Implementation Of Multiple Linear Regression In Google Colab

Linear regression is a statistical method used for predictive analysis. It models the relationship between a dependent variable and a single independent variable by fitting a linear equation to the data. Multiple Linear Regression extends this concept by modelling the relationship between a dependent variable and two or more independent variables. This technique allows us to understand how multiple features collectively affect the outcomes. Steps to perform multiple linear regression are similar to that of simple linear Regression but difference comes in the evaluation process. We can use it to find out which factor has the highest influence on the predicted output and how different variables are related to each other.

Equation for multiple linear regression is: y = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \cdots + \beta_n X_n The goal of the algorithm is to find the best fit line equation that can predict the values based on the independent variables. A regression model learns from the dataset with known X and y values and uses it to predict y values for unknown X. In multiple regression model we may encounter categorical data such as gender (male/female), location (urban/rural), etc. Since regression models require numerical inputs then categorical data must be transformed into a usable form.

This is where Dummy Variables used. These are binary variables (0 or 1) that represent the presence or absence of each category. For example: There was an error while loading. Please reload this page. This document discusses modeling via multiple linear regression, and the tools in pandas and sklearn that can assist with this.

If you do not have the sklearn library installed then you will need to run in the Jupyter/Colab terminal to install. Remember: you only need to install once per machine (or Colab session). Recall that in machine learning our goal is to predict the value of some target variable using one or more predictor variables. Mathematically, we we’re in the following setup where \(y\) is our target variable and \(X\) represents the collection (data frame) of our predictor variables.

To predict \(y\) well we need to estimate \(f\) well. We will see many different ways to estimate \(f\) including those methods mentioned in our previous modeling introduction: This repository contains a Google Colab notebook that covers the concepts and implementations of linear regression and multiple linear regression, including applications using neural networks with TensorFlow. This project aims to provide a thorough understanding of linear regression models. It spans from basic theoretical concepts to practical implementations, incorporating advanced techniques using neural networks. The notebook includes the following sections:

Regression Neural Network using TensorFlow

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