ch4_polynomial_regression.ipynb

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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ThatNinjaGuy/Machine-learning-A-Z-Course/blob/develop/ch4_polynomial_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vN99YjPTDena"
      },
      "source": [
        "# Polynomial Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZIx_naXnDyHd"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FjnmdyPLD2tS"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6c8YExmOD5x5"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQOdXhjXD_AE"
      },
      "source": [
        "dataset = pd.read_csv('Position_Salaries.csv')\n",
        "X = dataset.iloc[:, 1:-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Le8SEL-YEOLb"
      },
      "source": [
        "## Training the Linear Regression model on the whole dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lin_reg = LinearRegression()\n",
        "lin_reg.fit(X, y)"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 74
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      "outputs": [
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          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
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              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 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-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 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-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 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-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 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-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" 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-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" 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": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rb5nWuSHEfBV"
      },
      "source": [
        "## Training the Polynomial Regression model on the whole dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "# Modifying the degree results in the better calulations as more degrees of powers of a metric is applied\n",
        "# This can be fine tuned based on complexity and time of computation\n",
        "# This can lead to overfitting as well, if it is optimized too much\n",
        "poly_reg = PolynomialFeatures(degree = 5)\n",
        "X_poly = poly_reg.fit_transform(X)\n",
        "print(X_poly)"
      ],
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      },
      "execution_count": 34,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "[[1.0000e+00 1.0000e+00 1.0000e+00 1.0000e+00 1.0000e+00 1.0000e+00]\n",
            " [1.0000e+00 2.0000e+00 4.0000e+00 8.0000e+00 1.6000e+01 3.2000e+01]\n",
            " [1.0000e+00 3.0000e+00 9.0000e+00 2.7000e+01 8.1000e+01 2.4300e+02]\n",
            " [1.0000e+00 4.0000e+00 1.6000e+01 6.4000e+01 2.5600e+02 1.0240e+03]\n",
            " [1.0000e+00 5.0000e+00 2.5000e+01 1.2500e+02 6.2500e+02 3.1250e+03]\n",
            " [1.0000e+00 6.0000e+00 3.6000e+01 2.1600e+02 1.2960e+03 7.7760e+03]\n",
            " [1.0000e+00 7.0000e+00 4.9000e+01 3.4300e+02 2.4010e+03 1.6807e+04]\n",
            " [1.0000e+00 8.0000e+00 6.4000e+01 5.1200e+02 4.0960e+03 3.2768e+04]\n",
            " [1.0000e+00 9.0000e+00 8.1000e+01 7.2900e+02 6.5610e+03 5.9049e+04]\n",
            " [1.0000e+00 1.0000e+01 1.0000e+02 1.0000e+03 1.0000e+04 1.0000e+05]]\n"
          ]
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      "cell_type": "code",
      "source": [
        "lin_reg_2 = LinearRegression()\n",
        "lin_reg_2.fit(X_poly, y)"
      ],
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          "base_uri": "https://localhost:8080/",
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      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "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>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-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" 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": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0O8R0tzbEpvy"
      },
      "source": [
        "## Visualising the Linear Regression results"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X, y, color= 'red')\n",
        "plt.plot(X, lin_reg.predict(X), color = 'blue')\n",
        "plt.title('Truth or Bluff (Linear Regression)')\n",
        "plt.xlabel('Position level')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "UgMeon2vS4Fo",
        "outputId": "bd07eb86-207f-4821-d6b8-470d4e38a7bb"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "stOnSo74E52m"
      },
      "source": [
        "## Visualising the Polynomial Regression results"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X, y, color='red')\n",
        "plt.plot(X, lin_reg_2.predict(X_poly), color='blue')\n",
        "plt.title('Truth or Bluff (Polynomial Regression)')\n",
        "plt.xlabel('Position level')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "xRS8XSNSTKV9",
        "outputId": "0409fc80-f021-493a-9907-47c50af3e599"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U_qsAMKnE-PJ"
      },
      "source": [
        "## Visualising the Polynomial Regression results (for higher resolution and smoother curve)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_grid = np.arange(min(X), max(X), 0.1)\n",
        "X_grid = X_grid.reshape((len(X_grid), 1))\n",
        "plt.scatter(X, y, color = 'red')\n",
        "plt.plot(X_grid, lin_reg_2.predict(poly_reg.fit_transform(X_grid)), color = 'blue')\n",
        "plt.title('Truth or Bluff (Polynomial Regression)')\n",
        "plt.xlabel('Position level')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "id": "0mYHyjD6U14f",
        "outputId": "f7a9c7a1-668a-40c4-e321-50fd2cf935e1"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-38-72d1221b8e2e>:1: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
            "  X_grid = np.arange(min(X), max(X), 0.1)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "diyJFZHhFFeK"
      },
      "source": [
        "## Predicting a new result with Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lin_reg.predict([[6.5],[7.5]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DietBf70VLBq",
        "outputId": "93be3139-1d36-4652-dc37-87ea22fc8d01"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([330378.78787879, 411257.57575758])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DW7I7ZVDFNkk"
      },
      "source": [
        "## Predicting a new result with Polynomial Regression"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = lin_reg_2.predict(poly_reg.fit_transform([[6.5],[7.5]]))\n",
        "print (y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9LsKmPaeVe8m",
        "outputId": "91c7b8ac-db33-49ba-ad04-929570f44560"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[174878.07765173 237446.00451604]\n"
          ]
        }
      ]
    }
  ]
}