polynomial_regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of polynomial_regression.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sandeep8064/Machine-learning-/blob/main/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",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "AhQj3HyKAysO"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6c8YExmOD5x5"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('Position_Salaries.csv')\n",
        "x = dataset.iloc[:,1:-1].values\n",
        "y = dataset.iloc[:,-1].values"
      ],
      "metadata": {
        "id": "ALtM4XBFA-nB"
      },
      "execution_count": 2,
      "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",
        "linreg = LinearRegression()\n",
        "linreg.fit(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kvva26KJERbh",
        "outputId": "ba3fb1aa-ac72-4e3d-f0ee-f73850e48864"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rb5nWuSHEfBV"
      },
      "source": [
        "## Training the Polynomial Regression model on the whole dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "polyreg = PolynomialFeatures(degree=4)\n",
        "x_poly = polyreg.fit_transform(x)\n",
        "linreg2 = LinearRegression()\n",
        "linreg2.fit(x_poly,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4RPFGQRtHuLB",
        "outputId": "6e150f16-9898-455a-d7a0-2ea3acca41e7"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_poly)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qgWrFGQQI0sD",
        "outputId": "365b95ed-4eb0-46fa-e717-094443248a6c"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  1.   1.   1.]\n",
            " [  1.   2.   4.]\n",
            " [  1.   3.   9.]\n",
            " [  1.   4.  16.]\n",
            " [  1.   5.  25.]\n",
            " [  1.   6.  36.]\n",
            " [  1.   7.  49.]\n",
            " [  1.   8.  64.]\n",
            " [  1.   9.  81.]\n",
            " [  1.  10. 100.]]\n"
          ]
        }
      ]
    },
    {
      "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,linreg.predict(x), color='blue')\n",
        "plt.title('Position Vs Salary(Linear Regression')\n",
        "plt.xlabel('Poistion')\n",
        "plt.ylabel('Salaries')\n",
        "plt.show()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "sdk_999vLfy-",
        "outputId": "d4863e2b-c65b-43f1-9553-1bb07056c44e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "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,linreg2.predict(x_poly))\n",
        "plt.title('Position Vs Salaries')\n",
        "plt.xlabel('Position')\n",
        "plt.ylabel('Salaries')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "pM4Ho3GuM0h4",
        "outputId": "2e36f6ae-f760-49a8-ca5e-acda0826e0a8"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U_qsAMKnE-PJ"
      },
      "source": [
        "## Visualising the Polynomial Regression results (for higher degree i.e from 2 to 4)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x,y,color='red')\n",
        "plt.plot(x,linreg2.predict(x_poly))\n",
        "plt.title('Position Vs Salaries')\n",
        "plt.xlabel('Position')\n",
        "plt.ylabel('Salaries')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "c0b5f9f5-095f-4d22-f919-2dd3a98ed886",
        "id": "cSatpLDkQKBi"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "diyJFZHhFFeK"
      },
      "source": [
        "## Predicting a new result with Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "linreg.predict([[6.5]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sUj1ypOMSrwu",
        "outputId": "c120a2a5-250c-48d7-ca13-ed13c484a4a2"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([330378.78787879])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DW7I7ZVDFNkk"
      },
      "source": [
        "## Predicting a new result with Polynomial Regression"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "linreg2.predict(polyreg.fit_transform([[6.5]]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GaKP55IoS19j",
        "outputId": "4b3d0a32-d71c-47f1-96a7-7aa653587ce5"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([158862.45265155])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    }
  ]
}