day_56_polynomial_regression.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Manish-k723/100-days-of-ML/blob/main/day_56_polynomial_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "d2fcc898",
      "metadata": {
        "id": "d2fcc898"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from sklearn.linear_model import LinearRegression,SGDRegressor\n",
        "\n",
        "from sklearn.preprocessing import PolynomialFeatures,StandardScaler\n",
        "\n",
        "from sklearn.metrics import r2_score\n",
        "\n",
        "from sklearn.pipeline import Pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "c929c0ff",
      "metadata": {
        "id": "c929c0ff"
      },
      "outputs": [],
      "source": [
        "X = 6 * np.random.rand(200, 1) - 3\n",
        "y = 0.8 * X**2 + 0.9 * X + 2 + np.random.randn(200, 1)\n",
        "\n",
        "# y = 0.8x^2 + 0.9x + 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "4dbb8ca1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "4dbb8ca1",
        "outputId": "ceb1fbd5-ef70-4c71-ec9f-f26f167a162f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(X, y,'b.')\n",
        "plt.xlabel(\"X\")\n",
        "plt.ylabel(\"y\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "aaacb73d",
      "metadata": {
        "id": "aaacb73d"
      },
      "outputs": [],
      "source": [
        "# Train test split\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "398d34ad",
      "metadata": {
        "id": "398d34ad"
      },
      "outputs": [],
      "source": [
        "# Applying linear regression\n",
        "lr = LinearRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "4223f668",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "4223f668",
        "outputId": "8bdddd58-b668-4051-8d66-54475582d5e9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "lr.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "40d930bc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "40d930bc",
        "outputId": "7c52be5f-a29a-4aaf-e4b4-e3c61cb18770"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.37428086972351293"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "y_pred = lr.predict(X_test)\n",
        "r2_score(y_test,y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "826ec29a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "826ec29a",
        "outputId": "51c7f8bb-da07-4cbd-eb10-64c43fa8754d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(X_train,lr.predict(X_train),color='r')\n",
        "plt.plot(X, y, \"b.\")\n",
        "plt.xlabel(\"X\")\n",
        "plt.ylabel(\"y\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "3499d04b",
      "metadata": {
        "id": "3499d04b"
      },
      "outputs": [],
      "source": [
        "# Applying Polynomial Linear Regression\n",
        "# degree 2\n",
        "poly = PolynomialFeatures(degree=2,include_bias=True)\n",
        "\n",
        "X_train_trans = poly.fit_transform(X_train)\n",
        "X_test_trans = poly.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "be131b5d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "be131b5d",
        "outputId": "032431e3-3f9a-474e-bdfe-6a8a864ffb1b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.23244917]\n",
            "[1.         0.23244917 0.05403262]\n"
          ]
        }
      ],
      "source": [
        "print(X_train[0])\n",
        "print(X_train_trans[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "60691ef2",
      "metadata": {
        "id": "60691ef2"
      },
      "outputs": [],
      "source": [
        "# include_bias parameter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "e316df6e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "e316df6e",
        "outputId": "8b5f8fa0-c2e1-41ed-802a-6bc1f1888f71"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "lr = LinearRegression()\n",
        "lr.fit(X_train_trans,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "ff5960ed",
      "metadata": {
        "id": "ff5960ed"
      },
      "outputs": [],
      "source": [
        "y_pred = lr.predict(X_test_trans)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "3b2af778",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3b2af778",
        "outputId": "f475dcab-2645-4a6d-c005-2dbc66400d54"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8784561715384334"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "r2_score(y_test,y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "1d5ac42b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1d5ac42b",
        "outputId": "be8e27ea-6d44-45d0-d94e-d64c79a8cf24"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.         0.79131532 0.77589152]]\n",
            "[2.15157487]\n"
          ]
        }
      ],
      "source": [
        "print(lr.coef_)\n",
        "print(lr.intercept_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "d3a4d4be",
      "metadata": {
        "id": "d3a4d4be"
      },
      "outputs": [],
      "source": [
        "X_new=np.linspace(-3, 3, 200).reshape(200, 1)\n",
        "X_new_poly = poly.transform(X_new)\n",
        "y_new = lr.predict(X_new_poly)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "99d73c4e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "99d73c4e",
        "outputId": "69fb1354-8c66-4965-b000-84a9ef386a61"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(X_new, y_new, \"r-\", linewidth=2, label=\"Predictions\")\n",
        "plt.plot(X_train, y_train, \"b.\",label='Training points')\n",
        "plt.plot(X_test, y_test, \"g.\",label='Testing points')\n",
        "plt.xlabel(\"X\")\n",
        "plt.ylabel(\"y\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "id": "4313857c",
      "metadata": {
        "id": "4313857c"
      },
      "outputs": [],
      "source": [
        "def polynomial_regression(degree):\n",
        "    X_new=np.linspace(-3, 3, 100).reshape(100, 1)\n",
        "    X_new_poly = poly.transform(X_new)\n",
        "\n",
        "    polybig_features = PolynomialFeatures(degree=degree, include_bias=False)\n",
        "    std_scaler = StandardScaler()\n",
        "    lin_reg = LinearRegression()\n",
        "    polynomial_regression = Pipeline([\n",
        "            (\"poly_features\", polybig_features),\n",
        "            (\"std_scaler\", std_scaler),\n",
        "            (\"lin_reg\", lin_reg),\n",
        "        ])\n",
        "    polynomial_regression.fit(X, y)\n",
        "    y_newbig = polynomial_regression.predict(X_new)\n",
        "    plt.plot(X_new, y_newbig,'r', label=\"Degree \" + str(degree), linewidth=2)\n",
        "\n",
        "    plt.plot(X_train, y_train, \"b.\", linewidth=3)\n",
        "    plt.plot(X_test, y_test, \"g.\", linewidth=3)\n",
        "    plt.legend(loc=\"upper left\")\n",
        "    plt.xlabel(\"X\")\n",
        "    plt.ylabel(\"y\")\n",
        "    plt.axis([-3, 3, 0, 10])\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "id": "8e56415e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "8e56415e",
        "outputId": "a4c894c6-3cb1-4677-d095-9bf2973db983"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "polynomial_regression(65)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "id": "7da24a4b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7da24a4b",
        "outputId": "70f345b6-f07e-4457-9478-3a82a95bd2b3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0],\n",
              "       [1],\n",
              "       [2]])"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "poly.powers_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "fb0d9c8e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "fb0d9c8e",
        "outputId": "ce92c06b-ccd9-480b-fda7-dd8a1aa8e951"
      },
      "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": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Applying Gradient Descent\n",
        "\n",
        "poly = PolynomialFeatures(degree=2)\n",
        "\n",
        "X_train_trans = poly.fit_transform(X_train)\n",
        "X_test_trans = poly.transform(X_test)\n",
        "\n",
        "sgd = SGDRegressor(max_iter=100)\n",
        "sgd.fit(X_train_trans,y_train)\n",
        "\n",
        "X_new=np.linspace(-2.9, 2.8, 200).reshape(200, 1)\n",
        "X_new_poly = poly.transform(X_new)\n",
        "y_new = sgd.predict(X_new_poly)\n",
        "\n",
        "y_pred = sgd.predict(X_test_trans)\n",
        "\n",
        "plt.plot(X_new, y_new, \"r-\", linewidth=2, label=\"Predictions \" + str(round(r2_score(y_test,y_pred),2)))\n",
        "plt.plot(X_train, y_train, \"b.\",label='Training points')\n",
        "plt.plot(X_test, y_test, \"g.\",label='Testing points')\n",
        "plt.xlabel(\"X\")\n",
        "plt.ylabel(\"y\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "c3427a03",
      "metadata": {
        "id": "c3427a03"
      },
      "outputs": [],
      "source": [
        "# 3D polynomial regression\n",
        "x = 7 * np.random.rand(100, 1) - 2.8\n",
        "y = 7 * np.random.rand(100, 1) - 2.8\n",
        "\n",
        "z = x**2 + y**2 + 0.2*x + 0.2*y + 0.1*x*y +2 + np.random.randn(100, 1)\n",
        "# z = x^2 + y^2 + 0.2x + 0.2y + 0.1xy + 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "869809ab",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "869809ab",
        "outputId": "d53d9747-9de9-42f0-faf7-6cab2bec530b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ],
      "source": [
        "import plotly.express as px\n",
        "df = px.data.iris()\n",
        "fig = px.scatter_3d(df, x=x.ravel(), y=y.ravel(), z=z.ravel())\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "30f6256e",
      "metadata": {
        "id": "30f6256e"
      },
      "outputs": [],
      "source": [
        "lr = LinearRegression()\n",
        "lr.fit(np.array([x,y]).reshape(100,2),z)\n",
        "\n",
        "x_input = np.linspace(x.min(), x.max(), 10)\n",
        "y_input = np.linspace(y.min(), y.max(), 10)\n",
        "xGrid, yGrid = np.meshgrid(x_input,y_input)\n",
        "\n",
        "final = np.vstack((xGrid.ravel().reshape(1,100),yGrid.ravel().reshape(1,100))).T\n",
        "\n",
        "z_final = lr.predict(final).reshape(10,10)\n",
        "\n"
      ]
    },
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      "execution_count": 25,
      "id": "59f98047",
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          "height": 542
        },
        "id": "59f98047",
        "outputId": "694e8dec-bd8d-45ce-8d11-943998b56b84"
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      "outputs": [
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              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import plotly.graph_objects as go\n",
        "\n",
        "fig = px.scatter_3d(df, x=x.ravel(), y=y.ravel(), z=z.ravel())\n",
        "\n",
        "fig.add_trace(go.Surface(x = x_input, y = y_input, z =z_final ))\n",
        "\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "3b155294",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3b155294",
        "outputId": "f946b9a1-2195-4656-b641-7fade05f891c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "X_multi = np.array([x,y]).reshape(100,2)\n",
        "X_multi.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "55e30cc7",
      "metadata": {
        "id": "55e30cc7"
      },
      "outputs": [],
      "source": [
        "poly = PolynomialFeatures(degree=30)\n",
        "X_multi_trans = poly.fit_transform(X_multi)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0486c5ea",
      "metadata": {
        "id": "0486c5ea"
      },
      "outputs": [],
      "source": [
        "# print(\"Input\",poly.n_input_features_)\n",
        "print(\"Ouput\",poly.n_output_features_)\n",
        "print(\"Powers\\n\",poly.powers_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "64c43050",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64c43050",
        "outputId": "1324677d-b8f8-43ce-8af5-3840387d1794"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 496)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "X_multi_trans.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "b285b9ae",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "b285b9ae",
        "outputId": "24562d38-6547-471b-920b-ff5da9117b25"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "lr = LinearRegression()\n",
        "lr.fit(X_multi_trans,z)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "e38c6645",
      "metadata": {
        "id": "e38c6645"
      },
      "outputs": [],
      "source": [
        "X_test_multi = poly.transform(final)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "4c33cbb5",
      "metadata": {
        "id": "4c33cbb5"
      },
      "outputs": [],
      "source": [
        "z_final = lr.predict(X_multi_trans).reshape(10,10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "de8b7bd8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "de8b7bd8",
        "outputId": "777788ba-1e74-432a-ff92-e97ae89bf795"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ],
      "source": [
        "fig = px.scatter_3d(x=x.ravel(), y=y.ravel(), z=z.ravel())\n",
        "\n",
        "fig.add_trace(go.Surface(x = x_input, y = y_input, z =z_final))\n",
        "\n",
        "fig.update_layout(scene = dict(zaxis = dict(range=[0,35])))\n",
        "\n",
        "fig.show()"
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