Linear Regression Assignment

This repository contains files related to a linear regression assignment. The assignment involves implementing linear regression from scratch using Python and performing various tasks such as data cleaning, exploratory data analysis (EDA), correlation analysis, implementing the linear regression algorithm, and evaluating the model.

Files Included:

1. Dataset.csv: This file contains the dataset used for the linear regression assignment. It includes data on advertising budgets and sales.

2. Linear_Regression_Assignment.ipynb: This Jupyter Notebook file contains the implementation of linear regression, including data cleaning, EDA, correlation analysis, implementation of the linear regression algorithm, and evaluation of the model.

3. linear_regression.py: This Python script contains the implementation of linear regression algorithm.

4. requirements.txt: This file lists all the Python libraries required to run the code in the Jupyter Notebook. You can install these dependencies using the command pip install -r requirements.txt.

Usage:

To use this repository, follow these steps:

1. Clone the repository to your local machine: git clone https://github.com/gourav-rathi/implementing-linear-regression.git

2. Install the required dependencies using pip: pip install -r requirements.txt

3. Open and run the Jupyter Notebook Linear_Regression_Assignment.ipynb to execute the linear regression tasks.

Additional Notes:

• Make sure you have Python installed on your system.
• It's recommended to use a virtual environment to manage dependencies.
• For any issues or questions, feel free to contact the repository owner.

Disclaimer:

This repository is created for educational purposes and serves as an assignment submission. The data and code provided here may not be suitable for production use without further refinement and testing.