sshabzz/final_project

Final Boot Camp Project

Salary Prediction Using Machine Learning

This project focuses on predicting salary based on various factors using machine learning techniques. It serves as the final project for the Ironhack bootcamp.

Table of Contents

• Project Overview
• Dataset
• Installation
• Usage
• Results
• Contributing
• Credits

Project Overview

The goal of this project is to develop accurate models that can predict salary based on occupation, education, and experience-related features. It involves data preprocessing, feature engineering, model selection, evaluation, and tuning.

The main objectives of the project are:

• Exploring and understanding the dataset
• Performing data cleaning and preprocessing
• Applying various machine learning algorithms for salary prediction
• Evaluating and comparing the performance of different models
• Selecting the best-performing model and fine-tuning it
• Providing insights and recommendations based on the results

Dataset

The dataset used for this project is a pre-processed and cleaned dataset obtained from reliable sources. It contains information about monthly salaries and related attributes of individuals. The features include gender, education level, years of experience, and more.

Installation

To run the project locally, please follow these steps:

1. Clone the repository to your local machine.
2. Install the required dependencies by running the following command: pip install -r requirements.txt.

Usage

1. Make sure you have the necessary data file, salary_cleaned.csv, placed in the appropriate location.
2. Run the main code file, salary_prediction.py, to train and evaluate the models.
3. View the results, including MAE, MSE, and R-squared scores, printed in the console.
4. Explore the code and modify it as needed for further analysis or experimentation.

Results

The project evaluates and compares the performance of three machine learning models: Gradient Boosting, K-Nearest Neighbors, and Random Forest. The key evaluation metrics include Mean Absolute Error (MAE), Mean Squared Error (MSE), and Coefficient of Determination (R-squared).

Based on the evaluation, the best-performing model for salary prediction is the Gradient Boosting model, which achieved the lowest MAE and MSE values and the highest R-squared score on the testing set.

Contributing

Contributions to this project are welcome. If you have any suggestions, improvements, or bug fixes, please open an issue or submit a pull request.

Credits

This analysis was conducted by Shahbaz Mazhar With the guidance from Ignacio, Lukaz, Sandra and my amazing fellow classmates.