Polynomial_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Polynomial Regression.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP2KWz3W0borR8I5Hk5JBte"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**Importing the Libraries**"
      ],
      "metadata": {
        "id": "xK31HUJEWCQ3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "cDKefJCHVt_G"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing the dataset**"
      ],
      "metadata": {
        "id": "MY35XqsqXNKf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('Position_Salaries.csv')\n",
        "x = dataset.iloc[:, 1:-1].values\n",
        "y = dataset.iloc[:, -1].values\n",
        "print(X)\n",
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J9PKvONBXQZ8",
        "outputId": "37489873-83f0-4fa5-c950-5686e1675b46"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1]\n",
            " [ 2]\n",
            " [ 3]\n",
            " [ 4]\n",
            " [ 5]\n",
            " [ 6]\n",
            " [ 7]\n",
            " [ 8]\n",
            " [ 9]\n",
            " [10]]\n",
            "[  45000   50000   60000   80000  110000  150000  200000  300000  500000\n",
            " 1000000]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Traning the linear Regression model on the whole dataset**"
      ],
      "metadata": {
        "id": "ASa5Jc2ZXin2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lin_reg=LinearRegression()\n",
        "lin_reg.fit(x,y)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G-3btKHJXo4V",
        "outputId": "cbd5748d-5d01-45eb-cdb1-7696c5181fc0"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Training the Polynomial Regression model on the whole dataset**"
      ],
      "metadata": {
        "id": "5hgSUz1ba831"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_reg = PolynomialFeatures(degree = 4)\n",
        "X_poly = poly_reg.fit_transform(X)\n",
        "lin_reg_2 = LinearRegression()\n",
        "lin_reg_2.fit(X_poly, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yB43IWTgbBVG",
        "outputId": "c51123b4-c634-4440-8dc7-307f51e78001"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Viuslization of the linear Regression Result**"
      ],
      "metadata": {
        "id": "3xRLoYyqYEnV"
      }
    },
    {
      "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": 295
        },
        "id": "YqMNF3MSYRiV",
        "outputId": "dd8998a3-2506-4f15-cf39-1153c866c6b4"
      },
      "execution_count": 8,
      "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",
      "source": [
        "**Viuslization of the polynomial Regression Result**"
      ],
      "metadata": {
        "id": "TipR-RfxZGfX"
      }
    },
    {
      "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": 295
        },
        "id": "FN5NupquZNam",
        "outputId": "de532282-4d77-4f52-bbfc-f1bfabae9862"
      },
      "execution_count": 14,
      "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",
      "source": [
        "**Predicting a new result with Linear Regression**"
      ],
      "metadata": {
        "id": "qlZLtB9IZnqG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lin_reg.predict([[6.5]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pd9M5e4CZpOe",
        "outputId": "4017d5a7-1221-4fbb-cffc-3cec7fb0ecfd"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([330378.78787879])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predicting a new result with Polynomial Regression**"
      ],
      "metadata": {
        "id": "U_VwM10Wacme"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lin_reg_2.predict(poly_reg.fit_transform([[6.5]]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xBG6qaI9aede",
        "outputId": "923416d0-180e-4cd9-e773-40b44ef805df"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([158862.45265155])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    }
  ]
}