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Employee Salary Prediction using Decision Tree Classifier

This project aims to predict the salary of employees based on various features using a decision tree classifier algorithm. By analyzing the provided dataset, the model can make accurate predictions if the salaries of new employees is above 100K or not.

This repository contains the code and resources necessary to build and train the decision tree classifier model for employee salary prediction.

Features

  • Decision Tree Classifier: The project utilizes the decision tree classifier algorithm, a popular machine learning algorithm used for classification tasks, to predict the salary of employees.
  • Feature Engineering: The dataset is preprocessed and features are engineered to ensure optimal performance of the model. This includes handling missing values, categorical encoding, and feature scaling.
  • Model Evaluation: The performance of the trained model is evaluated using appropriate evaluation metrics such as accuracy, precision, recall, and F1 score.
  • Model Deployment: The trained model can be deployed in a production environment or integrated into an application to predict salaries based on input data.

Technologies Used

  • Python: The primary programming language used for building and training the decision tree classifier model.
  • Scikit-learn: A popular machine learning library in Python used for implementing the decision tree classifier algorithm and performing model evaluation.
  • Pandas: A data manipulation library used for data preprocessing and feature engineering tasks.
  • NumPy: A library for numerical computations used for handling and manipulating numerical data.

Getting Started

To get started with the Employee Salary Prediction using Decision Tree Classifier project, follow these steps:

  1. Clone this repository: git clone https://github.com/shaadclt/Employee-Salary-Prediction.git
  2. Navigate to the project directory: cd Employee-Salary-Prediction
  3. Open the jupyter notebook file: Employee Salary Prediction.ipynb
  4. Explore the generated results, including evaluation metrics and visualizations.

Note: Make sure you have Python and pip installed on your system before proceeding with the above steps.

Dataset

The dataset used for this project contains information about employees, including features such as company, job title, degree and salary.

Contributing

Contributions to this project are welcome and encouraged. If you have any ideas for improvements or would like to add new features, feel free to open an issue or submit a pull request. Make sure to follow the project's code style and adhere to best practices.

License

This project is licensed under the MIT License. You are free to use and modify the code as per the terms of the license.

Contact

If you have any questions or inquiries about this project, feel free to contact the project maintainer at shaadclt@gmail.com.