https://github.com/zainashrafofficial/ml_project_2024

A machine learning semester project of university, implementing classification and regression models to predict house prices & number of rooms using a dataset with over 160,000 entries.
https://github.com/zainashrafofficial/ml_project_2024

ai ann artificial-neural-networks classification decision-trees jupyter-notebook linear-regression logistic-regression matplotlib ml numpy pandas python random-forest regression-models svm tensorflow

Last synced: 12 days ago
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A machine learning semester project of university, implementing classification and regression models to predict house prices & number of rooms using a dataset with over 160,000 entries.

• Host: GitHub
• URL: https://github.com/zainashrafofficial/ml_project_2024
• Owner: zainashrafofficial
• License: mit
• Created: 2025-01-30T12:58:46.000Z (13 days ago)
• Default Branch: main
• Last Pushed: 2025-01-30T13:15:34.000Z (13 days ago)
• Last Synced: 2025-01-30T14:19:16.809Z (13 days ago)
• Topics: ai, ann, artificial-neural-networks, classification, decision-trees, jupyter-notebook, linear-regression, logistic-regression, matplotlib, ml, numpy, pandas, python, random-forest, regression-models, svm, tensorflow
• Language: Jupyter Notebook
• Homepage: https://zainashrafofficial.com
• Size: 7.79 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# House Prices & Number of Rooms Prediction

## Machine Learning Semester Project University

## Overview

This repository contains a Machine Learning project focusing on **Classification and Regression** techniques for predicting house prices. The dataset consists of **160,000+ rows**, offering a comprehensive foundation for analyzing various house price trends and factors influencing them.

## Project Scope

This project is divided into two main sections:

1. **Classification:** Number of Rooms Prediction

2. **Regression:** Predicting the exact price of houses using multiple regression models.

## Dataset

The dataset comprises **160,000+ records** with various attributes such as:

- Square footage

- Number of bedrooms & bathrooms

- Location

- And many more features influencing house prices.

## Methodologies Used

- **Data Preprocessing:** Handling missing values, feature engineering, and scaling.

- **Exploratory Data Analysis (EDA):** Visualizing and understanding key patterns.

- **Feature Selection:** Identifying significant features impacting house prices.

- **Model Training:**

  - **Classification Models:** Decision Trees, Random Forest, Logistic Regression, Artificial Neural Network(ANN), etc.

  - **Regression Models:** Linear Regression, Decision Tree, Random Forest, Artificial Neural Network(ANN), etc.

- **Model Evaluation:** Using metrics such as R² Score, F1 Score Accuracy, Precision, and Recall.

## Installation & Usage

### Prerequisites

Ensure you have the following installed:

- Python

- Jupyter Notebook

- Required libraries: `numpy`, `pandas`, `scikit-learn`, `matplotlib`, `seaborn`

### Setup

Clone the repository:

```bash

git clone https://github.com/zainashrafofficial/ML_Project_2024.git

cd ML_Project_2024

``` 

## Future Improvements

- Implementing deep learning models for improved accuracy.

- Incorporating real-time housing market data for better predictions.

- Optimizing feature engineering for enhanced performance.

## Contributions

Feel free to contribute by submitting issues or pull requests!

## License

This project is licensed under the MIT License.