Salary Prediction based on Position using Support Vector Regression

This project involves the prediction of salary based on position using Support Vector Regression (SVR) in Jupyter Notebook. The dataset contains information about different positions and their corresponding salaries. Through this analysis, we aim to build a regression model that accurately predicts the salary based on the given position.

Dataset

The salary dataset used for this analysis includes information about different positions and their corresponding salaries.

Prerequisites

Before running the code, make sure you have the following dependencies installed:

• Python (3.x)
• Jupyter Notebook
• Pandas
• NumPy
• Matplotlib
• scikit-learn

Getting Started

To get started, follow the steps below:

1. Clone the repository:

git clone https://github.com/shaadclt/Salary-Prediction-SupportVectorRegressor.git

2. Change into the project directory:

cd Salary-Prediction-SupportVectorRegressor

3. Install the required dependencies:

4. Run Jupyter Notebook:

jupyter notebook

5. Open the Salary Prediction.ipynb notebook in Jupyter.

6. Run the notebook cells to load the dataset, perform data preprocessing, train the Support Vector Regression model, and evaluate its performance.

Analysis Overview

The notebook provides a step-by-step guide to predict salary based on position using Support Vector Regression (SVR). The analysis includes the following tasks:

• Loading and understanding the dataset
• Data cleaning and preprocessing
• Feature selection and transformation
• Splitting the dataset into training and testing sets
• Training the SVR model
• Evaluating the model's performance using metrics such as mean squared error and R-squared score
• Making predictions on new data points

Results and Insights

After training the model and evaluating its performance, you will gain insights into how well the Support Vector Regression model predicts salary based on the given position. The notebook includes performance metrics and visualizations to assess the accuracy of the model. Feel free to refer to the notebook for detailed results and interpretations.

Customization

You can customize the analysis to suit your specific requirements. For example, you can experiment with different feature engineering techniques, try different regression algorithms, or incorporate additional features from the dataset to improve the model's accuracy.

License

This project is licensed under the MIT License. See the LICENSE file for more information.

Acknowledgments

• This analysis is inspired by the need to predict salary based on position, which can be useful for HR departments, recruiters, and employees in negotiating compensation packages.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.