KSSRINIVASARAO/LinearRegression

linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called a simple linear regression. One of the most known and used algorithms that most of tech_specialist use and love is Linear Regression. Tried to solve it with a dataset

Linear Regression : Linear Regressoin is the most basic and popular algorithm of machine learning. It is supervised machine learning algorithm which is predicted output is real values/continuous. Or, Relationship between input/independent variables and single output/dependent variable. We will get linear regression image look like : -

Objective : To make a simple linear regression model operating on one variable from scratch using statistical formulas

Simple Linear Regression A linear line relationship between one input/independent variable(X) and one output/dependent variable(y).

y = mX + b Where, y is dependent/target variable; X is input/independent variable; m is slop of regression line; b is y-intercept.

Approach : The model is built in the following steps

Data Wrangling

Data Cleaning : Missing Values

Visualisation : UniVariate

• Histograms

• Scatter Plot

• Boxplot Parameter Functions :

• Mean and Variance

• Co-Variance

• Coefficients

• Splitting Test Data Model Building and Predictions

RMSE Function building and calculation