This GitHub repository contains scripts for simple linear regression and data processing in R. The repository includes two main R scripts:

1. SimpleLinearRegression.R: This script demonstrates the implementation of simple linear regression. It uses the Salary_Data.csv dataset, splitting it into training and test sets, creating a linear regression model based on years of experience, and making predictions. Visualization of the predicted vs. actual salary for both the training and test sets is included.

2. data_processing_template.R: This script provides a template for data pre-processing. It imports the Data.csv dataset, handles missing data by imputing mean values, encodes categorical variables, and splits the dataset into training and test sets. Additionally, it performs feature scaling by standardizing numerical features.

To use the simple linear regression script:

1. Make sure you have the required libraries installed: readr, caTools, ggplot2.
2. Ensure that the Salary_Data.csv dataset is in the same directory as the script.
3. Run the script to execute the simple linear regression analysis.

To use the data processing template:

1. Make sure you have the required library installed: caTools.
2. Ensure that the Data.csv dataset is in the same directory as the script.
3. Run the script to perform data pre-processing, including handling missing data, encoding categorical variables, and feature scaling.

Feel free to customize and adapt the scripts based on your specific datasets and requirements. For any issues or suggestions, please open an issue on the repository.

Happy coding!