breast_cancer_model .ipynb

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      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "u10jnhA519a4"
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      "source": [
        "import sklearn.datasets\n",
        "\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#Data Loading\n"
      ],
      "metadata": {
        "id": "_WWOd-kW2QuS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "breast_cancer = sklearn.datasets.load_breast_cancer()"
      ],
      "metadata": {
        "id": "1RzX6rYs2axu"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = breast_cancer.data\n",
        "Y = breast_cancer.target"
      ],
      "metadata": {
        "id": "9xR8Ao0J3qIh"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)\n",
        "print(Y)"
      ],
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        "id": "8nYoMW9w3xa2",
        "outputId": "5079a3f4-d8d0-4c8a-f059-a5f8158db595"
      },
      "execution_count": 23,
      "outputs": [
        {
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          "text": [
            "[[1.799e+01 1.038e+01 1.228e+02 ... 2.654e-01 4.601e-01 1.189e-01]\n",
            " [2.057e+01 1.777e+01 1.329e+02 ... 1.860e-01 2.750e-01 8.902e-02]\n",
            " [1.969e+01 2.125e+01 1.300e+02 ... 2.430e-01 3.613e-01 8.758e-02]\n",
            " ...\n",
            " [1.660e+01 2.808e+01 1.083e+02 ... 1.418e-01 2.218e-01 7.820e-02]\n",
            " [2.060e+01 2.933e+01 1.401e+02 ... 2.650e-01 4.087e-01 1.240e-01]\n",
            " [7.760e+00 2.454e+01 4.792e+01 ... 0.000e+00 2.871e-01 7.039e-02]]\n",
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            " 1 1 1 1 1 1 1 0 0 0 0 0 0 1]\n"
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      "source": [
        "print(X.shape,Y.shape)"
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      "execution_count": 24,
      "outputs": [
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          "name": "stdout",
          "text": [
            "(569, 30) (569,)\n"
          ]
        }
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "data=pd.DataFrame(X , columns=breast_cancer.feature_names)"
      ],
      "metadata": {
        "id": "5RrFevJP376R"
      },
      "execution_count": 25,
      "outputs": []
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        "data['class']=Y"
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      "execution_count": 26,
      "outputs": []
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        "data.head(5)"
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      },
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      "outputs": [
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              "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0        17.99         10.38          122.80     1001.0          0.11840   \n",
              "1        20.57         17.77          132.90     1326.0          0.08474   \n",
              "2        19.69         21.25          130.00     1203.0          0.10960   \n",
              "3        11.42         20.38           77.58      386.1          0.14250   \n",
              "4        20.29         14.34          135.10     1297.0          0.10030   \n",
              "\n",
              "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0           0.27760          0.3001              0.14710         0.2419   \n",
              "1           0.07864          0.0869              0.07017         0.1812   \n",
              "2           0.15990          0.1974              0.12790         0.2069   \n",
              "3           0.28390          0.2414              0.10520         0.2597   \n",
              "4           0.13280          0.1980              0.10430         0.1809   \n",
              "\n",
              "   mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
              "0                 0.07871  ...          17.33           184.60      2019.0   \n",
              "1                 0.05667  ...          23.41           158.80      1956.0   \n",
              "2                 0.05999  ...          25.53           152.50      1709.0   \n",
              "3                 0.09744  ...          26.50            98.87       567.7   \n",
              "4                 0.05883  ...          16.67           152.20      1575.0   \n",
              "\n",
              "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
              "0            0.1622             0.6656           0.7119                0.2654   \n",
              "1            0.1238             0.1866           0.2416                0.1860   \n",
              "2            0.1444             0.4245           0.4504                0.2430   \n",
              "3            0.2098             0.8663           0.6869                0.2575   \n",
              "4            0.1374             0.2050           0.4000                0.1625   \n",
              "\n",
              "   worst symmetry  worst fractal dimension  class  \n",
              "0          0.4601                  0.11890      0  \n",
              "1          0.2750                  0.08902      0  \n",
              "2          0.3613                  0.08758      0  \n",
              "3          0.6638                  0.17300      0  \n",
              "4          0.2364                  0.07678      0  \n",
              "\n",
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              "      <th></th>\n",
              "      <th>mean radius</th>\n",
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              "      <th>mean symmetry</th>\n",
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              "      <th>...</th>\n",
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              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
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              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
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              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
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              "      <td>17.33</td>\n",
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              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
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              "      <th>1</th>\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>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
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              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
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              "      <th>3</th>\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>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    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",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      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-60bb9fbf-8f14-46a6-915d-5b06d85f6144 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data['class'].value_counts())\n",
        "print(\"Mean of the data : \",data['class'].mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iaVYwGar44o7",
        "outputId": "ff4cbcdd-2d09-4b80-9901-ca79864c095a"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "class\n",
            "1    357\n",
            "0    212\n",
            "Name: count, dtype: int64\n",
            "Mean of the data :  0.6274165202108963\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(breast_cancer.target_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "suOkIs8N5LGW",
        "outputId": "8095e14b-b403-4241-df35-93de61039c51"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['malignant' 'benign']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The above shows  1 is malignant and 2 benign i.e with cancer there are more samples than without"
      ],
      "metadata": {
        "id": "z4xuJ_yd5qWg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby('class').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "id": "kHrg1vDE5hGP",
        "outputId": "ca021f58-9c89-4082-81f4-895c76b328c1"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       mean radius  mean texture  mean perimeter   mean area  mean smoothness  \\\n",
              "class                                                                           \n",
              "0        17.462830     21.604906      115.365377  978.376415         0.102898   \n",
              "1        12.146524     17.914762       78.075406  462.790196         0.092478   \n",
              "\n",
              "       mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "class                                                                         \n",
              "0              0.145188        0.160775             0.087990       0.192909   \n",
              "1              0.080085        0.046058             0.025717       0.174186   \n",
              "\n",
              "       mean fractal dimension  ...  worst radius  worst texture  \\\n",
              "class                          ...                                \n",
              "0                    0.062680  ...     21.134811      29.318208   \n",
              "1                    0.062867  ...     13.379801      23.515070   \n",
              "\n",
              "       worst perimeter   worst area  worst smoothness  worst compactness  \\\n",
              "class                                                                      \n",
              "0           141.370330  1422.286321          0.144845           0.374824   \n",
              "1            87.005938   558.899440          0.124959           0.182673   \n",
              "\n",
              "       worst concavity  worst concave points  worst symmetry  \\\n",
              "class                                                          \n",
              "0             0.450606              0.182237        0.323468   \n",
              "1             0.166238              0.074444        0.270246   \n",
              "\n",
              "       worst fractal dimension  \n",
              "class                           \n",
              "0                     0.091530  \n",
              "1                     0.079442  \n",
              "\n",
              "[2 rows x 30 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-510548b9-e0fa-46eb-95a1-f102d389f883\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.462830</td>\n",
              "      <td>21.604906</td>\n",
              "      <td>115.365377</td>\n",
              "      <td>978.376415</td>\n",
              "      <td>0.102898</td>\n",
              "      <td>0.145188</td>\n",
              "      <td>0.160775</td>\n",
              "      <td>0.087990</td>\n",
              "      <td>0.192909</td>\n",
              "      <td>0.062680</td>\n",
              "      <td>...</td>\n",
              "      <td>21.134811</td>\n",
              "      <td>29.318208</td>\n",
              "      <td>141.370330</td>\n",
              "      <td>1422.286321</td>\n",
              "      <td>0.144845</td>\n",
              "      <td>0.374824</td>\n",
              "      <td>0.450606</td>\n",
              "      <td>0.182237</td>\n",
              "      <td>0.323468</td>\n",
              "      <td>0.091530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12.146524</td>\n",
              "      <td>17.914762</td>\n",
              "      <td>78.075406</td>\n",
              "      <td>462.790196</td>\n",
              "      <td>0.092478</td>\n",
              "      <td>0.080085</td>\n",
              "      <td>0.046058</td>\n",
              "      <td>0.025717</td>\n",
              "      <td>0.174186</td>\n",
              "      <td>0.062867</td>\n",
              "      <td>...</td>\n",
              "      <td>13.379801</td>\n",
              "      <td>23.515070</td>\n",
              "      <td>87.005938</td>\n",
              "      <td>558.899440</td>\n",
              "      <td>0.124959</td>\n",
              "      <td>0.182673</td>\n",
              "      <td>0.166238</td>\n",
              "      <td>0.074444</td>\n",
              "      <td>0.270246</td>\n",
              "      <td>0.079442</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 30 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-510548b9-e0fa-46eb-95a1-f102d389f883')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-510548b9-e0fa-46eb-95a1-f102d389f883 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-510548b9-e0fa-46eb-95a1-f102d389f883');\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",
              "\n",
              "\n",
              "<div id=\"df-94b0eea5-5288-4af0-a30c-f9fa16f83a77\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-94b0eea5-5288-4af0-a30c-f9fa16f83a77')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    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",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      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-94b0eea5-5288-4af0-a30c-f9fa16f83a77 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Train - Test split"
      ],
      "metadata": {
        "id": "2i8PBbBY6Fhe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n"
      ],
      "metadata": {
        "id": "L8Etg5LV56Vh"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop('class',axis=1)\n",
        "Y=data['class']"
      ],
      "metadata": {
        "id": "X9VOZodM6PxP"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "x and y are now dataframes and not numpy"
      ],
      "metadata": {
        "id": "-IGLkA4d6ZCL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "type(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "E86XBZF86YTx",
        "outputId": "ed1c7d5c-dac1-47fc-8f91-b014835bdd78"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ],
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              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
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              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.frame.DataFrame</b><br/>def __init__(data=None, index: Axes | None=None, columns: Axes | None=None, dtype: Dtype | None=None, copy: bool | None=None) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py</a>Two-dimensional, size-mutable, potentially heterogeneous tabular data.\n",
              "\n",
              "Data structure also contains labeled axes (rows and columns).\n",
              "Arithmetic operations align on both row and column labels. Can be\n",
              "thought of as a dict-like container for Series objects. The primary\n",
              "pandas data structure.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame\n",
              "    Dict can contain Series, arrays, constants, dataclass or list-like objects. If\n",
              "    data is a dict, column order follows insertion-order. If a dict contains Series\n",
              "    which have an index defined, it is aligned by its index. This alignment also\n",
              "    occurs if data is a Series or a DataFrame itself. Alignment is done on\n",
              "    Series/DataFrame inputs.\n",
              "\n",
              "    If data is a list of dicts, column order follows insertion-order.\n",
              "\n",
              "index : Index or array-like\n",
              "    Index to use for resulting frame. Will default to RangeIndex if\n",
              "    no indexing information part of input data and no index provided.\n",
              "columns : Index or array-like\n",
              "    Column labels to use for resulting frame when data does not have them,\n",
              "    defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels,\n",
              "    will perform column selection instead.\n",
              "dtype : dtype, default None\n",
              "    Data type to force. Only a single dtype is allowed. If None, infer.\n",
              "copy : bool or None, default None\n",
              "    Copy data from inputs.\n",
              "    For dict data, the default of None behaves like ``copy=True``.  For DataFrame\n",
              "    or 2d ndarray input, the default of None behaves like ``copy=False``.\n",
              "    If data is a dict containing one or more Series (possibly of different dtypes),\n",
              "    ``copy=False`` will ensure that these inputs are not copied.\n",
              "\n",
              "    .. versionchanged:: 1.3.0\n",
              "\n",
              "See Also\n",
              "--------\n",
              "DataFrame.from_records : Constructor from tuples, also record arrays.\n",
              "DataFrame.from_dict : From dicts of Series, arrays, or dicts.\n",
              "read_csv : Read a comma-separated values (csv) file into DataFrame.\n",
              "read_table : Read general delimited file into DataFrame.\n",
              "read_clipboard : Read text from clipboard into DataFrame.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide &lt;basics.dataframe&gt;` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing DataFrame from a dictionary.\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [1, 2], &#x27;col2&#x27;: [3, 4]}\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d)\n",
              "&gt;&gt;&gt; df\n",
              "   col1  col2\n",
              "0     1     3\n",
              "1     2     4\n",
              "\n",
              "Notice that the inferred dtype is int64.\n",
              "\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int64\n",
              "col2    int64\n",
              "dtype: object\n",
              "\n",
              "To enforce a single dtype:\n",
              "\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d, dtype=np.int8)\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int8\n",
              "col2    int8\n",
              "dtype: object\n",
              "\n",
              "Constructing DataFrame from a dictionary including Series:\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [0, 1, 2, 3], &#x27;col2&#x27;: pd.Series([2, 3], index=[2, 3])}\n",
              "&gt;&gt;&gt; pd.DataFrame(data=d, index=[0, 1, 2, 3])\n",
              "   col1  col2\n",
              "0     0   NaN\n",
              "1     1   NaN\n",
              "2     2   2.0\n",
              "3     3   3.0\n",
              "\n",
              "Constructing DataFrame from numpy ndarray:\n",
              "\n",
              "&gt;&gt;&gt; df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),\n",
              "...                    columns=[&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;])\n",
              "&gt;&gt;&gt; df2\n",
              "   a  b  c\n",
              "0  1  2  3\n",
              "1  4  5  6\n",
              "2  7  8  9\n",
              "\n",
              "Constructing DataFrame from a numpy ndarray that has labeled columns:\n",
              "\n",
              "&gt;&gt;&gt; data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],\n",
              "...                 dtype=[(&quot;a&quot;, &quot;i4&quot;), (&quot;b&quot;, &quot;i4&quot;), (&quot;c&quot;, &quot;i4&quot;)])\n",
              "&gt;&gt;&gt; df3 = pd.DataFrame(data, columns=[&#x27;c&#x27;, &#x27;a&#x27;])\n",
              "...\n",
              "&gt;&gt;&gt; df3\n",
              "   c  a\n",
              "0  3  1\n",
              "1  6  4\n",
              "2  9  7\n",
              "\n",
              "Constructing DataFrame from dataclass:\n",
              "\n",
              "&gt;&gt;&gt; from dataclasses import make_dataclass\n",
              "&gt;&gt;&gt; Point = make_dataclass(&quot;Point&quot;, [(&quot;x&quot;, int), (&quot;y&quot;, int)])\n",
              "&gt;&gt;&gt; pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])\n",
              "   x  y\n",
              "0  0  0\n",
              "1  0  3\n",
              "2  2  3\n",
              "\n",
              "Constructing DataFrame from Series/DataFrame:\n",
              "\n",
              "&gt;&gt;&gt; ser = pd.Series([1, 2, 3], index=[&quot;a&quot;, &quot;b&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=ser, index=[&quot;a&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df\n",
              "   0\n",
              "a  1\n",
              "c  3\n",
              "\n",
              "&gt;&gt;&gt; df1 = pd.DataFrame([1, 2, 3], index=[&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], columns=[&quot;x&quot;])\n",
              "&gt;&gt;&gt; df2 = pd.DataFrame(data=df1, index=[&quot;a&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df2\n",
              "   x\n",
              "a  1\n",
              "c  3</pre>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train,Y_test = train_test_split(X,Y,test_size = 0.1,stratify = Y, random_state = 42)"
      ],
      "metadata": {
        "id": "LTfc-ful6eNX"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape,Y_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u7bL9vHJ61UM",
        "outputId": "5f464e49-690a-4d60-e676-98286e005fdb"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(512, 30) (512,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test.shape,Y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VbJM9YwS668d",
        "outputId": "e99eff7a-8d2d-4c68-ef0e-68e22fb02156"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(57, 30) (57,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_train.mean(),Y_test.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V06Kjuz17CEL",
        "outputId": "10ede3bd-30e2-4d24-d112-801e5ac7d593"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.626953125 0.631578947368421\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data Visualization"
      ],
      "metadata": {
        "id": "dmohi29C8mpP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "7ZS5HLlp7sOZ"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(X_train.T,'*')\n",
        "plt.xticks(rotation = 'vertical')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "as8CLzN982gD",
        "outputId": "7bd7fcf0-210b-4cf1-96a9-46d0ddef9351"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Binarizing the data"
      ],
      "metadata": {
        "id": "KCKqlVH-_sbE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_binarised_train = X_train.apply(pd.cut, bins = 2, labels =[1,0])"
      ],
      "metadata": {
        "id": "VxivL-AX9Olk"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(X_binarised_train.T,'*')\n",
        "plt.xticks(rotation = 'vertical')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "H6rZPOVT-Xq5",
        "outputId": "d45e2b50-3f7f-4e2c-c003-afd021b82dbe"
      },
      "execution_count": 41,
      "outputs": [
        {
          "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": [
        "X_binarised_test = X_test.apply(pd.cut,bins=2,labels = [1,0])"
      ],
      "metadata": {
        "id": "V9IrKAFk_T4i"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(X_binarised_test)"
      ],
      "metadata": {
        "id": "JgUQQLc5_0W1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "555ec8c2-8f5f-48fc-960d-dbb3239adef2"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.frame.DataFrame</b><br/>def __init__(data=None, index: Axes | None=None, columns: Axes | None=None, dtype: Dtype | None=None, copy: bool | None=None) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py</a>Two-dimensional, size-mutable, potentially heterogeneous tabular data.\n",
              "\n",
              "Data structure also contains labeled axes (rows and columns).\n",
              "Arithmetic operations align on both row and column labels. Can be\n",
              "thought of as a dict-like container for Series objects. The primary\n",
              "pandas data structure.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame\n",
              "    Dict can contain Series, arrays, constants, dataclass or list-like objects. If\n",
              "    data is a dict, column order follows insertion-order. If a dict contains Series\n",
              "    which have an index defined, it is aligned by its index. This alignment also\n",
              "    occurs if data is a Series or a DataFrame itself. Alignment is done on\n",
              "    Series/DataFrame inputs.\n",
              "\n",
              "    If data is a list of dicts, column order follows insertion-order.\n",
              "\n",
              "index : Index or array-like\n",
              "    Index to use for resulting frame. Will default to RangeIndex if\n",
              "    no indexing information part of input data and no index provided.\n",
              "columns : Index or array-like\n",
              "    Column labels to use for resulting frame when data does not have them,\n",
              "    defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels,\n",
              "    will perform column selection instead.\n",
              "dtype : dtype, default None\n",
              "    Data type to force. Only a single dtype is allowed. If None, infer.\n",
              "copy : bool or None, default None\n",
              "    Copy data from inputs.\n",
              "    For dict data, the default of None behaves like ``copy=True``.  For DataFrame\n",
              "    or 2d ndarray input, the default of None behaves like ``copy=False``.\n",
              "    If data is a dict containing one or more Series (possibly of different dtypes),\n",
              "    ``copy=False`` will ensure that these inputs are not copied.\n",
              "\n",
              "    .. versionchanged:: 1.3.0\n",
              "\n",
              "See Also\n",
              "--------\n",
              "DataFrame.from_records : Constructor from tuples, also record arrays.\n",
              "DataFrame.from_dict : From dicts of Series, arrays, or dicts.\n",
              "read_csv : Read a comma-separated values (csv) file into DataFrame.\n",
              "read_table : Read general delimited file into DataFrame.\n",
              "read_clipboard : Read text from clipboard into DataFrame.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide &lt;basics.dataframe&gt;` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing DataFrame from a dictionary.\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [1, 2], &#x27;col2&#x27;: [3, 4]}\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d)\n",
              "&gt;&gt;&gt; df\n",
              "   col1  col2\n",
              "0     1     3\n",
              "1     2     4\n",
              "\n",
              "Notice that the inferred dtype is int64.\n",
              "\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int64\n",
              "col2    int64\n",
              "dtype: object\n",
              "\n",
              "To enforce a single dtype:\n",
              "\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d, dtype=np.int8)\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int8\n",
              "col2    int8\n",
              "dtype: object\n",
              "\n",
              "Constructing DataFrame from a dictionary including Series:\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [0, 1, 2, 3], &#x27;col2&#x27;: pd.Series([2, 3], index=[2, 3])}\n",
              "&gt;&gt;&gt; pd.DataFrame(data=d, index=[0, 1, 2, 3])\n",
              "   col1  col2\n",
              "0     0   NaN\n",
              "1     1   NaN\n",
              "2     2   2.0\n",
              "3     3   3.0\n",
              "\n",
              "Constructing DataFrame from numpy ndarray:\n",
              "\n",
              "&gt;&gt;&gt; df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),\n",
              "...                    columns=[&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;])\n",
              "&gt;&gt;&gt; df2\n",
              "   a  b  c\n",
              "0  1  2  3\n",
              "1  4  5  6\n",
              "2  7  8  9\n",
              "\n",
              "Constructing DataFrame from a numpy ndarray that has labeled columns:\n",
              "\n",
              "&gt;&gt;&gt; data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],\n",
              "...                 dtype=[(&quot;a&quot;, &quot;i4&quot;), (&quot;b&quot;, &quot;i4&quot;), (&quot;c&quot;, &quot;i4&quot;)])\n",
              "&gt;&gt;&gt; df3 = pd.DataFrame(data, columns=[&#x27;c&#x27;, &#x27;a&#x27;])\n",
              "...\n",
              "&gt;&gt;&gt; df3\n",
              "   c  a\n",
              "0  3  1\n",
              "1  6  4\n",
              "2  9  7\n",
              "\n",
              "Constructing DataFrame from dataclass:\n",
              "\n",
              "&gt;&gt;&gt; from dataclasses import make_dataclass\n",
              "&gt;&gt;&gt; Point = make_dataclass(&quot;Point&quot;, [(&quot;x&quot;, int), (&quot;y&quot;, int)])\n",
              "&gt;&gt;&gt; pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])\n",
              "   x  y\n",
              "0  0  0\n",
              "1  0  3\n",
              "2  2  3\n",
              "\n",
              "Constructing DataFrame from Series/DataFrame:\n",
              "\n",
              "&gt;&gt;&gt; ser = pd.Series([1, 2, 3], index=[&quot;a&quot;, &quot;b&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=ser, index=[&quot;a&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df\n",
              "   0\n",
              "a  1\n",
              "c  3\n",
              "\n",
              "&gt;&gt;&gt; df1 = pd.DataFrame([1, 2, 3], index=[&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], columns=[&quot;x&quot;])\n",
              "&gt;&gt;&gt; df2 = pd.DataFrame(data=df1, index=[&quot;a&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df2\n",
              "   x\n",
              "a  1\n",
              "c  3</pre>\n",
              "      <script>\n",
              "      if (google.colab.kernel.accessAllowed && google.colab.files && google.colab.files.view) {\n",
              "        for (const element of document.querySelectorAll('.filepath')) {\n",
              "          element.style.display = 'block'\n",
              "          element.onclick = (event) => {\n",
              "            event.preventDefault();\n",
              "            event.stopPropagation();\n",
              "            google.colab.files.view(element.textContent, 490);\n",
              "          };\n",
              "        }\n",
              "      }\n",
              "      for (const element of document.querySelectorAll('.function-repr-contents')) {\n",
              "        element.onclick = (event) => {\n",
              "          event.preventDefault();\n",
              "          event.stopPropagation();\n",
              "          element.classList.toggle('function-repr-contents-collapsed');\n",
              "        };\n",
              "      }\n",
              "      </script>\n",
              "      </div>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_binarised_test = X_binarised_test.values\n",
        "X_binarised_train = X_binarised_train.values"
      ],
      "metadata": {
        "id": "EecG_3uiAgv3"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(X_binarised_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1KNdCel_AqJF",
        "outputId": "001acb82-9e55-4d82-de06-9ca444002911"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#MP neuron"
      ],
      "metadata": {
        "id": "tbfi3M6XBkEG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_binarised_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vddDAZF7AsMJ",
        "outputId": "eae45bd3-2497-4b4d-e441-98f4dcf2635e"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(512, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for b in range(X_binarised_train.shape[1]+1):\n",
        "  Y_pred_train = []\n",
        "  accurate_rows = 0\n",
        "  for x,y in zip(X_binarised_train,Y_train):\n",
        "    Y_pred = (np.sum(x)>=b)\n",
        "    Y_pred_train.append(Y_pred)\n",
        "    accurate_rows+=(y == Y_pred)\n",
        "  print(b,accurate_rows/X_binarised_train.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vXzuRA9bCNkL",
        "outputId": "7b92e003-9d7a-496b-feee-2b843d9b1f5f"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 0.626953125\n",
            "1 0.626953125\n",
            "2 0.626953125\n",
            "3 0.626953125\n",
            "4 0.626953125\n",
            "5 0.626953125\n",
            "6 0.626953125\n",
            "7 0.626953125\n",
            "8 0.626953125\n",
            "9 0.626953125\n",
            "10 0.626953125\n",
            "11 0.626953125\n",
            "12 0.626953125\n",
            "13 0.62890625\n",
            "14 0.6328125\n",
            "15 0.638671875\n",
            "16 0.642578125\n",
            "17 0.65234375\n",
            "18 0.662109375\n",
            "19 0.6640625\n",
            "20 0.677734375\n",
            "21 0.6953125\n",
            "22 0.712890625\n",
            "23 0.740234375\n",
            "24 0.775390625\n",
            "25 0.802734375\n",
            "26 0.830078125\n",
            "27 0.861328125\n",
            "28 0.8515625\n",
            "29 0.80859375\n",
            "30 0.71875\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "accuracy is max at b=27"
      ],
      "metadata": {
        "id": "_0_AGf60D-m-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Evaluation"
      ],
      "metadata": {
        "id": "anEqDeT4E_DI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "bKPsR1TkDipQ"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yN1MgxwnF4kT",
        "outputId": "a3142995-f9a3-4e6e-82b4-89e59723e092"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(57,)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b=27\n",
        "Y_pred_test = []\n",
        "for x in X_binarised_test:\n",
        "  Y_pred = (np.sum(x)>=b)\n",
        "  Y_pred_test.append(Y_pred)\n",
        "\n",
        "accuracy = accuracy_score(Y_pred_test, Y_test)\n",
        "print(accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xrDLAqTvFKPN",
        "outputId": "156fca88-3a11-419f-a20c-bcc139af7b15"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8421052631578947\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#MP neuron Class"
      ],
      "metadata": {
        "id": "AD-6pDKUGYfA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class MpNeuron:\n",
        "\n",
        "    def __init__(self):\n",
        "        self.b = None\n",
        "\n",
        "    def model(self, x):\n",
        "        return (sum(x) >= self.b)\n",
        "\n",
        "    def predict(self, X):\n",
        "        Y = []\n",
        "        for x in X:\n",
        "            result = self.model(x)\n",
        "            Y.append(result)\n",
        "        return np.array(Y)\n",
        "    def fit(self, X, Y):\n",
        "        accuracy = {}\n",
        "        for b in range(X.shape[1] + 1):\n",
        "            self.b = b\n",
        "            Y_pred = self.predict(X)\n",
        "            accuracy[b] = accuracy_score(Y, Y_pred)\n",
        "        best_b = max(accuracy, key=accuracy.get)\n",
        "        self.b = best_b\n",
        "\n",
        "        print('Optimal value of b is ', best_b)\n",
        "        print('Highest accuracy is ', accuracy[best_b])\n"
      ],
      "metadata": {
        "id": "40BNrYsoFVLw"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mp_neuron=MpNeuron()"
      ],
      "metadata": {
        "id": "ObAsZzo6Ia6a"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B1-kPWdGMLZZ",
        "outputId": "becaa83d-410a-40ea-8ff9-5dca8efa79df"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(512,)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mp_neuron.fit(X_binarised_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_E83341XJ1ml",
        "outputId": "162e42df-b24c-43d8-e624-ae4d62709e20"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal value of b is  27\n",
            "Highest accuracy is  0.861328125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mp_neuron.fit(X_binarised_test,Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wKkvGh6GKbjB",
        "outputId": "9bdf3746-23b4-4d42-ae0d-39159b924877"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal value of b is  22\n",
            "Highest accuracy is  0.8421052631578947\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Perceptron model\n"
      ],
      "metadata": {
        "id": "-RbWVrWtNs4H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Perceptron:\n",
        "\n",
        "  def __init__(self):\n",
        "    self.w = None\n",
        "    self.b = None\n",
        "\n",
        "  def model(self, x):\n",
        "    return 1 if (np.dot(self.w, x) >= self.b) else 0\n",
        "\n",
        "  def predict(self, X):\n",
        "    Y = []\n",
        "    for x in X:\n",
        "      result = self.model(x)\n",
        "      Y.append(result)\n",
        "    return np.array(Y)\n",
        "\n",
        "  def fit(self, X, Y,epochs,lr):\n",
        "    self.w = np.ones(X.shape[1])\n",
        "    self.b = 0\n",
        "    for i in range(epochs):\n",
        "      for x, y in zip(X, Y):\n",
        "        y_pred = self.model(x)\n",
        "        if y == 1 and y_pred == 0:\n",
        "          self.w += lr*x\n",
        "          self.b += lr*1\n",
        "        elif y == 0 and y_pred == 1:\n",
        "          self.w -= lr*x\n",
        "          self.b -= lr*1"
      ],
      "metadata": {
        "id": "0Q2jRacYNHnk"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "perceptron = Perceptron()"
      ],
      "metadata": {
        "id": "ueO6jEfLQls0"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "perceptron.fit(X_train,Y_train,2,1)"
      ],
      "metadata": {
        "id": "YVydsviLQd4X"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_predicted = perceptron.predict(X_train)\n",
        "accuracy = accuracy_score(Y_predicted,Y_train)\n",
        "print(accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EE7RwjDAQjTt",
        "outputId": "4769c4ed-dfb1-4323-f3c2-141e153b6194"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.919921875\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_predicted = perceptron.predict(X_test)\n",
        "accuracy = accuracy_score(Y_predicted,Y_test)\n",
        "print(accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wYPVlvUwTIeU",
        "outputId": "f487cfae-9a44-4506-ff74-169fab1213a6"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8771929824561403\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gRPw2zTNTkTq"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}