Hill_valley.ipynb

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      "source": [
        "import pandas as pd"
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      "execution_count": 14,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Hill Valley Dataset.csv')"
      ],
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      "execution_count": 15,
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              "         V1        V2        V3        V4        V5        V6        V7  \\\n",
              "0     39.02     36.49     38.20     38.85     39.38     39.74     37.02   \n",
              "1      1.83      1.71      1.77      1.77      1.68      1.78      1.80   \n",
              "2  68177.69  66138.42  72981.88  74304.33  67549.66  69367.34  69169.41   \n",
              "3  44889.06  39191.86  40728.46  38576.36  45876.06  47034.00  46611.43   \n",
              "4      5.70      5.40      5.28      5.38      5.27      5.61      6.00   \n",
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              "         V8        V9       V10  ...       V92       V93       V94       V95  \\\n",
              "0     39.53     38.81     38.79  ...     36.62     36.92     38.80     38.52   \n",
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              "4      5.38      5.34      5.87  ...      5.17      5.67      5.60      5.94   \n",
              "\n",
              "        V96       V97       V98       V99      V100  Class  \n",
              "0     38.07     36.73     39.46     37.50     39.10      0  \n",
              "1      1.74      1.80      1.78      1.75      1.69      1  \n",
              "2  72571.58  66348.97  71063.72  67404.27  74920.24      1  \n",
              "3  45410.53  47139.44  43095.68  40888.34  39615.19      0  \n",
              "4      5.73      5.22      5.30      5.73      5.91      0  \n",
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              "      <th>V1</th>\n",
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              "      <td>39.02</td>\n",
              "      <td>36.49</td>\n",
              "      <td>38.20</td>\n",
              "      <td>38.85</td>\n",
              "      <td>39.38</td>\n",
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              "      <td>39.53</td>\n",
              "      <td>38.81</td>\n",
              "      <td>38.79</td>\n",
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              "      <td>36.62</td>\n",
              "      <td>36.92</td>\n",
              "      <td>38.80</td>\n",
              "      <td>38.52</td>\n",
              "      <td>38.07</td>\n",
              "      <td>36.73</td>\n",
              "      <td>39.46</td>\n",
              "      <td>37.50</td>\n",
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              "      <td>1.71</td>\n",
              "      <td>1.77</td>\n",
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              "      <td>1.68</td>\n",
              "      <td>1.78</td>\n",
              "      <td>1.80</td>\n",
              "      <td>1.70</td>\n",
              "      <td>1.75</td>\n",
              "      <td>1.78</td>\n",
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              "      <td>1.80</td>\n",
              "      <td>1.79</td>\n",
              "      <td>1.77</td>\n",
              "      <td>1.74</td>\n",
              "      <td>1.74</td>\n",
              "      <td>1.80</td>\n",
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              "      <th>2</th>\n",
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              "      <td>74304.33</td>\n",
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              "      <td>69367.34</td>\n",
              "      <td>69169.41</td>\n",
              "      <td>73268.61</td>\n",
              "      <td>74465.84</td>\n",
              "      <td>72503.37</td>\n",
              "      <td>...</td>\n",
              "      <td>73438.88</td>\n",
              "      <td>71053.35</td>\n",
              "      <td>71112.62</td>\n",
              "      <td>74916.48</td>\n",
              "      <td>72571.58</td>\n",
              "      <td>66348.97</td>\n",
              "      <td>71063.72</td>\n",
              "      <td>67404.27</td>\n",
              "      <td>74920.24</td>\n",
              "      <td>1</td>\n",
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              "      <th>3</th>\n",
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              "      <td>39191.86</td>\n",
              "      <td>40728.46</td>\n",
              "      <td>38576.36</td>\n",
              "      <td>45876.06</td>\n",
              "      <td>47034.00</td>\n",
              "      <td>46611.43</td>\n",
              "      <td>37668.32</td>\n",
              "      <td>40980.89</td>\n",
              "      <td>38466.15</td>\n",
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              "      <td>42625.67</td>\n",
              "      <td>40684.20</td>\n",
              "      <td>46960.73</td>\n",
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              "      <td>0</td>\n",
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              "      <th>4</th>\n",
              "      <td>5.70</td>\n",
              "      <td>5.40</td>\n",
              "      <td>5.28</td>\n",
              "      <td>5.38</td>\n",
              "      <td>5.27</td>\n",
              "      <td>5.61</td>\n",
              "      <td>6.00</td>\n",
              "      <td>5.38</td>\n",
              "      <td>5.34</td>\n",
              "      <td>5.87</td>\n",
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              "      <td>5.17</td>\n",
              "      <td>5.67</td>\n",
              "      <td>5.60</td>\n",
              "      <td>5.94</td>\n",
              "      <td>5.73</td>\n",
              "      <td>5.22</td>\n",
              "      <td>5.30</td>\n",
              "      <td>5.73</td>\n",
              "      <td>5.91</td>\n",
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      "execution_count": 17,
      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1212 entries, 0 to 1211\n",
            "Columns: 101 entries, V1 to Class\n",
            "dtypes: float64(100), int64(1)\n",
            "memory usage: 956.5 KB\n"
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        "id": "B47mvdqHoEOF",
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      "execution_count": 18,
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              "                  V1             V2             V3             V4  \\\n",
              "count    1212.000000    1212.000000    1212.000000    1212.000000   \n",
              "mean     8169.091881    8144.306262    8192.653738    8176.868738   \n",
              "std     17974.950461   17881.049734   18087.938901   17991.903982   \n",
              "min         0.920000       0.900000       0.850000       0.890000   \n",
              "25%        19.602500      19.595000      18.925000      19.277500   \n",
              "50%       301.425000     295.205000     297.260000     299.720000   \n",
              "75%      5358.795000    5417.847500    5393.367500    5388.482500   \n",
              "max    117807.870000  108896.480000  119031.350000  110212.590000   \n",
              "\n",
              "                  V5             V6             V7             V8  \\\n",
              "count    1212.000000    1212.000000    1212.000000    1212.000000   \n",
              "mean     8128.297211    8173.030008    8188.582748    8183.641543   \n",
              "std     17846.757963   17927.114105   18029.562695   18048.582159   \n",
              "min         0.880000       0.860000       0.870000       0.650000   \n",
              "25%        19.210000      19.582500      18.690000      19.062500   \n",
              "50%       295.115000     294.380000     295.935000     290.850000   \n",
              "75%      5321.987500    5328.040000    5443.977500    5283.655000   \n",
              "max    113000.470000  116848.390000  115609.240000  118522.320000   \n",
              "\n",
              "                  V9            V10  ...            V92            V93  \\\n",
              "count    1212.000000    1212.000000  ...    1212.000000    1212.000000   \n",
              "mean     8154.670066    8120.767574  ...    8120.056815    8125.917409   \n",
              "std     17982.390713   17900.798206  ...   17773.190621   17758.182403   \n",
              "min         0.650000       0.620000  ...       0.870000       0.900000   \n",
              "25%        19.532500      19.285000  ...      19.197500      18.895000   \n",
              "50%       294.565000     295.160000  ...     297.845000     295.420000   \n",
              "75%      5378.180000    5319.097500  ...    5355.355000    5386.037500   \n",
              "max    112895.900000  117798.300000  ...  113858.680000  112948.830000   \n",
              "\n",
              "                 V94            V95            V96            V97  \\\n",
              "count    1212.000000    1212.000000    1212.000000    1212.000000   \n",
              "mean     8158.793812    8140.885421    8213.480611    8185.594002   \n",
              "std     17919.510371   17817.945646   18016.445265   17956.084223   \n",
              "min         0.870000       0.880000       0.890000       0.890000   \n",
              "25%        19.237500      19.385000      19.027500      19.135000   \n",
              "50%       299.155000     293.355000     301.370000     296.960000   \n",
              "75%      5286.385000    5345.797500    5300.890000    5361.047500   \n",
              "max    112409.570000  112933.730000  112037.220000  115110.420000   \n",
              "\n",
              "                 V98            V99           V100        Class  \n",
              "count    1212.000000    1212.000000    1212.000000  1212.000000  \n",
              "mean     8140.195355    8192.960891    8156.197376     0.500000  \n",
              "std     17768.356106   18064.781479   17829.310973     0.500206  \n",
              "min         0.860000       0.910000       0.890000     0.000000  \n",
              "25%        19.205000      18.812500      19.145000     0.000000  \n",
              "50%       300.925000     299.200000     302.275000     0.500000  \n",
              "75%      5390.850000    5288.712500    5357.847500     1.000000  \n",
              "max    116431.960000  113291.960000  114533.760000     1.000000  \n",
              "\n",
              "[8 rows x 101 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-29ec2135-23b3-4fd9-9ee8-0b92bc5f42a2\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>...</th>\n",
              "      <th>V92</th>\n",
              "      <th>V93</th>\n",
              "      <th>V94</th>\n",
              "      <th>V95</th>\n",
              "      <th>V96</th>\n",
              "      <th>V97</th>\n",
              "      <th>V98</th>\n",
              "      <th>V99</th>\n",
              "      <th>V100</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1212.000000</td>\n",
              "      <td>1212.000000</td>\n",
              "      <td>1212.000000</td>\n",
              "      <td>1212.000000</td>\n",
              "      <td>1212.000000</td>\n",
              "      <td>1212.000000</td>\n",
              "      <td>1212.000000</td>\n",
              "      <td>1212.000000</td>\n",
              "      <td>1212.000000</td>\n",
              "      <td>1212.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1212.000000</td>\n",
              "      <td>1212.000000</td>\n",
              "      <td>1212.000000</td>\n",
              "      <td>1212.000000</td>\n",
              "      <td>1212.000000</td>\n",
              "      <td>1212.000000</td>\n",
              "      <td>1212.000000</td>\n",
              "      <td>1212.000000</td>\n",
              "      <td>1212.000000</td>\n",
              "      <td>1212.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8169.091881</td>\n",
              "      <td>8144.306262</td>\n",
              "      <td>8192.653738</td>\n",
              "      <td>8176.868738</td>\n",
              "      <td>8128.297211</td>\n",
              "      <td>8173.030008</td>\n",
              "      <td>8188.582748</td>\n",
              "      <td>8183.641543</td>\n",
              "      <td>8154.670066</td>\n",
              "      <td>8120.767574</td>\n",
              "      <td>...</td>\n",
              "      <td>8120.056815</td>\n",
              "      <td>8125.917409</td>\n",
              "      <td>8158.793812</td>\n",
              "      <td>8140.885421</td>\n",
              "      <td>8213.480611</td>\n",
              "      <td>8185.594002</td>\n",
              "      <td>8140.195355</td>\n",
              "      <td>8192.960891</td>\n",
              "      <td>8156.197376</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>17974.950461</td>\n",
              "      <td>17881.049734</td>\n",
              "      <td>18087.938901</td>\n",
              "      <td>17991.903982</td>\n",
              "      <td>17846.757963</td>\n",
              "      <td>17927.114105</td>\n",
              "      <td>18029.562695</td>\n",
              "      <td>18048.582159</td>\n",
              "      <td>17982.390713</td>\n",
              "      <td>17900.798206</td>\n",
              "      <td>...</td>\n",
              "      <td>17773.190621</td>\n",
              "      <td>17758.182403</td>\n",
              "      <td>17919.510371</td>\n",
              "      <td>17817.945646</td>\n",
              "      <td>18016.445265</td>\n",
              "      <td>17956.084223</td>\n",
              "      <td>17768.356106</td>\n",
              "      <td>18064.781479</td>\n",
              "      <td>17829.310973</td>\n",
              "      <td>0.500206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.920000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.850000</td>\n",
              "      <td>0.890000</td>\n",
              "      <td>0.880000</td>\n",
              "      <td>0.860000</td>\n",
              "      <td>0.870000</td>\n",
              "      <td>0.650000</td>\n",
              "      <td>0.650000</td>\n",
              "      <td>0.620000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.870000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.870000</td>\n",
              "      <td>0.880000</td>\n",
              "      <td>0.890000</td>\n",
              "      <td>0.890000</td>\n",
              "      <td>0.860000</td>\n",
              "      <td>0.910000</td>\n",
              "      <td>0.890000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>19.602500</td>\n",
              "      <td>19.595000</td>\n",
              "      <td>18.925000</td>\n",
              "      <td>19.277500</td>\n",
              "      <td>19.210000</td>\n",
              "      <td>19.582500</td>\n",
              "      <td>18.690000</td>\n",
              "      <td>19.062500</td>\n",
              "      <td>19.532500</td>\n",
              "      <td>19.285000</td>\n",
              "      <td>...</td>\n",
              "      <td>19.197500</td>\n",
              "      <td>18.895000</td>\n",
              "      <td>19.237500</td>\n",
              "      <td>19.385000</td>\n",
              "      <td>19.027500</td>\n",
              "      <td>19.135000</td>\n",
              "      <td>19.205000</td>\n",
              "      <td>18.812500</td>\n",
              "      <td>19.145000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>301.425000</td>\n",
              "      <td>295.205000</td>\n",
              "      <td>297.260000</td>\n",
              "      <td>299.720000</td>\n",
              "      <td>295.115000</td>\n",
              "      <td>294.380000</td>\n",
              "      <td>295.935000</td>\n",
              "      <td>290.850000</td>\n",
              "      <td>294.565000</td>\n",
              "      <td>295.160000</td>\n",
              "      <td>...</td>\n",
              "      <td>297.845000</td>\n",
              "      <td>295.420000</td>\n",
              "      <td>299.155000</td>\n",
              "      <td>293.355000</td>\n",
              "      <td>301.370000</td>\n",
              "      <td>296.960000</td>\n",
              "      <td>300.925000</td>\n",
              "      <td>299.200000</td>\n",
              "      <td>302.275000</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5358.795000</td>\n",
              "      <td>5417.847500</td>\n",
              "      <td>5393.367500</td>\n",
              "      <td>5388.482500</td>\n",
              "      <td>5321.987500</td>\n",
              "      <td>5328.040000</td>\n",
              "      <td>5443.977500</td>\n",
              "      <td>5283.655000</td>\n",
              "      <td>5378.180000</td>\n",
              "      <td>5319.097500</td>\n",
              "      <td>...</td>\n",
              "      <td>5355.355000</td>\n",
              "      <td>5386.037500</td>\n",
              "      <td>5286.385000</td>\n",
              "      <td>5345.797500</td>\n",
              "      <td>5300.890000</td>\n",
              "      <td>5361.047500</td>\n",
              "      <td>5390.850000</td>\n",
              "      <td>5288.712500</td>\n",
              "      <td>5357.847500</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>117807.870000</td>\n",
              "      <td>108896.480000</td>\n",
              "      <td>119031.350000</td>\n",
              "      <td>110212.590000</td>\n",
              "      <td>113000.470000</td>\n",
              "      <td>116848.390000</td>\n",
              "      <td>115609.240000</td>\n",
              "      <td>118522.320000</td>\n",
              "      <td>112895.900000</td>\n",
              "      <td>117798.300000</td>\n",
              "      <td>...</td>\n",
              "      <td>113858.680000</td>\n",
              "      <td>112948.830000</td>\n",
              "      <td>112409.570000</td>\n",
              "      <td>112933.730000</td>\n",
              "      <td>112037.220000</td>\n",
              "      <td>115110.420000</td>\n",
              "      <td>116431.960000</td>\n",
              "      <td>113291.960000</td>\n",
              "      <td>114533.760000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 101 columns</p>\n",
              "</div>\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e2314663-b5e6-4425-89ba-1ccc597732f2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x-XfO1lxoKc0",
        "outputId": "819593f6-b103-4e5d-80c3-fef0b86df85a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
              "       ...\n",
              "       'V92', 'V93', 'V94', 'V95', 'V96', 'V97', 'V98', 'V99', 'V100',\n",
              "       'Class'],\n",
              "      dtype='object', length=101)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['Class']"
      ],
      "metadata": {
        "id": "6Tf23L61oMxw"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i5gx8Sx_oOqp",
        "outputId": "81eeb62d-daa9-421b-ed1b-b6575373cf00"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1212,)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=df.drop('Class',axis=1)"
      ],
      "metadata": {
        "id": "dsUnyVzMorFX"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L5ffhZv8ozYW",
        "outputId": "6d11db7d-05f2-4c44-dfb2-83863cb5959b"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1212, 100)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "AXLhlOxJo3sF"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test = train_test_split(x,y,random_state=2529)"
      ],
      "metadata": {
        "id": "MnpwdypFo6ZC"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "q1jkCV_SpBqX"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "logit = LogisticRegression(max_iter=500)"
      ],
      "metadata": {
        "id": "L465atxgpD5W"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "logit.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "Ff_cnINGpGG_",
        "outputId": "fe606f15-bd25-4e80-b877-62d6fc01beed"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=500)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=500)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=500)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logit.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "67gxBLZzpJaM",
        "outputId": "18fd68a4-9c5f-4669-8da1-527f0f0fdd12"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.08272553])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logit.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uWKjKHPBpL2s",
        "outputId": "52f1aef0-8d41-49c3-9934-cea49fd0be85"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.51483646, -1.20187319, -1.43976875, -1.14264459, -1.13035086,\n",
              "        -0.87336932, -0.47843433, -0.4568868 , -0.08141883,  0.75069886,\n",
              "         0.86850745,  0.39143538,  0.31390037,  0.49953723,  1.07471323,\n",
              "         0.19009805,  0.09981579,  0.35382468,  0.1168908 ,  0.15666276,\n",
              "        -0.10308645, -0.07121218,  0.11684062,  0.19372702,  0.16906668,\n",
              "         0.23874664,  0.09995129,  0.14492076,  0.10444511,  0.13804417,\n",
              "         0.09172775,  0.38728544,  0.33193755, -0.11475304,  0.30383677,\n",
              "         0.32282466,  0.27775299,  0.1000914 ,  0.04589301,  0.04802637,\n",
              "         0.17853668, -0.03240562,  0.08549588,  0.26001891,  0.36655645,\n",
              "         0.17347642, -0.16256095,  0.0808282 , -0.09713156,  0.45323114,\n",
              "         0.2347364 ,  0.05427608,  0.00515393,  0.24435279,  0.49667248,\n",
              "         0.13578941,  0.12414868,  0.05072632, -0.2840424 ,  0.30055144,\n",
              "         0.12898603,  0.31579501,  0.06474958,  0.2184907 , -0.19833385,\n",
              "         0.2084842 ,  0.27822406, -0.02662214, -0.19605618,  0.55141759,\n",
              "        -0.03949637,  0.19990352,  0.41784608, -0.14391376, -0.01079118,\n",
              "         0.17378295, -0.07260857,  0.27253714, -0.10887662, -0.02985389,\n",
              "         0.21646699,  0.42770079,  0.61824748,  0.12357037,  0.44384913,\n",
              "         0.86427398,  0.47345486,  0.48983971,  0.52451789,  0.46421582,\n",
              "        -0.19118247, -0.05605844, -0.53474302, -0.93750074, -0.82976027,\n",
              "        -0.91947643, -1.41295634, -1.12439751, -1.29597966, -1.33391394]])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = logit.predict(x_test)"
      ],
      "metadata": {
        "id": "u-809UG6pOeJ"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l3BDXgYppRqE",
        "outputId": "d2fe044f-8b69-42b0-9c9f-bee4c38ff39e"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0,\n",
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              "       0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0,\n",
              "       0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1,\n",
              "       1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logit.predict_proba(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r3iso7WzpU_Y",
        "outputId": "02a4c702-f4e8-4222-f2dc-65daeafa64ea"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       [0.00000000e+000, 1.00000000e+000],\n",
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              "       [9.99889518e-001, 1.10481573e-004],\n",
              "       [8.20507711e-001, 1.79492289e-001],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [1.00000000e+000, 8.18274256e-088],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [1.00000000e+000, 2.20895544e-029],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 2.09020415e-034],\n",
              "       [1.60841736e-006, 9.99998392e-001],\n",
              "       [1.00000000e+000, 2.40641167e-015],\n",
              "       [1.00000000e+000, 1.87236988e-035],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [1.00000000e+000, 4.46139277e-020],\n",
              "       [1.00000000e+000, 1.92133165e-178],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [1.00000000e+000, 4.99419572e-098],\n",
              "       [1.00000000e+000, 4.01237201e-254],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.94477040e-006, 9.99998055e-001],\n",
              "       [9.99917752e-001, 8.22475223e-005],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
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              "       [1.50757311e-004, 9.99849243e-001],\n",
              "       [7.27580136e-001, 2.72419864e-001],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [2.14577800e-009, 9.99999998e-001],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [9.54199009e-001, 4.58009911e-002],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [3.98082389e-001, 6.01917611e-001],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 8.99728867e-083],\n",
              "       [4.85519721e-001, 5.14480279e-001],\n",
              "       [7.83974507e-001, 2.16025493e-001],\n",
              "       [1.99840144e-015, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [9.41696404e-001, 5.83035962e-002],\n",
              "       [9.99467895e-001, 5.32105276e-004],\n",
              "       [7.06284953e-001, 2.93715047e-001],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [5.95473968e-004, 9.99404526e-001],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 3.94606120e-022],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [4.55875170e-001, 5.44124830e-001],\n",
              "       [7.88140580e-001, 2.11859420e-001],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 3.21335697e-014],\n",
              "       [6.46692655e-002, 9.35330735e-001],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [9.99951323e-001, 4.86765258e-005],\n",
              "       [9.99885538e-001, 1.14461531e-004],\n",
              "       [9.99569648e-001, 4.30352385e-004],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [8.98026747e-001, 1.01973253e-001],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [7.00042586e-003, 9.92999574e-001],\n",
              "       [1.00000000e+000, 2.54392468e-015],\n",
              "       [1.62386493e-001, 8.37613507e-001],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 3.02560853e-288],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 2.02027399e-080],\n",
              "       [3.96063989e-001, 6.03936011e-001],\n",
              "       [7.91967764e-001, 2.08032236e-001],\n",
              "       [4.38425753e-002, 9.56157425e-001],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 9.63509901e-027],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [9.37516981e-001, 6.24830187e-002],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [1.00000000e+000, 2.03029361e-024],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 1.69573037e-010],\n",
              "       [9.22367925e-001, 7.76320755e-002],\n",
              "       [2.66453526e-015, 1.00000000e+000],\n",
              "       [1.00000000e+000, 7.99545323e-159],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [5.17785925e-005, 9.99948221e-001],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [9.99999951e-001, 4.92678818e-008],\n",
              "       [3.00730552e-010, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [9.18821037e-005, 9.99908118e-001],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [5.13123849e-001, 4.86876151e-001],\n",
              "       [9.99999896e-001, 1.04470825e-007],\n",
              "       [9.99674972e-001, 3.25028492e-004],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [7.22826753e-005, 9.99927717e-001],\n",
              "       [9.94452974e-001, 5.54702615e-003],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [8.52154223e-001, 1.47845777e-001],\n",
              "       [9.99165733e-001, 8.34266896e-004],\n",
              "       [6.00258605e-001, 3.99741395e-001],\n",
              "       [4.69109693e-001, 5.30890307e-001],\n",
              "       [6.09428233e-003, 9.93905718e-001],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [1.00000000e+000, 2.04559820e-089],\n",
              "       [9.92510370e-001, 7.48963046e-003],\n",
              "       [1.00000000e+000, 2.40027485e-038],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 4.92270544e-113],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 2.63680470e-028],\n",
              "       [8.67353849e-001, 1.32646151e-001],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 3.71855035e-223],\n",
              "       [3.05430787e-001, 6.94569213e-001],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [1.00000000e+000, 3.29305318e-054],\n",
              "       [3.97355114e-001, 6.02644886e-001],\n",
              "       [5.26655169e-003, 9.94733448e-001],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [3.56835201e-001, 6.43164799e-001],\n",
              "       [1.99838474e-001, 8.00161526e-001],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.96234886e-001, 8.03765114e-001],\n",
              "       [3.03912451e-012, 1.00000000e+000],\n",
              "       [1.00000000e+000, 1.76248260e-202],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [7.10478664e-001, 2.89521336e-001],\n",
              "       [9.99941561e-001, 5.84390523e-005],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [6.87745429e-001, 3.12254571e-001],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [8.73788210e-003, 9.91262118e-001],\n",
              "       [1.81818573e-001, 8.18181427e-001],\n",
              "       [1.00000000e+000, 4.71885918e-017],\n",
              "       [1.66042659e-003, 9.98339573e-001],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [9.89266751e-001, 1.07332487e-002],\n",
              "       [3.51055326e-002, 9.64894467e-001],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [9.99999299e-001, 7.01488199e-007],\n",
              "       [1.00000000e+000, 2.25110775e-051],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 2.65138317e-014],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [2.46765077e-001, 7.53234923e-001],\n",
              "       [1.00000000e+000, 1.21303041e-192],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.46687107e-011, 1.00000000e+000],\n",
              "       [9.73962162e-001, 2.60378384e-002],\n",
              "       [8.45692484e-002, 9.15430752e-001],\n",
              "       [1.00000000e+000, 8.57351557e-032],\n",
              "       [1.00000000e+000, 2.18286677e-045],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [9.99997973e-001, 2.02700683e-006],\n",
              "       [1.00000000e+000, 2.90954054e-169],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [9.93166123e-001, 6.83387736e-003],\n",
              "       [1.00000000e+000, 5.39710579e-062],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.54769531e-011, 1.00000000e+000],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [9.99999969e-001, 3.05085271e-008],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [6.18420817e-001, 3.81579183e-001],\n",
              "       [2.60047501e-001, 7.39952499e-001],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [9.99999962e-001, 3.76795038e-008],\n",
              "       [9.49008819e-001, 5.09911815e-002],\n",
              "       [1.00000000e+000, 1.78664319e-015],\n",
              "       [4.83545835e-002, 9.51645417e-001],\n",
              "       [1.00000000e+000, 1.65387585e-083],\n",
              "       [1.00000000e+000, 4.58268588e-163],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.73379390e-001, 8.26620610e-001],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [2.79894239e-001, 7.20105761e-001],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [6.13149008e-001, 3.86850992e-001],\n",
              "       [9.99988463e-001, 1.15372174e-005],\n",
              "       [1.00000000e+000, 1.77844288e-055],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 4.53846824e-042],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [1.00000000e+000, 0.00000000e+000],\n",
              "       [1.00000000e+000, 6.69702356e-045],\n",
              "       [8.38295870e-001, 1.61704130e-001],\n",
              "       [0.00000000e+000, 1.00000000e+000],\n",
              "       [9.99995238e-001, 4.76163909e-006]])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report"
      ],
      "metadata": {
        "id": "CQXJj6NWpZ8V"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9GxVaV4apdkj",
        "outputId": "27127ab0-ff11-4b9e-ae7d-28a7f9d80e33"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[145,   4],\n",
              "       [  7, 147]])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YUGhmt1tphHY",
        "outputId": "0e7d57f5-b116-46e5-d0f5-6be8cceba7ca"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.97      0.96       149\n",
            "           1       0.97      0.95      0.96       154\n",
            "\n",
            "    accuracy                           0.96       303\n",
            "   macro avg       0.96      0.96      0.96       303\n",
            "weighted avg       0.96      0.96      0.96       303\n",
            "\n"
          ]
        }
      ]
    }
  ]
}