HappyMonk.ipynb

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      "cell_type": "code",
      "source": [
        "# Importing the necessary libraries\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, f1_score\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "DqEpTDXhuyxD"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing the wisconsin breast cancer dataset\n",
        "\n",
        "data=pd.read_csv(\"/content/drive/MyDrive/data.csv\")\n",
        "display(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "MBThoSj_uyt3",
        "outputId": "55d7a191-4fe3-4bcc-cd58-905d4019a59a"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0      842302         M        17.99         10.38          122.80     1001.0   \n",
              "1      842517         M        20.57         17.77          132.90     1326.0   \n",
              "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
              "3    84348301         M        11.42         20.38           77.58      386.1   \n",
              "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
              "..        ...       ...          ...           ...             ...        ...   \n",
              "564    926424         M        21.56         22.39          142.00     1479.0   \n",
              "565    926682         M        20.13         28.25          131.20     1261.0   \n",
              "566    926954         M        16.60         28.08          108.30      858.1   \n",
              "567    927241         M        20.60         29.33          140.10     1265.0   \n",
              "568     92751         B         7.76         24.54           47.92      181.0   \n",
              "\n",
              "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0            0.11840           0.27760         0.30010              0.14710   \n",
              "1            0.08474           0.07864         0.08690              0.07017   \n",
              "2            0.10960           0.15990         0.19740              0.12790   \n",
              "3            0.14250           0.28390         0.24140              0.10520   \n",
              "4            0.10030           0.13280         0.19800              0.10430   \n",
              "..               ...               ...             ...                  ...   \n",
              "564          0.11100           0.11590         0.24390              0.13890   \n",
              "565          0.09780           0.10340         0.14400              0.09791   \n",
              "566          0.08455           0.10230         0.09251              0.05302   \n",
              "567          0.11780           0.27700         0.35140              0.15200   \n",
              "568          0.05263           0.04362         0.00000              0.00000   \n",
              "\n",
              "     ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
              "0    ...          17.33           184.60      2019.0           0.16220   \n",
              "1    ...          23.41           158.80      1956.0           0.12380   \n",
              "2    ...          25.53           152.50      1709.0           0.14440   \n",
              "3    ...          26.50            98.87       567.7           0.20980   \n",
              "4    ...          16.67           152.20      1575.0           0.13740   \n",
              "..   ...            ...              ...         ...               ...   \n",
              "564  ...          26.40           166.10      2027.0           0.14100   \n",
              "565  ...          38.25           155.00      1731.0           0.11660   \n",
              "566  ...          34.12           126.70      1124.0           0.11390   \n",
              "567  ...          39.42           184.60      1821.0           0.16500   \n",
              "568  ...          30.37            59.16       268.6           0.08996   \n",
              "\n",
              "     compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
              "0              0.66560           0.7119                0.2654          0.4601   \n",
              "1              0.18660           0.2416                0.1860          0.2750   \n",
              "2              0.42450           0.4504                0.2430          0.3613   \n",
              "3              0.86630           0.6869                0.2575          0.6638   \n",
              "4              0.20500           0.4000                0.1625          0.2364   \n",
              "..                 ...              ...                   ...             ...   \n",
              "564            0.21130           0.4107                0.2216          0.2060   \n",
              "565            0.19220           0.3215                0.1628          0.2572   \n",
              "566            0.30940           0.3403                0.1418          0.2218   \n",
              "567            0.86810           0.9387                0.2650          0.4087   \n",
              "568            0.06444           0.0000                0.0000          0.2871   \n",
              "\n",
              "     fractal_dimension_worst  Unnamed: 32  \n",
              "0                    0.11890          NaN  \n",
              "1                    0.08902          NaN  \n",
              "2                    0.08758          NaN  \n",
              "3                    0.17300          NaN  \n",
              "4                    0.07678          NaN  \n",
              "..                       ...          ...  \n",
              "564                  0.07115          NaN  \n",
              "565                  0.06637          NaN  \n",
              "566                  0.07820          NaN  \n",
              "567                  0.12400          NaN  \n",
              "568                  0.07039          NaN  \n",
              "\n",
              "[569 rows x 33 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>...</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <th>Unnamed: 32</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842302</td>\n",
              "      <td>M</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.30010</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>...</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.16220</td>\n",
              "      <td>0.66560</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>842517</td>\n",
              "      <td>M</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>...</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>84300903</td>\n",
              "      <td>M</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>...</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>M</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>...</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
              "      <td>M</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>...</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.13740</td>\n",
              "      <td>0.20500</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>564</th>\n",
              "      <td>926424</td>\n",
              "      <td>M</td>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>...</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>926682</td>\n",
              "      <td>M</td>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>...</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>926954</td>\n",
              "      <td>M</td>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>...</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>927241</td>\n",
              "      <td>M</td>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>...</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
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              "      <th>568</th>\n",
              "      <td>92751</td>\n",
              "      <td>B</td>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
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              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
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              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>NaN</td>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d1b364de-46fc-454e-8b38-011429c1fbfa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Retaining the necessary columns\n",
        "\n",
        "data=data.drop(['Unnamed: 32'], axis=1)"
      ],
      "metadata": {
        "id": "M706glqCuyql"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for null values\n",
        "\n",
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VnMPfJouvpKl",
        "outputId": "04b1d3e8-95c7-4595-a902-714d142e8700"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                         0\n",
              "diagnosis                  0\n",
              "radius_mean                0\n",
              "texture_mean               0\n",
              "perimeter_mean             0\n",
              "area_mean                  0\n",
              "smoothness_mean            0\n",
              "compactness_mean           0\n",
              "concavity_mean             0\n",
              "concave points_mean        0\n",
              "symmetry_mean              0\n",
              "fractal_dimension_mean     0\n",
              "radius_se                  0\n",
              "texture_se                 0\n",
              "perimeter_se               0\n",
              "area_se                    0\n",
              "smoothness_se              0\n",
              "compactness_se             0\n",
              "concavity_se               0\n",
              "concave points_se          0\n",
              "symmetry_se                0\n",
              "fractal_dimension_se       0\n",
              "radius_worst               0\n",
              "texture_worst              0\n",
              "perimeter_worst            0\n",
              "area_worst                 0\n",
              "smoothness_worst           0\n",
              "compactness_worst          0\n",
              "concavity_worst            0\n",
              "concave points_worst       0\n",
              "symmetry_worst             0\n",
              "fractal_dimension_worst    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for class imbalance\n",
        "\n",
        "data.diagnosis.value_counts().plot(kind='bar',color='brown')\n",
        "data.diagnosis.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "cuqFoXy9wQ10",
        "outputId": "f7f37b9e-007e-438f-d751-18e49bdb0003"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "B    357\n",
              "M    212\n",
              "Name: diagnosis, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Spliting the data into features and target variable\n",
        "\n",
        "X=data.iloc[:, 2:] \n",
        "y=data['diagnosis']"
      ],
      "metadata": {
        "id": "wIL8ZyWyvwo-"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training and testing sets\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
      ],
      "metadata": {
        "id": "kNzsmlowvwlg"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# List of various activation funstions\n",
        "\n",
        "activation_functions=['identity', 'logistic', 'tanh', 'relu']\n",
        "\n",
        "best_accuracy=0\n",
        "best_activation_function=''\n",
        "best_model=None\n",
        "count=0"
      ],
      "metadata": {
        "id": "PTuu6CFPvwjN"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Iterating over the activation functions and training the model\n",
        "\n",
        "for activation_function in activation_functions:\n",
        "    model=MLPClassifier(hidden_layer_sizes=(100,),activation=activation_function,max_iter=1000,random_state=42)\n",
        "    \n",
        "    # Sampling the initial parameters k0 and k1 from normal distribution\n",
        "    k0=np.random.normal()\n",
        "    k1=np.random.normal()\n",
        "    \n",
        "    # Defining a custom activation function with initial parameters k0 and k1\n",
        "    def custom_activation(x):\n",
        "        return k0+k1*x\n",
        "    \n",
        "    model._activation=custom_activation\n",
        "    \n",
        "    # Training the model\n",
        "    model.fit(X_train, y_train)\n",
        "    \n",
        "    # Predicting the target variable for the test set\n",
        "    y_pred=model.predict(X_test)\n",
        "    \n",
        "    # Calculating the accuracy score\n",
        "    accuracy=accuracy_score(y_test, y_pred)\n",
        "    \n",
        "    # Calculating the F1-Score\n",
        "    f1=f1_score(y_test, y_pred, average='weighted')\n",
        "    \n",
        "    # Finding the activation function that gives the best accuracy\n",
        "    if accuracy>best_accuracy:\n",
        "        best_accuracy=accuracy\n",
        "        best_activation_function=activation_function\n",
        "        best_model=model\n",
        "    \n",
        "    # Plotting the loss function vs. epochs\n",
        "    plt.plot(range(model.n_iter_), model.loss_curve_, label=activation_function)\n",
        "\n",
        "    # Storing the results in a dataframe\n",
        "    if count==0:\n",
        "      results_df=pd.DataFrame(data=[[activation_function,best_accuracy]], columns=['Activation Function', 'Model Accuracy'])\n",
        "    else:\n",
        "      results_df_2=pd.DataFrame(data=[[activation_function,best_accuracy]], columns=['Activation Function', 'Model Accuracy'])\n",
        "      results_df=results_df.append(results_df_2, ignore_index=True)\n",
        "    count+=1\n",
        "\n",
        "# Plotting the loss function vs. epochs\n",
        "\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ninIP73dvwhE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "c4f6288b-525d-4387-900e-d38c87e18413"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-1a7824457053>:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  results_df=results_df.append(results_df_2, ignore_index=True)\n",
            "<ipython-input-9-1a7824457053>:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  results_df=results_df.append(results_df_2, ignore_index=True)\n",
            "<ipython-input-9-1a7824457053>:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  results_df=results_df.append(results_df_2, ignore_index=True)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Printing the best activation function\n",
        "\n",
        "print(\"Best activation function:\", best_activation_function)"
      ],
      "metadata": {
        "id": "MmPlb1EwvwfG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "889e3fce-b6c6-455e-8a37-cde90531a89c"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best activation function: logistic\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Printing the final parameter values at the end of training\n",
        "\n",
        "final_parameters=[k0, k1]\n",
        "print(\"Final parameter values:\", final_parameters)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mcbu_HrV1R4_",
        "outputId": "5e722bd7-18cb-42e6-9563-6f4aaefbea29"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final parameter values: [-1.213398469890955, -1.4231619063773493]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the train accuracy and F1-Score\n",
        "\n",
        "y_train_pred=best_model.predict(X_train)\n",
        "train_accuracy=accuracy_score(y_train, y_train_pred)\n",
        "train_f1=f1_score(y_train, y_train_pred, average='weighted')\n",
        "print(\"Train accuracy:\", train_accuracy)\n",
        "print(\"Train F1-Score:\", train_f1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IsCDMQlW1UzC",
        "outputId": "4e841fc5-3950-4934-f810-61d8b87a4a2a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train accuracy: 0.9522613065326633\n",
            "Train F1-Score: 0.9519416866894206\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the test accuracy and F1-Score\n",
        "\n",
        "test_accuracy=best_accuracy\n",
        "test_f1=f1_score(y_test, y_pred, average='weighted')\n",
        "print(\"Test accuracy:\", test_accuracy)\n",
        "print(\"Test F1-Score:\", test_f1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qAPIChRk1Uvp",
        "outputId": "d4544868-91f3-41a1-ce7f-2f68758cfc40"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test accuracy: 0.9707602339181286\n",
            "Test F1-Score: 0.9647902680643603\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Comparing the activation functions\n",
        "\n",
        "display(results_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "pllyzdNI1Uqn",
        "outputId": "308fb2ac-a977-419d-bc25-604d56baeffa"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  Activation Function  Model Accuracy\n",
              "0            identity        0.959064\n",
              "1            logistic        0.970760\n",
              "2                tanh        0.970760\n",
              "3                relu        0.970760"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fb683402-54f1-45f6-b4d6-21dee1572546\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>Activation Function</th>\n",
              "      <th>Model Accuracy</th>\n",
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              "      <td>0.959064</td>\n",
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              "      <td>0.970760</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tanh</td>\n",
              "      <td>0.970760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>relu</td>\n",
              "      <td>0.970760</td>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          const element = document.querySelector('#df-fb683402-54f1-45f6-b4d6-21dee1572546');\n",
              "          const dataTable =\n",
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              "                                                     [key], {});\n",
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              "\n",
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    }
  ]
}