Salary Prediction Using Linear Regression

This repository hosts a project that applies linear regression to predict salaries based on years of experience. It's tailored for execution in a Google Colab environment and utilizes Python's data science libraries to demonstrate data preparation, model training, statistical analysis, and visualization.

Table of Contents

• Project Overview
• Features
• Data Description
• Dependencies
• Installation
• Usage
• Contributing
• License
• Contact

Project Overview

"SalaryPredictionLinearReg" is designed to predict salaries using linear regression techniques. The project demonstrates the entire workflow from data loading to model evaluation, focusing on hands-on application and detailed statistical analysis using Python.

Features

• Data Loading and Cleaning: Using pandas for efficient data handling.
• Model Training: Implementing linear regression with scikit-learn.
• Statistical Analysis: Utilizing statsmodels for in-depth analysis.
• Visualization: Creating insightful plots with matplotlib and seaborn.
• Model Evaluation: Employing regression diagnostic tools.

Data Description

The dataset features:

• YearsExperience: The number of years of professional experience.
• Salary: The annual salary associated with the years of experience.

Dependencies

To run this notebook, the following Python libraries are required:

• pandas
• numpy
• statsmodels
• matplotlib
• seaborn
• scikit-learn
• scipy

Installation

1. Open Google Colab and connect to your Google Drive:

   from google.colab import drive
   drive.mount('/content/drive')

2. Clone the repository into your drive:

   !git clone https://github.com/ascender1729/SalaryPredictionLinearReg.git
   cd SalaryPredictionLinearReg

3. Install the required dependencies:

   !pip install pandas numpy statsmodels scikit-learn matplotlib seaborn scipy

Usage

Navigate to the cloned repository directory in your Google Colab and open the Jupyter Notebook. Follow the steps outlined in the notebook to run the analysis from data preprocessing to model evaluation.

Contributing

Contributions to enhance the analysis or improve the model are welcome. Please fork the repository, make your proposed changes, and submit a pull request for review.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

Pavan Kumar - pavankumard.pg19.ma@nitp.ac.in

LinkedIn: Pavan Kumar

Project Link: SalaryPredictionLinearReg