ch10_polynomial_regression_template.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "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/ThatNinjaGuy/Machine-learning-A-Z-Course/blob/main/ch10_polynomial_regression_template.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": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6c8YExmOD5x5"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQOdXhjXD_AE"
      },
      "source": [
        "dataset = pd.read_csv('ENTER_THE_NAME_OF_YOUR_DATASET_HERE.csv')\n",
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ud_1XTb28iXH"
      },
      "source": [
        "## Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bUib_pbl8ipB"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rb5nWuSHEfBV"
      },
      "source": [
        "## Training the Polynomial Regression model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HYplp4pTEm0O"
      },
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.linear_model import LinearRegression\n",
        "poly_reg = PolynomialFeatures(degree = 4)\n",
        "X_poly = poly_reg.fit_transform(X_train)\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_poly, y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pzF3BRps9nlk"
      },
      "source": [
        "## Predicting the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "36aFLFBK9pMk"
      },
      "source": [
        "y_pred = regressor.predict(poly_reg.transform(X_test))\n",
        "np.set_printoptions(precision=2)\n",
        "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fz1uTlWV919-"
      },
      "source": [
        "## Evaluating the Model Performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvZQ_4W893-e"
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_score(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}