Poly_Nomial_Regression.ipynb

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    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "_twXYXzG9wM4"
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      "source": [
        "#POLYNOMIAL REGRESSION\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('Position_Salaries.csv')\n",
        "X = dataset.iloc[:, 1:2].values\n",
        "Y = dataset.iloc[:, 2].values"
      ],
      "metadata": {
        "id": "Ys9X1ob09_Tg"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)"
      ],
      "metadata": {
        "id": "zfoBwsZ-_PQf"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc_X = StandardScaler()\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_test = sc_X.transform(X_test)"
      ],
      "metadata": {
        "id": "8z3lbGbu_i6r"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#FITTING LINEAR REGRESSION MODEL\n",
        "from sklearn.linear_model import LinearRegression\n",
        "lin_reg = LinearRegression()\n",
        "lin_reg.fit(X, Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
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              "LinearRegression()"
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            ]
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          "metadata": {},
          "execution_count": 23
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    {
      "cell_type": "code",
      "source": [
        "#FITTING POLYNOMIAL REGRESSION MODEL\n",
        "from sklearn.preprocessing import PolynomialFeatures #creates features raised to some power aka new features\n",
        "poly_reg = PolynomialFeatures(degree = 4)"
      ],
      "metadata": {
        "id": "3qaAGszl-Ep3"
      },
      "execution_count": 24,
      "outputs": []
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      "source": [
        "X_poly = poly_reg.fit_transform(X) #oth column created for x0 feature-bias\n",
        "lin_reg2 = LinearRegression()\n",
        "lin_reg2.fit(X_poly, Y)"
      ],
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              "LinearRegression()"
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              "<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": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#VISUALIZING LINEAR REGRESSION\n",
        "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": "Q9yVgCw_-KkB",
        "outputId": "4a6f0527-4d20-41c1-9770-9ba79ab5a35a"
      },
      "execution_count": 26,
      "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": [
        "#VISUALIZING POLYNOMIAL REGRESSION\n",
        "plt.scatter(X, Y, color = 'red')\n",
        "plt.plot(X, lin_reg2.predict(poly_reg.fit_transform(X)), 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": "fIAGAkmL-OMe",
        "outputId": "78b57c01-3e11-4a31-cb86-c5065d51a055"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}