39c3f-5e8e-4a65-d86e-eaa86ad1b510"
      },
      "source": [
        "print(\"Accuracy = \")\r\n",
        "sum(Y_test1 == new_y).astype(int)/len(new_y)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy = \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.996969696969697"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HwbuTfLHCwhj",
        "outputId": "c8ac12d6-d1d7-4c49-9f89-07f1e33576d1"
      },
      "source": [
        "confusion_matrix(Y_test1, new_y)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1109,    0],\n",
              "       [   4,  207]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-UlAztiC5fa"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}