Book_Chapter5.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Mateusz-best-creator/Learning_ML/blob/main/Book_Chapter5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# SVM"
      ],
      "metadata": {
        "id": "DKyWaKOf116w"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "id": "t5e4jjR4wMKf",
        "outputId": "f0563ccc-38b9-4942-bd54-55d4c196caab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
              "                ('linearsvc', LinearSVC(C=1, random_state=42))])"
            ],
            "text/html": [
              "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearsvc&#x27;, LinearSVC(C=1, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearsvc&#x27;, LinearSVC(C=1, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(C=1, random_state=42)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "from sklearn.datasets import load_iris\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.svm import LinearSVC\n",
        "\n",
        "iris = load_iris(as_frame=True)\n",
        "X = iris.data[[\"petal length (cm)\", \"petal width (cm)\"]].values\n",
        "y = (iris.target == 2) # Iris virginica\n",
        "\n",
        "svm_clf = make_pipeline(StandardScaler(),\n",
        "                        LinearSVC(C=1, random_state=42))\n",
        "svm_clf.fit(X, y)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_new = [[5.5, 1.7], [5.0, 1.5]]\n",
        "svm_clf.predict(X_new)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "smmAluS91Jpm",
        "outputId": "063c1087-6c47-4076-cd79-f3f80e744d72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True, False])"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svm_clf.decision_function(X_new)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8tm0Y3qe1YxS",
        "outputId": "5cd37d2a-06ff-4f9e-b591-885ef80e9acb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.66163411, -0.22036063])"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import make_moons\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "\n",
        "X, y = make_moons(n_samples=100, noise=0.15, random_state=42)\n",
        "\n",
        "polynomial_svm_clf = make_pipeline(PolynomialFeatures(degree=3),\n",
        "                                   StandardScaler(),\n",
        "                                   LinearSVC(C=10, max_iter=10000, random_state=42))\n",
        "polynomial_svm_clf.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        },
        "id": "z0ZpKCBB1cRA",
        "outputId": "f4728e17-540d-4c5b-dd89-d84cf4049158"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=3)),\n",
              "                ('standardscaler', StandardScaler()),\n",
              "                ('linearsvc',\n",
              "                 LinearSVC(C=10, max_iter=10000, random_state=42))])"
            ],
            "text/html": [
              "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;polynomialfeatures&#x27;, PolynomialFeatures(degree=3)),\n",
              "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearsvc&#x27;,\n",
              "                 LinearSVC(C=10, max_iter=10000, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;polynomialfeatures&#x27;, PolynomialFeatures(degree=3)),\n",
              "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearsvc&#x27;,\n",
              "                 LinearSVC(C=10, max_iter=10000, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures(degree=3)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(C=10, max_iter=10000, random_state=42)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Polynomial Kernel"
      ],
      "metadata": {
        "id": "fRiJ3nk62Pmi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "poly_kernel_svm_clf = make_pipeline(StandardScaler(),\n",
        "                                    SVC(kernel=\"poly\", degree=3, coef0=1, C=5))\n",
        "poly_kernel_svm_clf.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "id": "V5Xijjfm2mtY",
        "outputId": "8539526e-c8e4-4088-9fe9-a301696d14e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
              "                ('svc', SVC(C=5, coef0=1, kernel='poly'))])"
            ],
            "text/html": [
              "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;svc&#x27;, SVC(C=5, coef0=1, kernel=&#x27;poly&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;svc&#x27;, SVC(C=5, coef0=1, kernel=&#x27;poly&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=5, coef0=1, kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Gaussian Similarity"
      ],
      "metadata": {
        "id": "dJxYuDgB2mwR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rbf_kernel_svm_clf = make_pipeline(StandardScaler(),\n",
        "                                   SVC(kernel=\"rbf\", gamma=5., C=0.001))\n",
        "rbf_kernel_svm_clf.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "id": "MbyhovJ32mzb",
        "outputId": "fc4a1b3b-3a95-412f-cce8-06e0b6dc01fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
              "                ('svc', SVC(C=0.001, gamma=5.0))])"
            ],
            "text/html": [
              "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;svc&#x27;, SVC(C=0.001, gamma=5.0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;svc&#x27;, SVC(C=0.001, gamma=5.0))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.001, gamma=5.0)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SVM Regression"
      ],
      "metadata": {
        "id": "9_RvZeLR2m2Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import LinearSVR\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "np.random.seed(42)\n",
        "m = 100\n",
        "X = 6 * np.random.rand(m, 1) - 3\n",
        "y = 0.5 * X ** 2 + X + 2 + np.random.randn(m, 1)\n",
        "plt.plot(X.ravel(), y.ravel(), 'b.');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "Hwne24bj30av",
        "outputId": "27324fa4-755a-47c2-bf44-9fa510894dd7"
      },
      "execution_count": null,
      "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": [
        "svm_reg = make_pipeline(StandardScaler(),\n",
        "                        LinearSVR(epsilon=0.5))\n",
        "svm_reg.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 182
        },
        "id": "R6n-pHXM30dw",
        "outputId": "eaef0fd8-af1d-41ef-d12b-1f33f970d16a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
              "                ('linearsvr', LinearSVR(epsilon=0.5))])"
            ],
            "text/html": [
              "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearsvr&#x27;, LinearSVR(epsilon=0.5))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearsvr&#x27;, LinearSVR(epsilon=0.5))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-47\" type=\"checkbox\" ><label for=\"sk-estimator-id-47\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVR</label><div class=\"sk-toggleable__content\"><pre>LinearSVR(epsilon=0.5)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVR\n",
        "svm_poly_reg = make_pipeline(StandardScaler(),\n",
        "                             SVR(kernel=\"poly\", degree=3, C=0.01, epsilon=0.1))\n",
        "svm_poly_reg.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 182
        },
        "id": "60yjdoBr30gb",
        "outputId": "0dcd82bb-ee48-425f-e71f-81c1c5791f88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
              "                ('svr', SVR(C=0.01, kernel='poly'))])"
            ],
            "text/html": [
              "<style>#sk-container-id-15 {color: black;background-color: white;}#sk-container-id-15 pre{padding: 0;}#sk-container-id-15 div.sk-toggleable {background-color: white;}#sk-container-id-15 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-15 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-15 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-15 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-15 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-15 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-15 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-15 div.sk-item {position: relative;z-index: 1;}#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-15 div.sk-item::before, #sk-container-id-15 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-15 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-15 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-15 div.sk-label-container {text-align: center;}#sk-container-id-15 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-15 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;svr&#x27;, SVR(C=0.01, kernel=&#x27;poly&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-48\" type=\"checkbox\" ><label for=\"sk-estimator-id-48\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;svr&#x27;, SVR(C=0.01, kernel=&#x27;poly&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-49\" type=\"checkbox\" ><label for=\"sk-estimator-id-49\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-50\" type=\"checkbox\" ><label for=\"sk-estimator-id-50\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR(C=0.01, kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exercises\n",
        "\n",
        "1. Fundamental idea behind SVMs is that we try to limit margin violations while having as wide street as possible (soft margin classification).\n",
        "\n",
        "2. Support vectors are points which boundary of our street touches.\n",
        "\n",
        "3. It is imporatant to scale features when using SVM beacuse it's then easier to find nice decision boundary.\n",
        "\n",
        "4. SVM can output confidence score, it can output probability if **probability** parameter is set to true.\n",
        "\n",
        "5. LinearSVC and SGDClassifier scale better to big datasets, SVC supports kernel trick, they all require scaling, only SGDClassifier support out-of-core method.\n",
        "\n",
        "6. You should increase C and increase gamma.\n",
        "\n",
        "7. epsilon-insentitive means that new points inside margin to do not influence model predictions.\n",
        "\n",
        "8. The point of using the kernel trick is that we can increase our data degree without actually transforming it at all."
      ],
      "metadata": {
        "id": "CA42nwUl4k2_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exercise 9"
      ],
      "metadata": {
        "id": "p0fRrn5-RV-4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import datasets\n",
        "\n",
        "iris = datasets.load_iris(as_frame=True)\n",
        "X = iris.data[[\"petal length (cm)\", \"petal width (cm)\"]].values\n",
        "y = iris.target\n",
        "\n",
        "setosa_or_versicolor = (y == 0) | (y == 1)\n",
        "X = X[setosa_or_versicolor]\n",
        "y = y[setosa_or_versicolor]"
      ],
      "metadata": {
        "id": "CQ_PBy5u4uZH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's train each type of SVM classifier: SVC, SGDClassifier, LinearSVC\n",
        "from sklearn.svm import SVC, LinearSVC\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
        "svc_clf = SVC(kernel=\"linear\")\n",
        "linear_svc_clf = LinearSVC()\n",
        "sgd_clf = SGDClassifier(penalty=\"l2\", alpha=0.01)\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "svc_clf.fit(X_train, y_train)\n",
        "linear_svc_clf.fit(X_train, y_train)\n",
        "sgd_clf.fit(X_train, y_train);"
      ],
      "metadata": {
        "id": "Iy9nO_kfKMyi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"SVC = {svc_clf.score(X_test, y_test)}\")\n",
        "print(f\"Linear SVC = {linear_svc_clf.score(X_test, y_test)}\")\n",
        "print(f\"SGD = {sgd_clf.score(X_test, y_test)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Al3aGktWKiSm",
        "outputId": "fe761516-d17b-4ff6-9057-437c96e24d81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVC = 1.0\n",
            "Linear SVC = 1.0\n",
            "SGD = 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_decision_boundary(model):\n",
        "    w = -model.coef_[0, 0] / model.coef_[0, 1]\n",
        "    b = -model.intercept_[0] / model.coef_[0, 1]\n",
        "    return scaler.inverse_transform([[-10, -10 * w + b], [10, 10 * w + b]])\n",
        "\n",
        "lin_line = compute_decision_boundary(linear_svc_clf)\n",
        "svc_line = compute_decision_boundary(svc_clf)\n",
        "sgd_line = compute_decision_boundary(sgd_clf)\n",
        "\n",
        "# Plot all three decision boundaries\n",
        "plt.figure(figsize=(11, 4))\n",
        "plt.plot(lin_line[:, 0], lin_line[:, 1], \"k:\", label=\"LinearSVC\")\n",
        "plt.plot(svc_line[:, 0], svc_line[:, 1], \"b--\", linewidth=2, label=\"SVC\")\n",
        "plt.plot(sgd_line[:, 0], sgd_line[:, 1], \"r-\", label=\"SGDClassifier\")\n",
        "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\") # label=\"Iris versicolor\"\n",
        "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\") # label=\"Iris setosa\"\n",
        "plt.xlabel(\"Petal length\")\n",
        "plt.ylabel(\"Petal width\")\n",
        "plt.legend(loc=\"upper center\")\n",
        "plt.axis([0, 5.5, 0, 2])\n",
        "plt.grid()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "cyhQz2h_QFJY",
        "outputId": "ff97805a-17f0-4d1d-cf3a-52cbf4b56a2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x400 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exercise 10"
      ],
      "metadata": {
        "id": "S2HTSlaJQcgg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_wine\n",
        "\n",
        "wine_data = load_wine(as_frame=True)\n",
        "data = wine_data.data\n",
        "target = wine_data.target\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "qjOijB6CRZdk",
        "outputId": "3ca01b0e-5cc2-443a-a6b3-5e5c0f39dc99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
              "0    14.23        1.71  2.43               15.6      127.0           2.80   \n",
              "1    13.20        1.78  2.14               11.2      100.0           2.65   \n",
              "2    13.16        2.36  2.67               18.6      101.0           2.80   \n",
              "3    14.37        1.95  2.50               16.8      113.0           3.85   \n",
              "4    13.24        2.59  2.87               21.0      118.0           2.80   \n",
              "\n",
              "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
              "0        3.06                  0.28             2.29             5.64  1.04   \n",
              "1        2.76                  0.26             1.28             4.38  1.05   \n",
              "2        3.24                  0.30             2.81             5.68  1.03   \n",
              "3        3.49                  0.24             2.18             7.80  0.86   \n",
              "4        2.69                  0.39             1.82             4.32  1.04   \n",
              "\n",
              "   od280/od315_of_diluted_wines  proline  \n",
              "0                          3.92   1065.0  \n",
              "1                          3.40   1050.0  \n",
              "2                          3.17   1185.0  \n",
              "3                          3.45   1480.0  \n",
              "4                          2.93    735.0  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "      <th>proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.23</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.43</td>\n",
              "      <td>15.6</td>\n",
              "      <td>127.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.06</td>\n",
              "      <td>0.28</td>\n",
              "      <td>2.29</td>\n",
              "      <td>5.64</td>\n",
              "      <td>1.04</td>\n",
              "      <td>3.92</td>\n",
              "      <td>1065.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13.20</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.14</td>\n",
              "      <td>11.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>2.65</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.26</td>\n",
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              "      <td>4.38</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1050.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.16</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.67</td>\n",
              "      <td>18.6</td>\n",
              "      <td>101.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.68</td>\n",
              "      <td>1.03</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1185.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14.37</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2.50</td>\n",
              "      <td>16.8</td>\n",
              "      <td>113.0</td>\n",
              "      <td>3.85</td>\n",
              "      <td>3.49</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.18</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.45</td>\n",
              "      <td>1480.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.24</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.82</td>\n",
              "      <td>4.32</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.93</td>\n",
              "      <td>735.0</td>\n",
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              "      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-c07eda5e-b34b-470e-8ef3-2194592171f4 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-c07eda5e-b34b-470e-8ef3-2194592171f4');\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",
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              "        element.appendChild(docLink);\n",
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              "\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --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",
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              "      border-top-color: var(--fill-color);\n",
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              "      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",
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              "      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-16e7853f-014e-4858-90a4-d37e9348d6e5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 178,\n  \"fields\": [\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8118265380058575,\n        \"min\": 11.03,\n        \"max\": 14.83,\n        \"num_unique_values\": 126,\n        \"samples\": [\n          11.62,\n          13.64,\n          13.69\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"malic_acid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1171460976144627,\n        \"min\": 0.74,\n        \"max\": 5.8,\n        \"num_unique_values\": 133,\n        \"samples\": [\n          1.21,\n          2.83,\n          1.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ash\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.27434400906081485,\n        \"min\": 1.36,\n        \"max\": 3.23,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          2.31,\n          2.43,\n          2.52\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcalinity_of_ash\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.339563767173505,\n        \"min\": 10.6,\n        \"max\": 30.0,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          25.5,\n          28.5,\n          15.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"magnesium\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.282483515295665,\n        \"min\": 70.0,\n        \"max\": 162.0,\n        \"num_unique_values\": 53,\n        \"samples\": [\n          126.0,\n          85.0,\n          162.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_phenols\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6258510488339893,\n        \"min\": 0.98,\n        \"max\": 3.88,\n        \"num_unique_values\": 97,\n        \"samples\": [\n          1.68,\n          2.11,\n          1.35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flavanoids\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9988586850169467,\n        \"min\": 0.34,\n        \"max\": 5.08,\n        \"num_unique_values\": 132,\n        \"samples\": [\n          3.18,\n          2.5,\n          3.17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nonflavanoid_phenols\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12445334029667937,\n        \"min\": 0.13,\n        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  },\n    {\n      \"column\": \"hue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22857156582982338,\n        \"min\": 0.48,\n        \"max\": 1.71,\n        \"num_unique_values\": 78,\n        \"samples\": [\n          1.22,\n          1.04,\n          1.45\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"od280/od315_of_diluted_wines\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7099904287650504,\n        \"min\": 1.27,\n        \"max\": 4.0,\n        \"num_unique_values\": 122,\n        \"samples\": [\n          4.0,\n          1.82,\n          1.59\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"proline\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.9074742768491,\n        \"min\": 278.0,\n        \"max\": 1680.0,\n        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            }
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hg6t-U1ZRZgf",
        "outputId": "3c740511-cc48-42f6-beea-df1ea2b23d12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 12))\n",
        "data.hist(bins=30)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "csHZGCElRZjd",
        "outputId": "647f1d34-67b9-406b-b7dc-79e4897842cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.25, shuffle=True)\n",
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s8NAdIyTuphV",
        "outputId": "183f8c28-cd89-47f9-c998-e3112c89de52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((133, 13), (45, 13), (133,), (45,))"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = make_pipeline(StandardScaler(),\n",
        "                      SVC(C=1))\n",
        "model.fit(X_train, y_train)\n",
        "model.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yl1OZ8vIRZmQ",
        "outputId": "9d0c2726-57d7-45b3-ed96-d91d10609d53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9777777777777777"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "param_grid = {'svc__C': [0.1, 1, 10, 100, 1000],\n",
        "              'svc__gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
        "              'svc__kernel': ['rbf']}\n",
        "\n",
        "grid = GridSearchCV(model,\n",
        "                    param_grid,\n",
        "                    refit = True,\n",
        "                    verbose = 3)\n",
        "\n",
        "# fitting the model for grid search\n",
        "grid.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4dawAi90uw9z",
        "outputId": "8d38f7d8-26dc-4370-8ace-15da42174467"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n",
            "[CV 1/5] END svc__C=0.1, svc__gamma=1, svc__kernel=rbf;, score=0.407 total time=   0.0s\n",
            "[CV 2/5] END svc__C=0.1, svc__gamma=1, svc__kernel=rbf;, score=0.370 total time=   0.0s\n",
            "[CV 3/5] END svc__C=0.1, svc__gamma=1, svc__kernel=rbf;, score=0.407 total time=   0.0s\n",
            "[CV 4/5] END svc__C=0.1, svc__gamma=1, svc__kernel=rbf;, score=0.423 total time=   0.0s\n",
            "[CV 5/5] END svc__C=0.1, svc__gamma=1, svc__kernel=rbf;, score=0.423 total time=   0.0s\n",
            "[CV 1/5] END svc__C=0.1, svc__gamma=0.1, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 2/5] END svc__C=0.1, svc__gamma=0.1, svc__kernel=rbf;, score=0.926 total time=   0.0s\n",
            "[CV 3/5] END svc__C=0.1, svc__gamma=0.1, svc__kernel=rbf;, score=0.889 total time=   0.0s\n",
            "[CV 4/5] END svc__C=0.1, svc__gamma=0.1, svc__kernel=rbf;, score=0.962 total time=   0.0s\n",
            "[CV 5/5] END svc__C=0.1, svc__gamma=0.1, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 1/5] END svc__C=0.1, svc__gamma=0.01, svc__kernel=rbf;, score=0.407 total time=   0.0s\n",
            "[CV 2/5] END svc__C=0.1, svc__gamma=0.01, svc__kernel=rbf;, score=0.370 total time=   0.0s\n",
            "[CV 3/5] END svc__C=0.1, svc__gamma=0.01, svc__kernel=rbf;, score=0.407 total time=   0.0s\n",
            "[CV 4/5] END svc__C=0.1, svc__gamma=0.01, svc__kernel=rbf;, score=0.462 total time=   0.0s\n",
            "[CV 5/5] END svc__C=0.1, svc__gamma=0.01, svc__kernel=rbf;, score=0.462 total time=   0.0s\n",
            "[CV 1/5] END svc__C=0.1, svc__gamma=0.001, svc__kernel=rbf;, score=0.407 total time=   0.0s\n",
            "[CV 2/5] END svc__C=0.1, svc__gamma=0.001, svc__kernel=rbf;, score=0.370 total time=   0.0s\n",
            "[CV 3/5] END svc__C=0.1, svc__gamma=0.001, svc__kernel=rbf;, score=0.407 total time=   0.0s\n",
            "[CV 4/5] END svc__C=0.1, svc__gamma=0.001, svc__kernel=rbf;, score=0.423 total time=   0.0s\n",
            "[CV 5/5] END svc__C=0.1, svc__gamma=0.001, svc__kernel=rbf;, score=0.423 total time=   0.0s\n",
            "[CV 1/5] END svc__C=0.1, svc__gamma=0.0001, svc__kernel=rbf;, score=0.407 total time=   0.0s\n",
            "[CV 2/5] END svc__C=0.1, svc__gamma=0.0001, svc__kernel=rbf;, score=0.370 total time=   0.0s\n",
            "[CV 3/5] END svc__C=0.1, svc__gamma=0.0001, svc__kernel=rbf;, score=0.407 total time=   0.0s\n",
            "[CV 4/5] END svc__C=0.1, svc__gamma=0.0001, svc__kernel=rbf;, score=0.423 total time=   0.0s\n",
            "[CV 5/5] END svc__C=0.1, svc__gamma=0.0001, svc__kernel=rbf;, score=0.423 total time=   0.0s\n",
            "[CV 1/5] END svc__C=1, svc__gamma=1, svc__kernel=rbf;, score=0.481 total time=   0.0s\n",
            "[CV 2/5] END svc__C=1, svc__gamma=1, svc__kernel=rbf;, score=0.519 total time=   0.0s\n",
            "[CV 3/5] END svc__C=1, svc__gamma=1, svc__kernel=rbf;, score=0.481 total time=   0.0s\n",
            "[CV 4/5] END svc__C=1, svc__gamma=1, svc__kernel=rbf;, score=0.500 total time=   0.0s\n",
            "[CV 5/5] END svc__C=1, svc__gamma=1, svc__kernel=rbf;, score=0.538 total time=   0.0s\n",
            "[CV 1/5] END svc__C=1, svc__gamma=0.1, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 2/5] END svc__C=1, svc__gamma=0.1, svc__kernel=rbf;, score=0.963 total time=   0.0s\n",
            "[CV 3/5] END svc__C=1, svc__gamma=0.1, svc__kernel=rbf;, score=0.926 total time=   0.0s\n",
            "[CV 4/5] END svc__C=1, svc__gamma=0.1, svc__kernel=rbf;, score=0.962 total time=   0.0s\n",
            "[CV 5/5] END svc__C=1, svc__gamma=0.1, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 1/5] END svc__C=1, svc__gamma=0.01, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 2/5] END svc__C=1, svc__gamma=0.01, svc__kernel=rbf;, score=0.963 total time=   0.0s\n",
            "[CV 3/5] END svc__C=1, svc__gamma=0.01, svc__kernel=rbf;, score=0.963 total time=   0.0s\n",
            "[CV 4/5] END svc__C=1, svc__gamma=0.01, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 5/5] END svc__C=1, svc__gamma=0.01, svc__kernel=rbf;, score=0.962 total time=   0.0s\n",
            "[CV 1/5] END svc__C=1, svc__gamma=0.001, svc__kernel=rbf;, score=0.593 total time=   0.0s\n",
            "[CV 2/5] END svc__C=1, svc__gamma=0.001, svc__kernel=rbf;, score=0.407 total time=   0.0s\n",
            "[CV 3/5] END svc__C=1, svc__gamma=0.001, svc__kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END svc__C=1, svc__gamma=0.001, svc__kernel=rbf;, score=0.615 total time=   0.0s\n",
            "[CV 5/5] END svc__C=1, svc__gamma=0.001, svc__kernel=rbf;, score=0.731 total time=   0.0s\n",
            "[CV 1/5] END svc__C=1, svc__gamma=0.0001, svc__kernel=rbf;, score=0.407 total time=   0.0s\n",
            "[CV 2/5] END svc__C=1, svc__gamma=0.0001, svc__kernel=rbf;, score=0.370 total time=   0.0s\n",
            "[CV 3/5] END svc__C=1, svc__gamma=0.0001, svc__kernel=rbf;, score=0.407 total time=   0.0s\n",
            "[CV 4/5] END svc__C=1, svc__gamma=0.0001, svc__kernel=rbf;, score=0.423 total time=   0.0s\n",
            "[CV 5/5] END svc__C=1, svc__gamma=0.0001, svc__kernel=rbf;, score=0.423 total time=   0.0s\n",
            "[CV 1/5] END svc__C=10, svc__gamma=1, svc__kernel=rbf;, score=0.593 total time=   0.0s\n",
            "[CV 2/5] END svc__C=10, svc__gamma=1, svc__kernel=rbf;, score=0.630 total time=   0.0s\n",
            "[CV 3/5] END svc__C=10, svc__gamma=1, svc__kernel=rbf;, score=0.481 total time=   0.0s\n",
            "[CV 4/5] END svc__C=10, svc__gamma=1, svc__kernel=rbf;, score=0.500 total time=   0.0s\n",
            "[CV 5/5] END svc__C=10, svc__gamma=1, svc__kernel=rbf;, score=0.654 total time=   0.1s\n",
            "[CV 1/5] END svc__C=10, svc__gamma=0.1, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 2/5] END svc__C=10, svc__gamma=0.1, svc__kernel=rbf;, score=0.963 total time=   0.0s\n",
            "[CV 3/5] END svc__C=10, svc__gamma=0.1, svc__kernel=rbf;, score=0.926 total time=   0.0s\n",
            "[CV 4/5] END svc__C=10, svc__gamma=0.1, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 5/5] END svc__C=10, svc__gamma=0.1, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 1/5] END svc__C=10, svc__gamma=0.01, svc__kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 2/5] END svc__C=10, svc__gamma=0.01, svc__kernel=rbf;, score=0.963 total time=   0.0s\n",
            "[CV 3/5] END svc__C=10, svc__gamma=0.01, svc__kernel=rbf;, score=0.926 total time=   0.0s\n",
            "[CV 4/5] END svc__C=10, svc__gamma=0.01, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 5/5] END svc__C=10, svc__gamma=0.01, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 1/5] END svc__C=10, svc__gamma=0.001, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 2/5] END svc__C=10, svc__gamma=0.001, svc__kernel=rbf;, score=0.963 total time=   0.0s\n",
            "[CV 3/5] END svc__C=10, svc__gamma=0.001, svc__kernel=rbf;, score=0.926 total time=   0.0s\n",
            "[CV 4/5] END svc__C=10, svc__gamma=0.001, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 5/5] END svc__C=10, svc__gamma=0.001, svc__kernel=rbf;, score=0.962 total time=   0.0s\n",
            "[CV 1/5] END svc__C=10, svc__gamma=0.0001, svc__kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 2/5] END svc__C=10, svc__gamma=0.0001, svc__kernel=rbf;, score=0.444 total time=   0.0s\n",
            "[CV 3/5] END svc__C=10, svc__gamma=0.0001, svc__kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END svc__C=10, svc__gamma=0.0001, svc__kernel=rbf;, score=0.615 total time=   0.0s\n",
            "[CV 5/5] END svc__C=10, svc__gamma=0.0001, svc__kernel=rbf;, score=0.731 total time=   0.0s\n",
            "[CV 1/5] END svc__C=100, svc__gamma=1, svc__kernel=rbf;, score=0.593 total time=   0.0s\n",
            "[CV 2/5] END svc__C=100, svc__gamma=1, svc__kernel=rbf;, score=0.630 total time=   0.0s\n",
            "[CV 3/5] END svc__C=100, svc__gamma=1, svc__kernel=rbf;, score=0.481 total time=   0.0s\n",
            "[CV 4/5] END svc__C=100, svc__gamma=1, svc__kernel=rbf;, score=0.500 total time=   0.1s\n",
            "[CV 5/5] END svc__C=100, svc__gamma=1, svc__kernel=rbf;, score=0.654 total time=   0.0s\n",
            "[CV 1/5] END svc__C=100, svc__gamma=0.1, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 2/5] END svc__C=100, svc__gamma=0.1, svc__kernel=rbf;, score=0.963 total time=   0.0s\n",
            "[CV 3/5] END svc__C=100, svc__gamma=0.1, svc__kernel=rbf;, score=0.926 total time=   0.0s\n",
            "[CV 4/5] END svc__C=100, svc__gamma=0.1, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 5/5] END svc__C=100, svc__gamma=0.1, svc__kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END svc__C=100, svc__gamma=0.01, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 2/5] END svc__C=100, svc__gamma=0.01, svc__kernel=rbf;, score=0.963 total time=   0.0s\n",
            "[CV 3/5] END svc__C=100, svc__gamma=0.01, svc__kernel=rbf;, score=0.926 total time=   0.0s\n",
            "[CV 4/5] END svc__C=100, svc__gamma=0.01, svc__kernel=rbf;, score=0.962 total time=   0.0s\n",
            "[CV 5/5] END svc__C=100, svc__gamma=0.01, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 1/5] END svc__C=100, svc__gamma=0.001, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 2/5] END svc__C=100, svc__gamma=0.001, svc__kernel=rbf;, score=0.963 total time=   0.0s\n",
            "[CV 3/5] END svc__C=100, svc__gamma=0.001, svc__kernel=rbf;, score=0.926 total time=   0.0s\n",
            "[CV 4/5] END svc__C=100, svc__gamma=0.001, svc__kernel=rbf;, score=0.962 total time=   0.0s\n",
            "[CV 5/5] END svc__C=100, svc__gamma=0.001, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 1/5] END svc__C=100, svc__gamma=0.0001, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 2/5] END svc__C=100, svc__gamma=0.0001, svc__kernel=rbf;, score=0.963 total time=   0.0s\n",
            "[CV 3/5] END svc__C=100, svc__gamma=0.0001, svc__kernel=rbf;, score=0.926 total time=   0.0s\n",
            "[CV 4/5] END svc__C=100, svc__gamma=0.0001, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 5/5] END svc__C=100, svc__gamma=0.0001, svc__kernel=rbf;, score=0.962 total time=   0.0s\n",
            "[CV 1/5] END svc__C=1000, svc__gamma=1, svc__kernel=rbf;, score=0.593 total time=   0.0s\n",
            "[CV 2/5] END svc__C=1000, svc__gamma=1, svc__kernel=rbf;, score=0.630 total time=   0.0s\n",
            "[CV 3/5] END svc__C=1000, svc__gamma=1, svc__kernel=rbf;, score=0.481 total time=   0.0s\n",
            "[CV 4/5] END svc__C=1000, svc__gamma=1, svc__kernel=rbf;, score=0.500 total time=   0.0s\n",
            "[CV 5/5] END svc__C=1000, svc__gamma=1, svc__kernel=rbf;, score=0.654 total time=   0.0s\n",
            "[CV 1/5] END svc__C=1000, svc__gamma=0.1, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 2/5] END svc__C=1000, svc__gamma=0.1, svc__kernel=rbf;, score=0.963 total time=   0.0s\n",
            "[CV 3/5] END svc__C=1000, svc__gamma=0.1, svc__kernel=rbf;, score=0.926 total time=   0.0s\n",
            "[CV 4/5] END svc__C=1000, svc__gamma=0.1, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 5/5] END svc__C=1000, svc__gamma=0.1, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 1/5] END svc__C=1000, svc__gamma=0.01, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 2/5] END svc__C=1000, svc__gamma=0.01, svc__kernel=rbf;, score=0.963 total time=   0.0s\n",
            "[CV 3/5] END svc__C=1000, svc__gamma=0.01, svc__kernel=rbf;, score=0.926 total time=   0.0s\n",
            "[CV 4/5] END svc__C=1000, svc__gamma=0.01, svc__kernel=rbf;, score=0.962 total time=   0.0s\n",
            "[CV 5/5] END svc__C=1000, svc__gamma=0.01, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 1/5] END svc__C=1000, svc__gamma=0.001, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 2/5] END svc__C=1000, svc__gamma=0.001, svc__kernel=rbf;, score=0.963 total time=   0.0s\n",
            "[CV 3/5] END svc__C=1000, svc__gamma=0.001, svc__kernel=rbf;, score=0.889 total time=   0.0s\n",
            "[CV 4/5] END svc__C=1000, svc__gamma=0.001, svc__kernel=rbf;, score=0.962 total time=   0.0s\n",
            "[CV 5/5] END svc__C=1000, svc__gamma=0.001, svc__kernel=rbf;, score=0.962 total time=   0.0s\n",
            "[CV 1/5] END svc__C=1000, svc__gamma=0.0001, svc__kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 2/5] END svc__C=1000, svc__gamma=0.0001, svc__kernel=rbf;, score=0.963 total time=   0.0s\n",
            "[CV 3/5] END svc__C=1000, svc__gamma=0.0001, svc__kernel=rbf;, score=0.926 total time=   0.0s\n",
            "[CV 4/5] END svc__C=1000, svc__gamma=0.0001, svc__kernel=rbf;, score=0.962 total time=   0.0s\n",
            "[CV 5/5] END svc__C=1000, svc__gamma=0.0001, svc__kernel=rbf;, score=1.000 total time=   0.0s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
              "                                       ('svc', SVC(C=1))]),\n",
              "             param_grid={'svc__C': [0.1, 1, 10, 100, 1000],\n",
              "                         'svc__gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
              "                         'svc__kernel': ['rbf']},\n",
              "             verbose=3)"
            ],
            "text/html": [
              "<style>#sk-container-id-16 {color: black;background-color: white;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-16 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-16 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                                       (&#x27;svc&#x27;, SVC(C=1))]),\n",
              "             param_grid={&#x27;svc__C&#x27;: [0.1, 1, 10, 100, 1000],\n",
              "                         &#x27;svc__gamma&#x27;: [1, 0.1, 0.01, 0.001, 0.0001],\n",
              "                         &#x27;svc__kernel&#x27;: [&#x27;rbf&#x27;]},\n",
              "             verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-51\" type=\"checkbox\" ><label for=\"sk-estimator-id-51\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                                       (&#x27;svc&#x27;, SVC(C=1))]),\n",
              "             param_grid={&#x27;svc__C&#x27;: [0.1, 1, 10, 100, 1000],\n",
              "                         &#x27;svc__gamma&#x27;: [1, 0.1, 0.01, 0.001, 0.0001],\n",
              "                         &#x27;svc__kernel&#x27;: [&#x27;rbf&#x27;]},\n",
              "             verbose=3)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-52\" type=\"checkbox\" ><label for=\"sk-estimator-id-52\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()), (&#x27;svc&#x27;, SVC(C=1))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-53\" type=\"checkbox\" ><label for=\"sk-estimator-id-53\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-54\" type=\"checkbox\" ><label for=\"sk-estimator-id-54\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=1)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5kLHovBBvVeD",
        "outputId": "4d27b481-1a0e-45b3-f997-09811d1148d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'svc__C': 10, 'svc__gamma': 0.1, 'svc__kernel': 'rbf'}"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N9adMrpDv2Tt",
        "outputId": "b7cbf7cb-4339-4fd9-b798-7a5bd8d0f980"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "71     1\n",
              "81     1\n",
              "153    2\n",
              "111    1\n",
              "120    1\n",
              "51     0\n",
              "149    2\n",
              "96     1\n",
              "59     1\n",
              "176    2\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "\n",
        "best_SVC = grid.best_estimator_\n",
        "svc_predictions = best_SVC.predict(X_test)\n",
        "print(f\"F1 = {f1_score(y_true=y_test, y_pred=svc_predictions, average='weighted')}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Aw4lVVLTvmKW",
        "outputId": "6fafdf95-d401-46f9-f518-ebf632bc423b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F1 = 0.9775661375661375\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exercise 11"
      ],
      "metadata": {
        "id": "jNj2_pgZvcp2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import fetch_california_housing\n",
        "\n",
        "housing = fetch_california_housing(as_frame=True)\n",
        "data = housing.data\n",
        "target = housing.target\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UDxItn_iwewu",
        "outputId": "613f7b40-8cf7-48ae-e8f0-99062ce7f7d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
              "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
              "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
              "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
              "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
              "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
              "\n",
              "   Longitude  \n",
              "0    -122.23  \n",
              "1    -122.22  \n",
              "2    -122.24  \n",
              "3    -122.25  \n",
              "4    -122.25  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0ad9743f-70f2-4c54-85f0-c4c6a94417bb\" 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>MedInc</th>\n",
              "      <th>HouseAge</th>\n",
              "      <th>AveRooms</th>\n",
              "      <th>AveBedrms</th>\n",
              "      <th>Population</th>\n",
              "      <th>AveOccup</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.3252</td>\n",
              "      <td>41.0</td>\n",
              "      <td>6.984127</td>\n",
              "      <td>1.023810</td>\n",
              "      <td>322.0</td>\n",
              "      <td>2.555556</td>\n",
              "      <td>37.88</td>\n",
              "      <td>-122.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.3014</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6.238137</td>\n",
              "      <td>0.971880</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>2.109842</td>\n",
              "      <td>37.86</td>\n",
              "      <td>-122.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.2574</td>\n",
              "      <td>52.0</td>\n",
              "      <td>8.288136</td>\n",
              "      <td>1.073446</td>\n",
              "      <td>496.0</td>\n",
              "      <td>2.802260</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.6431</td>\n",
              "      <td>52.0</td>\n",
              "      <td>5.817352</td>\n",
              "      <td>1.073059</td>\n",
              "      <td>558.0</td>\n",
              "      <td>2.547945</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.8462</td>\n",
              "      <td>52.0</td>\n",
              "      <td>6.281853</td>\n",
              "      <td>1.081081</td>\n",
              "      <td>565.0</td>\n",
              "      <td>2.181467</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0ad9743f-70f2-4c54-85f0-c4c6a94417bb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -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-0ad9743f-70f2-4c54-85f0-c4c6a94417bb 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-0ad9743f-70f2-4c54-85f0-c4c6a94417bb');\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-ea3548f7-9ba1-4b2a-a3ab-aaee48246887\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ea3548f7-9ba1-4b2a-a3ab-aaee48246887')\"\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-ea3548f7-9ba1-4b2a-a3ab-aaee48246887 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 20640,\n  \"fields\": [\n    {\n      \"column\": \"MedInc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8998217179452688,\n        \"min\": 0.4999,\n        \"max\": 15.0001,\n        \"num_unique_values\": 12928,\n        \"samples\": [\n          5.0286,\n          2.0433,\n          6.1228\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HouseAge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.58555761211165,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          35.0,\n          25.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveRooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4741731394243187,\n        \"min\": 0.8461538461538461,\n        \"max\": 141.9090909090909,\n        \"num_unique_values\": 19392,\n        \"samples\": [\n          6.111269614835948,\n          5.912820512820513,\n          5.7924528301886795\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveBedrms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.473910856795466,\n        \"min\": 0.3333333333333333,\n        \"max\": 34.06666666666667,\n        \"num_unique_values\": 14233,\n        \"samples\": [\n          0.9906542056074766,\n          1.112099644128114,\n          1.0398230088495575\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1132.462121765341,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 3888,\n        \"samples\": [\n          4169.0,\n          636.0,\n          3367.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveOccup\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.386049562213618,\n        \"min\": 0.6923076923076923,\n        \"max\": 1243.3333333333333,\n        \"num_unique_values\": 18841,\n        \"samples\": [\n          2.6939799331103678,\n          3.559375,\n          3.297082228116711\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1359523974571153,\n        \"min\": 32.54,\n        \"max\": 41.95,\n        \"num_unique_values\": 862,\n        \"samples\": [\n          33.7,\n          34.41,\n          38.24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0035317235025882,\n        \"min\": -124.35,\n        \"max\": -114.31,\n        \"num_unique_values\": 844,\n        \"samples\": [\n          -118.63,\n          -119.86,\n          -121.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rdg0dgXbwezW",
        "outputId": "d15410b4-503f-4e40-96bb-730bc720d52e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    4.526\n",
              "1    3.585\n",
              "2    3.521\n",
              "3    3.413\n",
              "4    3.422\n",
              "5    2.697\n",
              "6    2.992\n",
              "7    2.414\n",
              "8    2.267\n",
              "9    2.611\n",
              "Name: MedHouseVal, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.hist(bins=50)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "fCnWTDYywe2A",
        "outputId": "43e83d70-b080-4f03-a5f7-af53c4920a5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's create some additional features\n",
        "data[\"people_per_room\"] = data[\"Population\"] / data[\"AveRooms\"]\n",
        "data[\"bedrooms_per_people\"] = data[\"AveBedrms\"] / data[\"AveRooms\"]"
      ],
      "metadata": {
        "id": "NfSSsKRx9mFi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "temp_data = data.copy()\n",
        "temp_data[\"HousePrice\"] = target.copy()\n",
        "temp_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "tdxYB7LD4maJ",
        "outputId": "7db3957d-3932-4341-9884-d9b501daaf10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
              "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
              "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
              "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
              "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
              "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
              "\n",
              "   Longitude  people_per_room  bedrooms_per_people  HousePrice  \n",
              "0    -122.23        46.104545             0.146591       4.526  \n",
              "1    -122.22       384.890548             0.155797       3.585  \n",
              "2    -122.24        59.844581             0.129516       3.521  \n",
              "3    -122.25        95.919937             0.184458       3.413  \n",
              "4    -122.25        89.941610             0.172096       3.422  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MedInc</th>\n",
              "      <th>HouseAge</th>\n",
              "      <th>AveRooms</th>\n",
              "      <th>AveBedrms</th>\n",
              "      <th>Population</th>\n",
              "      <th>AveOccup</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.3252</td>\n",
              "      <td>41.0</td>\n",
              "      <td>6.984127</td>\n",
              "      <td>1.023810</td>\n",
              "      <td>322.0</td>\n",
              "      <td>2.555556</td>\n",
              "      <td>37.88</td>\n",
              "      <td>-122.23</td>\n",
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              "      <td>4.526</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.3014</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6.238137</td>\n",
              "      <td>0.971880</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>2.109842</td>\n",
              "      <td>37.86</td>\n",
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              "      <td>0.155797</td>\n",
              "      <td>3.585</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.2574</td>\n",
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              "      <td>1.073446</td>\n",
              "      <td>496.0</td>\n",
              "      <td>2.802260</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.24</td>\n",
              "      <td>59.844581</td>\n",
              "      <td>0.129516</td>\n",
              "      <td>3.521</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.6431</td>\n",
              "      <td>52.0</td>\n",
              "      <td>5.817352</td>\n",
              "      <td>1.073059</td>\n",
              "      <td>558.0</td>\n",
              "      <td>2.547945</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "      <td>95.919937</td>\n",
              "      <td>0.184458</td>\n",
              "      <td>3.413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.8462</td>\n",
              "      <td>52.0</td>\n",
              "      <td>6.281853</td>\n",
              "      <td>1.081081</td>\n",
              "      <td>565.0</td>\n",
              "      <td>2.181467</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "      <td>89.941610</td>\n",
              "      <td>0.172096</td>\n",
              "      <td>3.422</td>\n",
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              "\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-97a1fe22-8e2c-425e-a159-bc58132053a7 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-97a1fe22-8e2c-425e-a159-bc58132053a7');\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",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a96fff84-00e5-4934-ab96-5126b47c9358\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a96fff84-00e5-4934-ab96-5126b47c9358')\"\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",
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              "    </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-a96fff84-00e5-4934-ab96-5126b47c9358 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "temp_data",
              "summary": "{\n  \"name\": \"temp_data\",\n  \"rows\": 20640,\n  \"fields\": [\n    {\n      \"column\": \"MedInc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8998217179452688,\n        \"min\": 0.4999,\n        \"max\": 15.0001,\n        \"num_unique_values\": 12928,\n        \"samples\": [\n          5.0286,\n          2.0433,\n          6.1228\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HouseAge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.58555761211165,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          35.0,\n          25.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveRooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4741731394243187,\n        \"min\": 0.8461538461538461,\n        \"max\": 141.9090909090909,\n        \"num_unique_values\": 19392,\n        \"samples\": [\n          6.111269614835948,\n          5.912820512820513,\n          5.7924528301886795\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveBedrms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.473910856795466,\n        \"min\": 0.3333333333333333,\n        \"max\": 34.06666666666667,\n        \"num_unique_values\": 14233,\n        \"samples\": [\n          0.9906542056074766,\n          1.112099644128114,\n          1.0398230088495575\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1132.462121765341,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 3888,\n        \"samples\": [\n          4169.0,\n          636.0,\n          3367.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveOccup\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.386049562213618,\n        \"min\": 0.6923076923076923,\n        \"max\": 1243.3333333333333,\n        \"num_unique_values\": 18841,\n        \"samples\": [\n          2.6939799331103678,\n          3.559375,\n          3.297082228116711\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1359523974571153,\n        \"min\": 32.54,\n        \"max\": 41.95,\n        \"num_unique_values\": 862,\n        \"samples\": [\n          33.7,\n          34.41,\n          38.24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0035317235025882,\n        \"min\": -124.35,\n        \"max\": -114.31,\n        \"num_unique_values\": 844,\n        \"samples\": [\n          -118.63,\n          -119.86,\n          -121.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"people_per_room\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 247.14281588883057,\n        \"min\": 0.2114029468289558,\n        \"max\": 6770.139566341754,\n        \"num_unique_values\": 20621,\n        \"samples\": [\n          311.1404958677686,\n          541.330516287646,\n          339.76190476190476\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms_per_people\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.058023490093062645,\n        \"min\": 0.1,\n        \"max\": 1.0,\n        \"num_unique_values\": 19931,\n        \"samples\": [\n          0.21724137931034485,\n          0.21323529411764708,\n          0.25029515938606844\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HousePrice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1539561587441387,\n        \"min\": 0.14999,\n        \"max\": 5.00001,\n        \"num_unique_values\": 3842,\n        \"samples\": [\n          1.943,\n          3.79,\n          2.301\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temp_data.corr()[\"HousePrice\"].sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mwKGs5rt4xQg",
        "outputId": "d37f9679-8327-4e58-827d-e84546d45353"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "HousePrice             1.000000\n",
              "MedInc                 0.688075\n",
              "AveRooms               0.151948\n",
              "HouseAge               0.105623\n",
              "AveOccup              -0.023737\n",
              "Population            -0.024650\n",
              "Longitude             -0.045967\n",
              "AveBedrms             -0.046701\n",
              "people_per_room       -0.089313\n",
              "Latitude              -0.144160\n",
              "bedrooms_per_people   -0.255624\n",
              "Name: HousePrice, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that `MedInc` is relevant to our price, we will use stratified sampling to ensure that each set conatains same amount of these values."
      ],
      "metadata": {
        "id": "O9EluZdN9aZX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(data[\"MedInc\"], bins=50);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "YtKsyFcn-ChI",
        "outputId": "e1081e44-9382-4679-c2e3-9c8522280434"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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xCDAAAMA4BBgAAGAcAgwAADAOAQYAABjnvANMTU2NZs2aJbfbLZvNps2bNwf0+/1+LVu2TKNHj1ZsbKyysrJ06NChgDHt7e3Kz8+X3W5XfHy8CgsL1dnZGTBmz549uuaaaxQTE6Pk5GStXLny/GcHAAAGpfMOMMeOHdPkyZO1evXqs/avXLlS5eXlqqioUF1dneLi4pSdna3jx49bY/Lz87V//35VVVWpsrJSNTU1mjdvntXv8/k0Y8YMjRkzRg0NDXrqqaf08MMPa+3atd9iigAAYLCx+f1+/7fe2WbTpk2bNHv2bElfrb643W7dd999uv/++yVJXq9XTqdT69atU15eng4cOKC0tDTV19crPT1dkrR161bdeOON+uSTT+R2u7VmzRo98MAD8ng8ioqKkiSVlpZq8+bNOnjwYJ9q8/l8cjgc8nq9stvt33aKxhhb+lq4SzDexytywl0CAAx5ff38Duo1MIcPH5bH41FWVpbV5nA4lJGRodraWklSbW2t4uPjrfAiSVlZWYqIiFBdXZ015tprr7XCiyRlZ2erqalJn3/++VnP3dXVJZ/PF7ABAIDBKagBxuPxSJKcTmdAu9PptPo8Ho+SkpIC+iMjI5WQkBAw5mzH+PtznK6srEwOh8PakpOTv/uEAADAgDRo7kJasmSJvF6vtbW0tIS7JAAAECJBDTAul0uS1NraGtDe2tpq9blcLrW1tQX0nzx5Uu3t7QFjznaMvz/H6aKjo2W32wM2AAAwOEUG82CpqalyuVyqrq7WZZddJumri3Hq6up09913S5IyMzPV0dGhhoYGTZkyRZK0bds29fb2KiMjwxrzwAMPqLu7W8OHD5ckVVVVady4cRo5cmQwSwYsfbkQmgt9AWBgOO8VmM7OTjU2NqqxsVHSVxfuNjY2qrm5WTabTQsWLNBjjz2mV199VXv37tUdd9wht9tt3ak0YcIEzZw5U3PnztXOnTv13nvvqbi4WHl5eXK73ZKk22+/XVFRUSosLNT+/fv18ssv69lnn1VJSUnQJg4AAMx13iswu3bt0vXXX2+9PhUqCgoKtG7dOv3iF7/QsWPHNG/ePHV0dGjatGnaunWrYmJirH02bNig4uJiTZ8+XREREcrNzVV5ebnV73A49Oabb6qoqEhTpkzRqFGjtGzZsoBnxQAAgKHrOz0HZiDjOTAIBb5CAoDQCstzYAAAAPoDAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYJyg/hYSMNjxe0kAMDCwAgMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABgn6AGmp6dHS5cuVWpqqmJjY/X9739fjz76qPx+vzXG7/dr2bJlGj16tGJjY5WVlaVDhw4FHKe9vV35+fmy2+2Kj49XYWGhOjs7g10uAAAwUGSwD/jkk09qzZo1Wr9+vSZOnKhdu3bpzjvvlMPh0Pz58yVJK1euVHl5udavX6/U1FQtXbpU2dnZ+uCDDxQTEyNJys/P1//93/+pqqpK3d3duvPOOzVv3jxt3Lgx2CUDQTW29LVzjvl4RU4/VAIAg5fN//dLI0Fw0003yel06je/+Y3Vlpubq9jYWP3ud7+T3++X2+3Wfffdp/vvv1+S5PV65XQ6tW7dOuXl5enAgQNKS0tTfX290tPTJUlbt27VjTfeqE8++URut/ucdfh8PjkcDnm9Xtnt9mBOcUDqy4cmBg4CDACcXV8/v4P+FdLVV1+t6upqffjhh5Kkv/zlL3r33Xd1ww03SJIOHz4sj8ejrKwsax+Hw6GMjAzV1tZKkmpraxUfH2+FF0nKyspSRESE6urqznrerq4u+Xy+gA0AAAxOQf8KqbS0VD6fT+PHj9ewYcPU09Ojxx9/XPn5+ZIkj8cjSXI6nQH7OZ1Oq8/j8SgpKSmw0MhIJSQkWGNOV1ZWpuXLlwd7OgAAYAAK+grM73//e23YsEEbN27U7t27tX79ev3yl7/U+vXrg32qAEuWLJHX67W2lpaWkJ4PAACET9BXYBYtWqTS0lLl5eVJkiZNmqQjR46orKxMBQUFcrlckqTW1laNHj3a2q+1tVWXXXaZJMnlcqmtrS3guCdPnlR7e7u1/+mio6MVHR0d7OkAAIABKOgrMF9++aUiIgIPO2zYMPX29kqSUlNT5XK5VF1dbfX7fD7V1dUpMzNTkpSZmamOjg41NDRYY7Zt26be3l5lZGQEu2QAAGCYoK/AzJo1S48//rhSUlI0ceJE/fnPf9bTTz+tu+66S5Jks9m0YMECPfbYY7r44out26jdbrdmz54tSZowYYJmzpypuXPnqqKiQt3d3SouLlZeXl6f7kACAACDW9ADzHPPPaelS5fq3//939XW1ia3261/+7d/07Jly6wxv/jFL3Ts2DHNmzdPHR0dmjZtmrZu3Wo9A0aSNmzYoOLiYk2fPl0RERHKzc1VeXl5sMsFAAAGCvpzYAYKngODgYznwADA2YXtOTAAAAChRoABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHGC/mvUAM6tLz++yQ8+AsDXYwUGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcngNjgL48MwQAgKGEFRgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGCckAebTTz/Vz372MyUmJio2NlaTJk3Srl27rH6/369ly5Zp9OjRio2NVVZWlg4dOhRwjPb2duXn58tutys+Pl6FhYXq7OwMRbkAAMAwQQ8wn3/+uaZOnarhw4fr9ddf1wcffKBf/epXGjlypDVm5cqVKi8vV0VFherq6hQXF6fs7GwdP37cGpOfn6/9+/erqqpKlZWVqqmp0bx584JdLgAAMJDN7/f7g3nA0tJSvffee3rnnXfO2u/3++V2u3Xffffp/vvvlyR5vV45nU6tW7dOeXl5OnDggNLS0lRfX6/09HRJ0tatW3XjjTfqk08+kdvtPmcdPp9PDodDXq9Xdrs9eBMMA36Nemj6eEVOuEsAgH7X18/voK/AvPrqq0pPT9ePf/xjJSUl6fLLL9cLL7xg9R8+fFgej0dZWVlWm8PhUEZGhmprayVJtbW1io+Pt8KLJGVlZSkiIkJ1dXXBLhkAABgm6AHmr3/9q9asWaOLL75Yb7zxhu6++27Nnz9f69evlyR5PB5JktPpDNjP6XRafR6PR0lJSQH9kZGRSkhIsMacrqurSz6fL2ADAACDU2SwD9jb26v09HQ98cQTkqTLL79c+/btU0VFhQoKCoJ9OktZWZmWL18esuMDAICBI+grMKNHj1ZaWlpA24QJE9Tc3CxJcrlckqTW1taAMa2trVafy+VSW1tbQP/JkyfV3t5ujTndkiVL5PV6ra2lpSUo8wEAAANP0APM1KlT1dTUFND24YcfasyYMZKk1NRUuVwuVVdXW/0+n091dXXKzMyUJGVmZqqjo0MNDQ3WmG3btqm3t1cZGRlnPW90dLTsdnvABgAABqegf4W0cOFCXX311XriiSd02223aefOnVq7dq3Wrl0rSbLZbFqwYIEee+wxXXzxxUpNTdXSpUvldrs1e/ZsSV+t2MycOVNz585VRUWFuru7VVxcrLy8vD7dgQQAAAa3oN9GLUmVlZVasmSJDh06pNTUVJWUlGju3LlWv9/v10MPPaS1a9eqo6ND06ZN0/PPP68f/OAH1pj29nYVFxdry5YtioiIUG5ursrLy3XhhRf2qQZuo8ZQwK3WAAabvn5+hyTADAQEGAwFBBgAg03YngMDAAAQagQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAME5kuAsAEFpjS18755iPV+T0QyUAEDyswAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwTsgDzIoVK2Sz2bRgwQKr7fjx4yoqKlJiYqIuvPBC5ebmqrW1NWC/5uZm5eTk6IILLlBSUpIWLVqkkydPhrpcAABggJAGmPr6ev3617/WpZdeGtC+cOFCbdmyRa+88oq2b9+uo0eP6pZbbrH6e3p6lJOToxMnTuj999/X+vXrtW7dOi1btiyU5QIAAEOELMB0dnYqPz9fL7zwgkaOHGm1e71e/eY3v9HTTz+tf/7nf9aUKVP04osv6v3339eOHTskSW+++aY++OAD/e53v9Nll12mG264QY8++qhWr16tEydOhKpkAABgiJAFmKKiIuXk5CgrKyugvaGhQd3d3QHt48ePV0pKimprayVJtbW1mjRpkpxOpzUmOztbPp9P+/fvP+v5urq65PP5AjYAADA4RYbioC+99JJ2796t+vr6M/o8Ho+ioqIUHx8f0O50OuXxeKwxfx9eTvWf6jubsrIyLV++PAjVAwCAgS7oAaalpUX33nuvqqqqFBMTE+zDf60lS5aopKTEeu3z+ZScnNxv5wfCYWzpa+EuAQDCIuhfITU0NKitrU0//OEPFRkZqcjISG3fvl3l5eWKjIyU0+nUiRMn1NHREbBfa2urXC6XJMnlcp1xV9Kp16fGnC46Olp2uz1gAwAAg1PQA8z06dO1d+9eNTY2Wlt6erry8/Otfx4+fLiqq6utfZqamtTc3KzMzExJUmZmpvbu3au2tjZrTFVVlex2u9LS0oJdMgAAMEzQv0IaMWKELrnkkoC2uLg4JSYmWu2FhYUqKSlRQkKC7Ha77rnnHmVmZuqqq66SJM2YMUNpaWmaM2eOVq5cKY/HowcffFBFRUWKjo4OdskAAMAwIbmI91yeeeYZRUREKDc3V11dXcrOztbzzz9v9Q8bNkyVlZW6++67lZmZqbi4OBUUFOiRRx4JR7kAAGCAsfn9fn+4iwgFn88nh8Mhr9dr/PUwXKiJUPt4RU64SwAASX3//Oa3kAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGCcst1Hj/+MOIwAAzh8rMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4/AcGAB9eh4Rv1gNYCBhBQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHG4CwlAn3CnEoCBhBUYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYJeoApKyvTFVdcoREjRigpKUmzZ89WU1NTwJjjx4+rqKhIiYmJuvDCC5Wbm6vW1taAMc3NzcrJydEFF1ygpKQkLVq0SCdPngx2uQAAwEBBDzDbt29XUVGRduzYoaqqKnV3d2vGjBk6duyYNWbhwoXasmWLXnnlFW3fvl1Hjx7VLbfcYvX39PQoJydHJ06c0Pvvv6/169dr3bp1WrZsWbDLBQAABrL5/X5/KE/w2WefKSkpSdu3b9e1114rr9er733ve9q4caNuvfVWSdLBgwc1YcIE1dbW6qqrrtLrr7+um266SUePHpXT6ZQkVVRUaPHixfrss88UFRV1zvP6fD45HA55vV7Z7fZQTvE7GVv6WrhLAILm4xU54S4BgOH6+vkd8mtgvF6vJCkhIUGS1NDQoO7ubmVlZVljxo8fr5SUFNXW1kqSamtrNWnSJCu8SFJ2drZ8Pp/2799/1vN0dXXJ5/MFbAAAYHAKaYDp7e3VggULNHXqVF1yySWSJI/Ho6ioKMXHxweMdTqd8ng81pi/Dy+n+k/1nU1ZWZkcDoe1JScnB3k2AABgoAhpgCkqKtK+ffv00ksvhfI0kqQlS5bI6/VaW0tLS8jPCQAAwiMyVAcuLi5WZWWlampqdNFFF1ntLpdLJ06cUEdHR8AqTGtrq1wulzVm586dAcc7dZfSqTGni46OVnR0dJBnAQAABqKgr8D4/X4VFxdr06ZN2rZtm1JTUwP6p0yZouHDh6u6utpqa2pqUnNzszIzMyVJmZmZ2rt3r9ra2qwxVVVVstvtSktLC3bJAADAMEFfgSkqKtLGjRv1xz/+USNGjLCuWXE4HIqNjZXD4VBhYaFKSkqUkJAgu92ue+65R5mZmbrqqqskSTNmzFBaWprmzJmjlStXyuPx6MEHH1RRURGrLAAAIPgBZs2aNZKk6667LqD9xRdf1L/+679Kkp555hlFREQoNzdXXV1dys7O1vPPP2+NHTZsmCorK3X33XcrMzNTcXFxKigo0COPPBLscgEAgIFC/hyYcOE5MED/4zkwAL6rvn5+h+wiXgBDT18COSEHQDAQYEKI1RUAAEKDX6MGAADGYQUGwIDDV1EAzoUAA6Bf8dUqgGDgKyQAAGAcVmAAGImvmYChjRUYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjcBs1gEGLW62BwYsVGAAAYBwCDAAAMA4BBgAAGIcAAwAAjMNFvACGNC70BczECgwAADAOAQYAABiHAAMAAIxDgAEAAMbhIl4AOAcu9AUGHgIMAAxChC4MdgSYb6kvbw4AAJjIhADMNTAAAMA4BBgAAGAcvkICgCAwYcn9dCbWDJxCgAEAw/TnNXiEHAxUBBgA6CeDNQwM1nlhYCPAAMAAwh2OQN8M6It4V69erbFjxyomJkYZGRnauXNnuEsCAAADwIBdgXn55ZdVUlKiiooKZWRkaNWqVcrOzlZTU5OSkpLCXR4AIMj4KgrnY8AGmKefflpz587VnXfeKUmqqKjQa6+9pt/+9rcqLS0Nc3UAgPMx0C487otghSWCWWgMyABz4sQJNTQ0aMmSJVZbRESEsrKyVFtbe9Z9urq61NXVZb32er2SJJ/PF5Iae7u+DMlxAQBfL2XhK0P2XPuWZ/dDJV/py2dcqD5fTx3X7/d/47gBGWD+9re/qaenR06nM6Dd6XTq4MGDZ92nrKxMy5cvP6M9OTk5JDUCANCfHKvCXUGgUNfzxRdfyOFwfG3/gAww38aSJUtUUlJive7t7VV7e7sSExNls9kCxvp8PiUnJ6ulpUV2u72/Sw0b5j205i0N3bkzb+Y9FAzWefv9fn3xxRdyu93fOG5ABphRo0Zp2LBham1tDWhvbW2Vy+U66z7R0dGKjo4OaIuPj//G89jt9kH1R+8r5j30DNW5M++hhXkPHt+08nLKgLyNOioqSlOmTFF1dbXV1tvbq+rqamVmZoaxMgAAMBAMyBUYSSopKVFBQYHS09N15ZVXatWqVTp27Jh1VxIAABi6BmyA+clPfqLPPvtMy5Ytk8fj0WWXXaatW7eecWHvtxEdHa2HHnrojK+cBjvmPbTmLQ3duTNv5j0UDNV5n2Lzn+s+JQAAgAFmQF4DAwAA8E0IMAAAwDgEGAAAYBwCDAAAMM6QCzCrV6/W2LFjFRMTo4yMDO3cuTPcJYVcWVmZrrjiCo0YMUJJSUmaPXu2mpqawl1Wv1uxYoVsNpsWLFgQ7lJC7tNPP9XPfvYzJSYmKjY2VpMmTdKuXbvCXVZI9fT0aOnSpUpNTVVsbKy+//3v69FHHz3n76mYqKamRrNmzZLb7ZbNZtPmzZsD+v1+v5YtW6bRo0crNjZWWVlZOnToUHiKDaJvmnd3d7cWL16sSZMmKS4uTm63W3fccYeOHj0avoKD5Fx/77/385//XDabTatWreq3+sJlSAWYl19+WSUlJXrooYe0e/duTZ48WdnZ2Wprawt3aSG1fft2FRUVaceOHaqqqlJ3d7dmzJihY8eOhbu0flNfX69f//rXuvTSS8NdSsh9/vnnmjp1qoYPH67XX39dH3zwgX71q19p5MiR4S4tpJ588kmtWbNG//mf/6kDBw7oySef1MqVK/Xcc8+Fu7SgO3bsmCZPnqzVq1eftX/lypUqLy9XRUWF6urqFBcXp+zsbB0/fryfKw2ub5r3l19+qd27d2vp0qXavXu3/vCHP6ipqUk333xzGCoNrnP9vU/ZtGmTduzYcc5H8A8a/iHkyiuv9BcVFVmve3p6/G63219WVhbGqvpfW1ubX5J/+/bt4S6lX3zxxRf+iy++2F9VVeX/p3/6J/+9994b7pJCavHixf5p06aFu4x+l5OT47/rrrsC2m655RZ/fn5+mCrqH5L8mzZtsl739vb6XS6X/6mnnrLaOjo6/NHR0f7//u//DkOFoXH6vM9m586dfkn+I0eO9E9R/eDr5v3JJ5/4/+Ef/sG/b98+/5gxY/zPPPNMv9fW34bMCsyJEyfU0NCgrKwsqy0iIkJZWVmqra0NY2X9z+v1SpISEhLCXEn/KCoqUk5OTsDffjB79dVXlZ6erh//+MdKSkrS5ZdfrhdeeCHcZYXc1Vdfrerqan344YeSpL/85S969913dcMNN4S5sv51+PBheTyegP/eHQ6HMjIyhuR7nc1mO+fv4pmut7dXc+bM0aJFizRx4sRwl9NvBuyTeIPtb3/7m3p6es54kq/T6dTBgwfDVFX/6+3t1YIFCzR16lRdcskl4S4n5F566SXt3r1b9fX14S6l3/z1r3/VmjVrVFJSov/4j/9QfX295s+fr6ioKBUUFIS7vJApLS2Vz+fT+PHjNWzYMPX09Ojxxx9Xfn5+uEvrVx6PR5LO+l53qm8oOH78uBYvXqyf/vSng+6HDk/35JNPKjIyUvPnzw93Kf1qyAQYfKWoqEj79u3Tu+++G+5SQq6lpUX33nuvqqqqFBMTE+5y+k1vb6/S09P1xBNPSJIuv/xy7du3TxUVFYM6wPz+97/Xhg0btHHjRk2cOFGNjY1asGCB3G73oJ43ztTd3a3bbrtNfr9fa9asCXc5IdXQ0KBnn31Wu3fvls1mC3c5/WrIfIU0atQoDRs2TK2trQHtra2tcrlcYaqqfxUXF6uyslJvvfWWLrroonCXE3INDQ1qa2vTD3/4Q0VGRioyMlLbt29XeXm5IiMj1dPTE+4SQ2L06NFKS0sLaJswYYKam5vDVFH/WLRokUpLS5WXl6dJkyZpzpw5WrhwocrKysJdWr869X42VN/rToWXI0eOqKqqatCvvrzzzjtqa2tTSkqK9T535MgR3XfffRo7dmy4ywupIRNgoqKiNGXKFFVXV1ttvb29qq6uVmZmZhgrCz2/36/i4mJt2rRJ27ZtU2pqarhL6hfTp0/X3r171djYaG3p6enKz89XY2Ojhg0bFu4SQ2Lq1Kln3Cb/4YcfasyYMWGqqH98+eWXiogIfEsbNmyYent7w1RReKSmpsrlcgW81/l8PtXV1Q3697pT4eXQoUP605/+pMTExHCXFHJz5szRnj17At7n3G63Fi1apDfeeCPc5YXUkPoKqaSkRAUFBUpPT9eVV16pVatW6dixY7rzzjvDXVpIFRUVaePGjfrjH/+oESNGWN+DOxwOxcbGhrm60BkxYsQZ1/nExcUpMTFxUF//s3DhQl199dV64okndNttt2nnzp1au3at1q5dG+7SQmrWrFl6/PHHlZKSookTJ+rPf/6znn76ad11113hLi3oOjs79dFHH1mvDx8+rMbGRiUkJCglJUULFizQY489posvvlipqalaunSp3G63Zs+eHb6ig+Cb5j169Gjdeuut2r17tyorK9XT02O91yUkJCgqKipcZX9n5/p7nx7Uhg8fLpfLpXHjxvV3qf0r3LdB9bfnnnvOn5KS4o+KivJfeeWV/h07doS7pJCTdNbtxRdfDHdp/W4o3Ebt9/v9W7Zs8V9yySX+6Oho//jx4/1r164Nd0kh5/P5/Pfee68/JSXFHxMT4//Hf/xH/wMPPODv6uoKd2lB99Zbb531/+mCggK/3//VrdRLly71O51Of3R0tH/69On+pqam8BYdBN8078OHD3/te91bb70V7tK/k3P9vU83VG6jtvn9g/AxlQAAYFAbMtfAAACAwYMAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADj/D/76S536JU2qgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "data[\"IncomeBins\"] = pd.cut(x=data[\"MedInc\"], bins=[0, 2., 4., 6., 8., np.inf])\n",
        "data[\"IncomeBins\"].value_counts() / len(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "86Z-UlK84Plk",
        "outputId": "810a706d-5eb4-4f72-a6b3-b5d4a68ea056"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IncomeBins\n",
              "(2.0, 4.0]    0.489147\n",
              "(4.0, 6.0]    0.277374\n",
              "(0.0, 2.0]    0.119041\n",
              "(6.0, 8.0]    0.081008\n",
              "(8.0, inf]    0.033430\n",
              "Name: count, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, stratify=data[\"IncomeBins\"])\n",
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "srYh6vR3-MB0",
        "outputId": "0aeaaf57-38ad-4fc8-f6ea-4f363268bffd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((16512, 11), (4128, 11), (16512,), (4128,))"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.2, stratify=X_train[\"IncomeBins\"])\n",
        "X_train[\"IncomeBins\"].value_counts() / len(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "12AopmPj-hK_",
        "outputId": "520a498d-0f1c-4f82-a70c-6c713a9aadce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IncomeBins\n",
              "(2.0, 4.0]    0.489136\n",
              "(4.0, 6.0]    0.277387\n",
              "(0.0, 2.0]    0.119010\n",
              "(6.0, 8.0]    0.081005\n",
              "(8.0, inf]    0.033462\n",
              "Name: count, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.drop(\"IncomeBins\", axis=1, inplace=True)\n",
        "X_valid.drop(\"IncomeBins\", axis=1, inplace=True)\n",
        "X_test.drop(\"IncomeBins\", axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "GeS3tS00-uvU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.hist(bins=40);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "HtTcrp8U-92Q",
        "outputId": "e27735aa-629e-411a-9890-7a78b34085e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import FunctionTransformer\n",
        "from sklearn.svm import SVR\n",
        "\n",
        "num_pipeline = make_pipeline(SimpleImputer(strategy=\"median\"),\n",
        "                             StandardScaler())\n",
        "log_num_pipeline = make_pipeline(SimpleImputer(strategy=\"median\"),\n",
        "                                 FunctionTransformer(func=np.log, inverse_func=np.exp),\n",
        "                                 StandardScaler())\n",
        "\n",
        "preprocessing = ColumnTransformer([\n",
        "    (\"log\", log_num_pipeline, [\"MedInc\", \"AveRooms\", \"AveBedrms\", \"Population\", \"people_per_room\", \"bedrooms_per_people\"]),\n",
        "], remainder=num_pipeline)\n",
        "\n",
        "final_svc = make_pipeline(preprocessing, SVR())"
      ],
      "metadata": {
        "id": "dsyBF6L-3EDR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "scores = -cross_val_score(estimator=final_svc,\n",
        "                          X=X_train,\n",
        "                          y=y_train,\n",
        "                          scoring=\"neg_root_mean_squared_error\",\n",
        "                          cv=3,\n",
        "                          verbose=3)\n",
        "print(scores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LtycoZWx_WSS",
        "outputId": "d8f984fd-5b30-4b3c-cdc1-ad24da32742b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END ............................... score: (test=-0.606) total time=   5.5s\n",
            "[CV] END ............................... score: (test=-0.589) total time=   7.1s\n",
            "[CV] END ............................... score: (test=-0.629) total time=   5.6s\n",
            "[0.60556061 0.58884477 0.62868015]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid = {'svr__C': [0.1, 1, 10, 100, 1000],\n",
        "              'svr__gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
        "              'svr__kernel': ['rbf']}\n",
        "\n",
        "grid = GridSearchCV(final_svc,\n",
        "                    param_grid,\n",
        "                    refit = True,\n",
        "                    cv=3,\n",
        "                    verbose = 3)\n",
        "\n",
        "# fitting the model for grid search\n",
        "grid.fit(X_train[:2500], y_train[:2500])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "CyzkP90P_1mr",
        "outputId": "f60877e2-6890-4a6a-fac3-1af974da87f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 25 candidates, totalling 75 fits\n",
            "[CV 1/3] END svr__C=0.1, svr__gamma=1, svr__kernel=rbf;, score=0.280 total time=   0.2s\n",
            "[CV 2/3] END svr__C=0.1, svr__gamma=1, svr__kernel=rbf;, score=0.301 total time=   0.2s\n",
            "[CV 3/3] END svr__C=0.1, svr__gamma=1, svr__kernel=rbf;, score=0.223 total time=   0.2s\n",
            "[CV 1/3] END svr__C=0.1, svr__gamma=0.1, svr__kernel=rbf;, score=0.603 total time=   0.2s\n",
            "[CV 2/3] END svr__C=0.1, svr__gamma=0.1, svr__kernel=rbf;, score=0.639 total time=   0.2s\n",
            "[CV 3/3] END svr__C=0.1, svr__gamma=0.1, svr__kernel=rbf;, score=0.530 total time=   0.2s\n",
            "[CV 1/3] END svr__C=0.1, svr__gamma=0.01, svr__kernel=rbf;, score=0.479 total time=   0.2s\n",
            "[CV 2/3] END svr__C=0.1, svr__gamma=0.01, svr__kernel=rbf;, score=0.523 total time=   0.2s\n",
            "[CV 3/3] END svr__C=0.1, svr__gamma=0.01, svr__kernel=rbf;, score=0.422 total time=   0.2s\n",
            "[CV 1/3] END svr__C=0.1, svr__gamma=0.001, svr__kernel=rbf;, score=0.148 total time=   0.3s\n",
            "[CV 2/3] END svr__C=0.1, svr__gamma=0.001, svr__kernel=rbf;, score=0.187 total time=   0.2s\n",
            "[CV 3/3] END svr__C=0.1, svr__gamma=0.001, svr__kernel=rbf;, score=0.123 total time=   0.2s\n",
            "[CV 1/3] END svr__C=0.1, svr__gamma=0.0001, svr__kernel=rbf;, score=-0.027 total time=   0.2s\n",
            "[CV 2/3] END svr__C=0.1, svr__gamma=0.0001, svr__kernel=rbf;, score=-0.013 total time=   0.3s\n",
            "[CV 3/3] END svr__C=0.1, svr__gamma=0.0001, svr__kernel=rbf;, score=-0.043 total time=   0.3s\n",
            "[CV 1/3] END svr__C=1, svr__gamma=1, svr__kernel=rbf;, score=0.542 total time=   0.3s\n",
            "[CV 2/3] END svr__C=1, svr__gamma=1, svr__kernel=rbf;, score=0.585 total time=   0.3s\n",
            "[CV 3/3] END svr__C=1, svr__gamma=1, svr__kernel=rbf;, score=0.463 total time=   0.3s\n",
            "[CV 1/3] END svr__C=1, svr__gamma=0.1, svr__kernel=rbf;, score=0.677 total time=   0.2s\n",
            "[CV 2/3] END svr__C=1, svr__gamma=0.1, svr__kernel=rbf;, score=0.732 total time=   0.2s\n",
            "[CV 3/3] END svr__C=1, svr__gamma=0.1, svr__kernel=rbf;, score=0.616 total time=   0.2s\n",
            "[CV 1/3] END svr__C=1, svr__gamma=0.01, svr__kernel=rbf;, score=0.637 total time=   0.2s\n",
            "[CV 2/3] END svr__C=1, svr__gamma=0.01, svr__kernel=rbf;, score=0.662 total time=   0.2s\n",
            "[CV 3/3] END svr__C=1, svr__gamma=0.01, svr__kernel=rbf;, score=0.578 total time=   0.2s\n",
            "[CV 1/3] END svr__C=1, svr__gamma=0.001, svr__kernel=rbf;, score=0.463 total time=   0.2s\n",
            "[CV 2/3] END svr__C=1, svr__gamma=0.001, svr__kernel=rbf;, score=0.500 total time=   0.2s\n",
            "[CV 3/3] END svr__C=1, svr__gamma=0.001, svr__kernel=rbf;, score=0.417 total time=   0.2s\n",
            "[CV 1/3] END svr__C=1, svr__gamma=0.0001, svr__kernel=rbf;, score=0.149 total time=   0.2s\n",
            "[CV 2/3] END svr__C=1, svr__gamma=0.0001, svr__kernel=rbf;, score=0.189 total time=   0.2s\n",
            "[CV 3/3] END svr__C=1, svr__gamma=0.0001, svr__kernel=rbf;, score=0.124 total time=   0.2s\n",
            "[CV 1/3] END svr__C=10, svr__gamma=1, svr__kernel=rbf;, score=0.523 total time=   0.6s\n",
            "[CV 2/3] END svr__C=10, svr__gamma=1, svr__kernel=rbf;, score=0.570 total time=   0.5s\n",
            "[CV 3/3] END svr__C=10, svr__gamma=1, svr__kernel=rbf;, score=0.473 total time=   0.7s\n",
            "[CV 1/3] END svr__C=10, svr__gamma=0.1, svr__kernel=rbf;, score=0.691 total time=   0.7s\n",
            "[CV 2/3] END svr__C=10, svr__gamma=0.1, svr__kernel=rbf;, score=0.717 total time=   0.7s\n",
            "[CV 3/3] END svr__C=10, svr__gamma=0.1, svr__kernel=rbf;, score=0.650 total time=   0.8s\n",
            "[CV 1/3] END svr__C=10, svr__gamma=0.01, svr__kernel=rbf;, score=0.669 total time=   0.4s\n",
            "[CV 2/3] END svr__C=10, svr__gamma=0.01, svr__kernel=rbf;, score=0.690 total time=   0.3s\n",
            "[CV 3/3] END svr__C=10, svr__gamma=0.01, svr__kernel=rbf;, score=0.612 total time=   0.3s\n",
            "[CV 1/3] END svr__C=10, svr__gamma=0.001, svr__kernel=rbf;, score=0.595 total time=   0.2s\n",
            "[CV 2/3] END svr__C=10, svr__gamma=0.001, svr__kernel=rbf;, score=0.557 total time=   0.2s\n",
            "[CV 3/3] END svr__C=10, svr__gamma=0.001, svr__kernel=rbf;, score=0.546 total time=   0.3s\n",
            "[CV 1/3] END svr__C=10, svr__gamma=0.0001, svr__kernel=rbf;, score=0.456 total time=   0.2s\n",
            "[CV 2/3] END svr__C=10, svr__gamma=0.0001, svr__kernel=rbf;, score=0.480 total time=   0.2s\n",
            "[CV 3/3] END svr__C=10, svr__gamma=0.0001, svr__kernel=rbf;, score=0.414 total time=   0.2s\n",
            "[CV 1/3] END svr__C=100, svr__gamma=1, svr__kernel=rbf;, score=0.409 total time=   0.9s\n",
            "[CV 2/3] END svr__C=100, svr__gamma=1, svr__kernel=rbf;, score=0.549 total time=   0.6s\n",
            "[CV 3/3] END svr__C=100, svr__gamma=1, svr__kernel=rbf;, score=0.404 total time=   0.9s\n",
            "[CV 1/3] END svr__C=100, svr__gamma=0.1, svr__kernel=rbf;, score=0.645 total time=   2.4s\n",
            "[CV 2/3] END svr__C=100, svr__gamma=0.1, svr__kernel=rbf;, score=0.652 total time=   2.4s\n",
            "[CV 3/3] END svr__C=100, svr__gamma=0.1, svr__kernel=rbf;, score=0.629 total time=   3.7s\n",
            "[CV 1/3] END svr__C=100, svr__gamma=0.01, svr__kernel=rbf;, score=0.679 total time=   0.6s\n",
            "[CV 2/3] END svr__C=100, svr__gamma=0.01, svr__kernel=rbf;, score=0.690 total time=   0.5s\n",
            "[CV 3/3] END svr__C=100, svr__gamma=0.01, svr__kernel=rbf;, score=0.561 total time=   0.5s\n",
            "[CV 1/3] END svr__C=100, svr__gamma=0.001, svr__kernel=rbf;, score=0.649 total time=   0.3s\n",
            "[CV 2/3] END svr__C=100, svr__gamma=0.001, svr__kernel=rbf;, score=0.422 total time=   0.3s\n",
            "[CV 3/3] END svr__C=100, svr__gamma=0.001, svr__kernel=rbf;, score=0.585 total time=   0.3s\n",
            "[CV 1/3] END svr__C=100, svr__gamma=0.0001, svr__kernel=rbf;, score=0.567 total time=   0.3s\n",
            "[CV 2/3] END svr__C=100, svr__gamma=0.0001, svr__kernel=rbf;, score=-0.218 total time=   0.2s\n",
            "[CV 3/3] END svr__C=100, svr__gamma=0.0001, svr__kernel=rbf;, score=0.521 total time=   0.2s\n",
            "[CV 1/3] END svr__C=1000, svr__gamma=1, svr__kernel=rbf;, score=0.389 total time=   1.1s\n",
            "[CV 2/3] END svr__C=1000, svr__gamma=1, svr__kernel=rbf;, score=0.549 total time=   0.6s\n",
            "[CV 3/3] END svr__C=1000, svr__gamma=1, svr__kernel=rbf;, score=0.398 total time=   1.1s\n",
            "[CV 1/3] END svr__C=1000, svr__gamma=0.1, svr__kernel=rbf;, score=0.422 total time=  26.7s\n",
            "[CV 2/3] END svr__C=1000, svr__gamma=0.1, svr__kernel=rbf;, score=0.365 total time=  35.0s\n",
            "[CV 3/3] END svr__C=1000, svr__gamma=0.1, svr__kernel=rbf;, score=0.440 total time=  30.0s\n",
            "[CV 1/3] END svr__C=1000, svr__gamma=0.01, svr__kernel=rbf;, score=0.662 total time=   2.3s\n",
            "[CV 2/3] END svr__C=1000, svr__gamma=0.01, svr__kernel=rbf;, score=0.653 total time=   2.7s\n",
            "[CV 3/3] END svr__C=1000, svr__gamma=0.01, svr__kernel=rbf;, score=0.498 total time=   4.0s\n",
            "[CV 1/3] END svr__C=1000, svr__gamma=0.001, svr__kernel=rbf;, score=0.679 total time=   0.6s\n",
            "[CV 2/3] END svr__C=1000, svr__gamma=0.001, svr__kernel=rbf;, score=-0.022 total time=   0.6s\n",
            "[CV 3/3] END svr__C=1000, svr__gamma=0.001, svr__kernel=rbf;, score=0.500 total time=   0.5s\n",
            "[CV 1/3] END svr__C=1000, svr__gamma=0.0001, svr__kernel=rbf;, score=0.607 total time=   0.3s\n",
            "[CV 2/3] END svr__C=1000, svr__gamma=0.0001, svr__kernel=rbf;, score=-7.245 total time=   0.3s\n",
            "[CV 3/3] END svr__C=1000, svr__gamma=0.0001, svr__kernel=rbf;, score=0.559 total time=   0.3s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3,\n",
              "             estimator=Pipeline(steps=[('columntransformer',\n",
              "                                        ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
              "                                                                                     SimpleImputer(strategy='median')),\n",
              "                                                                                    ('standardscaler',\n",
              "                                                                                     StandardScaler())]),\n",
              "                                                          transformers=[('log',\n",
              "                                                                         Pipeline(steps=[('simpleimputer',\n",
              "                                                                                          SimpleImputer(strategy='median')),\n",
              "                                                                                         ('functiontransformer',\n",
              "                                                                                          FunctionTransformer(func=<ufunc 'log'>,\n",
              "                                                                                                              inverse_func=<ufunc 'exp'>)),\n",
              "                                                                                         ('standardscaler',\n",
              "                                                                                          StandardScaler())]),\n",
              "                                                                         ['MedInc',\n",
              "                                                                          'AveRooms',\n",
              "                                                                          'AveBedrms',\n",
              "                                                                          'Population',\n",
              "                                                                          'people_per_room',\n",
              "                                                                          'bedrooms_per_people'])])),\n",
              "                                       ('svr', SVR())]),\n",
              "             param_grid={'svr__C': [0.1, 1, 10, 100, 1000],\n",
              "                         'svr__gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
              "                         'svr__kernel': ['rbf']},\n",
              "             verbose=3)"
            ],
            "text/html": [
              "<style>#sk-container-id-17 {color: black;background-color: white;}#sk-container-id-17 pre{padding: 0;}#sk-container-id-17 div.sk-toggleable {background-color: white;}#sk-container-id-17 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-17 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-17 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-17 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-17 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-17 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-17 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-17 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-17 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-17 div.sk-item {position: relative;z-index: 1;}#sk-container-id-17 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-17 div.sk-item::before, #sk-container-id-17 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-17 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-17 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-17 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-17 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-17 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-17 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-17 div.sk-label-container {text-align: center;}#sk-container-id-17 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-17 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3,\n",
              "             estimator=Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                                        ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
              "                                                                                     SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                                                    (&#x27;standardscaler&#x27;,\n",
              "                                                                                     StandardScaler())]),\n",
              "                                                          transformers=[(&#x27;log&#x27;,\n",
              "                                                                         Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
              "                                                                                          SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                                                         (&#x27;functiontransformer&#x27;,\n",
              "                                                                                          FunctionTransformer(func=&lt;ufunc &#x27;log&#x27;&gt;,\n",
              "                                                                                                              inverse_func=&lt;ufunc &#x27;exp&#x27;&gt;)),\n",
              "                                                                                         (&#x27;standardscaler&#x27;,\n",
              "                                                                                          StandardScaler())]),\n",
              "                                                                         [&#x27;MedInc&#x27;,\n",
              "                                                                          &#x27;AveRooms&#x27;,\n",
              "                                                                          &#x27;AveBedrms&#x27;,\n",
              "                                                                          &#x27;Population&#x27;,\n",
              "                                                                          &#x27;people_per_room&#x27;,\n",
              "                                                                          &#x27;bedrooms_per_people&#x27;])])),\n",
              "                                       (&#x27;svr&#x27;, SVR())]),\n",
              "             param_grid={&#x27;svr__C&#x27;: [0.1, 1, 10, 100, 1000],\n",
              "                         &#x27;svr__gamma&#x27;: [1, 0.1, 0.01, 0.001, 0.0001],\n",
              "                         &#x27;svr__kernel&#x27;: [&#x27;rbf&#x27;]},\n",
              "             verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-55\" type=\"checkbox\" ><label for=\"sk-estimator-id-55\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3,\n",
              "             estimator=Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                                        ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
              "                                                                                     SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                                                    (&#x27;standardscaler&#x27;,\n",
              "                                                                                     StandardScaler())]),\n",
              "                                                          transformers=[(&#x27;log&#x27;,\n",
              "                                                                         Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
              "                                                                                          SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                                                         (&#x27;functiontransformer&#x27;,\n",
              "                                                                                          FunctionTransformer(func=&lt;ufunc &#x27;log&#x27;&gt;,\n",
              "                                                                                                              inverse_func=&lt;ufunc &#x27;exp&#x27;&gt;)),\n",
              "                                                                                         (&#x27;standardscaler&#x27;,\n",
              "                                                                                          StandardScaler())]),\n",
              "                                                                         [&#x27;MedInc&#x27;,\n",
              "                                                                          &#x27;AveRooms&#x27;,\n",
              "                                                                          &#x27;AveBedrms&#x27;,\n",
              "                                                                          &#x27;Population&#x27;,\n",
              "                                                                          &#x27;people_per_room&#x27;,\n",
              "                                                                          &#x27;bedrooms_per_people&#x27;])])),\n",
              "                                       (&#x27;svr&#x27;, SVR())]),\n",
              "             param_grid={&#x27;svr__C&#x27;: [0.1, 1, 10, 100, 1000],\n",
              "                         &#x27;svr__gamma&#x27;: [1, 0.1, 0.01, 0.001, 0.0001],\n",
              "                         &#x27;svr__kernel&#x27;: [&#x27;rbf&#x27;]},\n",
              "             verbose=3)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-56\" type=\"checkbox\" ><label for=\"sk-estimator-id-56\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                 ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
              "                                                              SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                             (&#x27;standardscaler&#x27;,\n",
              "                                                              StandardScaler())]),\n",
              "                                   transformers=[(&#x27;log&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                                  (&#x27;functiontransformer&#x27;,\n",
              "                                                                   FunctionTransformer(func=&lt;ufunc &#x27;log&#x27;&gt;,\n",
              "                                                                                       inverse_func=&lt;ufunc &#x27;exp&#x27;&gt;)),\n",
              "                                                                  (&#x27;standardscaler&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  [&#x27;MedInc&#x27;, &#x27;AveRooms&#x27;,\n",
              "                                                   &#x27;AveBedrms&#x27;, &#x27;Population&#x27;,\n",
              "                                                   &#x27;people_per_room&#x27;,\n",
              "                                                   &#x27;bedrooms_per_people&#x27;])])),\n",
              "                (&#x27;svr&#x27;, SVR())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-57\" type=\"checkbox\" ><label for=\"sk-estimator-id-57\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
              "                                             SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                            (&#x27;standardscaler&#x27;,\n",
              "                                             StandardScaler())]),\n",
              "                  transformers=[(&#x27;log&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                 (&#x27;functiontransformer&#x27;,\n",
              "                                                  FunctionTransformer(func=&lt;ufunc &#x27;log&#x27;&gt;,\n",
              "                                                                      inverse_func=&lt;ufunc &#x27;exp&#x27;&gt;)),\n",
              "                                                 (&#x27;standardscaler&#x27;,\n",
              "                                                  StandardScaler())]),\n",
              "                                 [&#x27;MedInc&#x27;, &#x27;AveRooms&#x27;, &#x27;AveBedrms&#x27;,\n",
              "                                  &#x27;Population&#x27;, &#x27;people_per_room&#x27;,\n",
              "                                  &#x27;bedrooms_per_people&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-58\" type=\"checkbox\" ><label for=\"sk-estimator-id-58\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">log</label><div class=\"sk-toggleable__content\"><pre>[&#x27;MedInc&#x27;, &#x27;AveRooms&#x27;, &#x27;AveBedrms&#x27;, &#x27;Population&#x27;, &#x27;people_per_room&#x27;, &#x27;bedrooms_per_people&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-59\" type=\"checkbox\" ><label for=\"sk-estimator-id-59\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-60\" type=\"checkbox\" ><label for=\"sk-estimator-id-60\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;ufunc &#x27;log&#x27;&gt;, inverse_func=&lt;ufunc &#x27;exp&#x27;&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-61\" type=\"checkbox\" ><label for=\"sk-estimator-id-61\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-62\" type=\"checkbox\" ><label for=\"sk-estimator-id-62\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-63\" type=\"checkbox\" ><label for=\"sk-estimator-id-63\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-64\" type=\"checkbox\" ><label for=\"sk-estimator-id-64\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-65\" type=\"checkbox\" ><label for=\"sk-estimator-id-65\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid.best_params_, grid.best_estimator_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FkYrjRKjApAE",
        "outputId": "5757c68c-74c3-4685-a4ce-aac4bb10c09c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "({'svr__C': 10, 'svr__gamma': 0.1, 'svr__kernel': 'rbf'},\n",
              " Pipeline(steps=[('columntransformer',\n",
              "                  ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
              "                                                               SimpleImputer(strategy='median')),\n",
              "                                                              ('standardscaler',\n",
              "                                                               StandardScaler())]),\n",
              "                                    transformers=[('log',\n",
              "                                                   Pipeline(steps=[('simpleimputer',\n",
              "                                                                    SimpleImputer(strategy='median')),\n",
              "                                                                   ('functiontransformer',\n",
              "                                                                    FunctionTransformer(func=<ufunc 'log'>,\n",
              "                                                                                        inverse_func=<ufunc 'exp'>)),\n",
              "                                                                   ('standardscaler',\n",
              "                                                                    StandardScaler())]),\n",
              "                                                   ['MedInc', 'AveRooms',\n",
              "                                                    'AveBedrms', 'Population',\n",
              "                                                    'people_per_room',\n",
              "                                                    'bedrooms_per_people'])])),\n",
              "                 ('svr', SVR(C=10, gamma=0.1))]))"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_svr = grid.best_estimator_"
      ],
      "metadata": {
        "id": "LjF8QQXTAu_o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scores_svr = -cross_val_score(estimator=best_svr,\n",
        "                          X=X_train,\n",
        "                          y=y_train,\n",
        "                          scoring=\"neg_root_mean_squared_error\",\n",
        "                          cv=3,\n",
        "                          verbose=3)\n",
        "print(scores_svr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t4LrkKg-AzBT",
        "outputId": "d3191cc7-45e1-4a24-caee-1ff4ec948270"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] END ............................... score: (test=-0.591) total time=  10.7s\n",
            "[CV] END ............................... score: (test=-0.569) total time=   9.7s\n",
            "[CV] END ............................... score: (test=-0.599) total time=  10.2s\n",
            "[0.59083931 0.56860939 0.5994242 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "best_svr.fit(X_train, y_train)\n",
        "print(f\"RMSE on validation set = {mean_squared_error(y_valid, best_svr.predict(X_valid), squared=False)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "py3QWlUVBAMw",
        "outputId": "4af69baf-c641-4842-ac7c-abcb2534de8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE on validation set = 0.5776635766973006\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_full = pd.concat([X_train, X_valid], axis=0)\n",
        "y_full = pd.concat([y_train, y_valid], axis=0)\n",
        "\n",
        "best_svr.fit(X_full, y_full)\n",
        "print(f\"RMSE on test set = {mean_squared_error(y_test, best_svr.predict(X_test), squared=False)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5jcrNhnUBqap",
        "outputId": "2e544495-903b-4727-ed89-c8cdf6133233"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE on validation set = 0.5688566878094117\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VvhTbd7xB77k"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}