Salary Prediction

This project demonstrates a basic implementation of Simple Linear Regression to predict salaries based on years of experience. It uses the Salary Dataset from Kaggle.

Features

• Data Preprocessing: The dataset is loaded and converted into NumPy arrays.
• Model Implementation: Simple Linear Regression is implemented from scratch using Python.
• Gradient Descent: The model uses gradient descent to optimize weights.
• Visualization: Graphs of actual vs. predicted salaries are included.
• Model Evaluation: Key metrics such as cost function and ( R^2 ) score are calculated.

Project Structure

The project consists of a single Jupyter Notebook file:

• main.ipynb: Contains the entire implementation of the project, including loading data, model training, visualization, and evaluation.

Dataset

The dataset is sourced from Kaggle: Salary Dataset
It contains two columns:

1. YearsExperience: Years of work experience.
2. Salary: Corresponding salary.

Requirements

• Python 3.7+
• Jupyter Notebook
• Required Libraries:
  • pandas
  • numpy
  • matplotlib

How to Run

1. Clone this repository:

   git clone https://github.com/KartikAg13/simple_linear_regression.git

2. Navigate to the project directory:

   cd simple_linear_regression

3. Open the Jupyter Notebook:

   jupyter notebook main.ipynb

4. Follow the cells to run the project step by step.

Results

• Optimal Parameters: The model finds the best values for weight (( w )) and bias (( b )) using gradient descent.
• Prediction Visualization: A graph is plotted showing the regression line (predicted) vs. actual data.
• Evaluation: The cost function and ( R^2 ) score are calculated to assess the model's performance.

Example Output

Contributing

This is a basic project designed for educational purposes. Feel free to fork and extend it! Contributions are welcome.

Acknowledgments

• Kaggle for providing the dataset.
• Coursera for inspiration through the Machine Learning course.