This repository contains Python code for comparing the performance of a Decision Tree Regressor and a Linear Regression model on a dataset.

• Introduction
• Setup Instructions
• Usage
• File Descriptions
• Results
• License

This project aims to evaluate the effectiveness of two different machine learning models, a Decision Tree Regressor and a Linear Regression model, on a given dataset. The models are trained, evaluated using metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared score, and compared based on their performance.

To run the code in this repository, follow these steps:

1. Clone the repository:

   git clone https://github.com/KanishkThamman/India-Housing
   cd India-Housing

2. Install the required dependencies: Ensure you have Python installed. Install necessary libraries using pip:

   pip install pandas matplotlib scikit-learn

3. Run the Jupyter Notebook or Python script: Execute the main script or open the Jupyter Notebook:

   jupyter notebook main.ipynb

1. Modify the dataset path or data loading code in the notebook/script (main.ipynb) to use your own dataset.
2. Adjust hyperparameters or add more models for comparison as needed.
3. Run cells sequentially to train models, tune hyperparameters, evaluate performance, and visualize results.

• main.ipynb: Jupyter Notebook containing Python code for model training, hyperparameter tuning, evaluation, and visualization.
• README.md: This file, providing an overview of the project and instructions for usage.
• House Price India.csv: This file provides the data.

• Achieved an R-squared score of 0.2.
• Hyperparameters optimized using GridSearchCV.
• Visualized decision tree structure.

• Achieved an R-squared score of 0.5 (update with your actual score).
• Provided a baseline comparison against the Decision Tree model.

This project is licensed under the MIT License