Housing Price Prediction Project 🏠

This project aims to predict housing prices using a Decision Tree Algorithm. It was developed as part of a Udacity Nanodegree course to demonstrate the fundamentals of machine learning and predictive modeling.

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⚙️ Project Overview

The core of this project is a machine learning model that can estimate a house's value based on a given set of features. The model is built using the Decision Tree Regression algorithm, a supervised learning method used for predicting continuous values.

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📂 Project Structure

• housing_data.csv: This file contains the dataset used to train and test the model. It includes various features like square footage, number of bedrooms, and location.
• housing_price_prediction.ipynb: A Jupyter Notebook containing all the code for data preprocessing, model training, evaluation, and prediction.
• README.md: This file, providing an overview of the project.

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🤖 Model Details

Decision Tree Algorithm

A Decision Tree is a flowchart-like structure where each internal node represents a test on an attribute (e.g., is the number of bedrooms greater than 3?), each branch represents the outcome of the test, and each leaf node represents the predicted value (in this case, the house price).

The model learns to make these decisions by analyzing the provided training data, creating a set of rules that lead to a final prediction.

Training and Evaluation

The dataset is split into a training set and a testing set. The model is trained on the training data to learn the patterns between the features and the house prices. Once trained, its performance is evaluated on the unseen testing data to assess its accuracy.

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🚀 How to Run the Project

1. Clone the Repository:

   git clone https://github.com/satishf889/housing-price-prediction.git

2. Install Required Libraries:

   pip install pandas scikit-learn jupyter matplotlib seaborn

3. Open the Jupyter Notebook:

   jupyter notebook housing_price_prediction.ipynb

4. Run the Notebook Cells: Execute all the cells in the notebook sequentially to see the data loading, model training, and prediction process.

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📈 Results and Conclusion

The trained model provides predictions for housing prices. The notebook includes visualizations that show the model's performance and allows for new predictions based on user-defined inputs. This project serves as a foundational example of using machine learning for predictive analysis on real-world data.