Salary Prediction Using Machine Learning

This project aims to predict salaries based on years of experience using machine learning techniques. The dataset contains two columns: yearsexperience and salary.

Table of Contents

• Getting Started
  • Prerequisites
  • Installation
• Usage
• Model Building
• Results
• Contributing
• License

Getting Started

Prerequisites

To run this project, you need to have Python and the following libraries installed:

• pandas
• numpy
• scikit-learn
• plotly

You can install them using the following command:

pip install pandas numpy scikit-learn plotly

Installation

1. Clone the repository:

git clone https://github.com/Mohshaikh23/Salary-Prediction-with-Machine-Learning.git
cd salary-prediction

2. Launch Jupyter Notebook:

jupyter notebook

Usage

The Jupyter Notebook in this repository contains the code for data preprocessing, model building, and evaluation. Open the notebook and follow the step-by-step instructions to run the code and see the results.

Model Building

We use a simple linear regression model to predict salaries based on years of experience. The dataset is split into training and testing sets, and the model is trained using the training data. The performance of the model is evaluated on the testing data.

Results

The trained model's performance is evaluated using metrics such as Mean Squared Error (MSE) and R-squared. The model's predictions are visualized using scatter plots.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, feel free to open an issue or create a pull request.

License

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