Linear Regression |  Advanced

This project implements linear regression using a dataset to predict a dependent variable based on an independent variable. It involves data preprocessing, making predictions, and visualizing results using matplotlib. The final output is a scatter plot with a fitted regression line, showcasing the model’s accuracy.

Important

When refering in the project about theta0($\theta_0$) and theta1($\theta_1$) in the project:

• theta0($\theta_0$) is the intercept, and can be used interchangeably with the term "intercept." It represents the value of 𝑦 when 𝑥=0.
• theta1($\theta_1$) is the slope, and can be used interchangeably with the term "slope." It represents how much 𝑦 changes for each unit increase in 𝑥.

Install Dependencies

. ./install.sh

Linear Regression Program

Computes a Linear Regression using Gradient Descend Algorithm with the dataset specified.

• use --dataset or -d to specify a dataset
• use --graphical or -g to open a graph window with the result
• use --output or -o to save the result in a json format file

for more information about options use:

python3 linear_regression.py -h

Try:

python3 linear_regression.py --dataset datasets/data.csv --graphical

Linear Predictor Program

This program calculates the predicted value of Y based on a given X value using a simple linear equation ( Y = $theta_0$ + $theta_1$ · X )

• use --theta0 or -t0 to specify the theta0 or intercept
• use --theta1 or -t1 to specify the theta1 or slope
• use --json or -j for input a json with theta0 and theta1 result from the previous program

for more information about options use:

python3 linear_predictor.py -h

Try:

python3 linear_predictor.py --theta0 8474.34137591075 --theta1 -0.021199045602042395