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https://github.com/gfav-cybergeek/prodigy_ml_01
A linear regression model to predict house prices based on square footage, number of bedrooms, and bathrooms. Includes feature engineering, preprocessing, and model evaluation.
https://github.com/gfav-cybergeek/prodigy_ml_01
ai airtificialintelligence algorithms algorithms-and-data-structures data-structures data-visualization jupyter jupyter-notebook jupyterlab machine-learning machine-learning-algorithms machine-learning-models python
Last synced: 28 days ago
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A linear regression model to predict house prices based on square footage, number of bedrooms, and bathrooms. Includes feature engineering, preprocessing, and model evaluation.
- Host: GitHub
- URL: https://github.com/gfav-cybergeek/prodigy_ml_01
- Owner: Gfav-CyberGeek
- Created: 2024-09-17T11:35:06.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-09-17T19:53:01.000Z (4 months ago)
- Last Synced: 2024-11-07T12:58:29.239Z (2 months ago)
- Topics: ai, airtificialintelligence, algorithms, algorithms-and-data-structures, data-structures, data-visualization, jupyter, jupyter-notebook, jupyterlab, machine-learning, machine-learning-algorithms, machine-learning-models, python
- Language: Jupyter Notebook
- Homepage:
- Size: 309 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# PRODIGY_ML_01
# House Price Prediction using Linear Regression
This repository contains the implementation of a linear regression model to predict the prices of houses based on their square footage, number of bedrooms, and bathrooms. The dataset is sourced from the [Kaggle House Prices: Advanced Regression Techniques](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data) competition, and additional features are used to improve model performance.
## Table of Contents
- [Overview](#overview)
- [Dataset](#dataset)
- [Installation](#installation)
- [Usage](#usage)
- [Model Details](#model-details)
- [Results](#results)
- [Future Improvements](#future-improvements)
- [Contributing](#contributing)
- [License](#license)
## Overview
This project implements a linear regression model to predict house prices using features such as:
- Square Footage (`1stFlrSF`)
- Number of Bedrooms (`BedroomAbvGr`)
- Number of Bathrooms (`FullBath`)
We extended the model by adding more features, including the neighborhood, house style, garage capacity, and living area, and applied data preprocessing techniques such as scaling and one-hot encoding to handle categorical variables.
## Dataset
The dataset is sourced from the Kaggle competition ["House Prices: Advanced Regression Techniques"](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data).
- **Features used**:
- `1stFlrSF` (First floor square footage)
- `GrLivArea` (Above-ground living area square footage)
- `BedroomAbvGr` (Number of bedrooms above ground)
- `FullBath` (Number of full bathrooms)
- `TotalBsmtSF` (Total basement square footage)
- `OverallQual` (Overall material and finish quality)
- Categorical features like `Neighborhood`, `HouseStyle`, etc.
- **Target variable**: `SalePrice` (House price in dollars)
## Installation
To get started with this project, clone the repository and install the necessary dependencies:
1. Clone the repository:
```bash
git clone https://github.com/Gfav-CyberGeek/PRODIGY_ML_01.git
cd PRODIGY_ML_01
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
## Usage
1. Download the dataset from the Kaggle competition [here](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data), and place the `train.csv` file in the project directory.
2. Run the Python script:
```bash
python model.py
```
3. The script will output the model's performance, including metrics such as Mean Squared Error (MSE) and R-squared (R²). Additionally, a scatter plot of actual vs predicted house prices will be displayed.
### Example Output
```
Mean Squared Error: 123456789.0
R-squared: 0.85
Cross-validated R-squared scores: [0.80, 0.84, 0.81, 0.83, 0.82]
Average cross-validated R-squared: 0.82
```
## Model Details
- **Preprocessing**: The dataset contains both numeric and categorical features. We used `StandardScaler` to scale numeric features and `OneHotEncoder` for categorical features.
- **Pipeline**: A scikit-learn pipeline was created to handle preprocessing and model training in a single step.
- **Model**: A simple linear regression model was trained on the selected features. We experimented with both basic and extended features to improve the model's performance.
### Features Used
- `1stFlrSF`: First floor square footage
- `GrLivArea`: Above-ground living area square footage
- `BedroomAbvGr`: Number of bedrooms above ground
- `FullBath`: Number of full bathrooms
- `TotalBsmtSF`: Total basement square footage
- `OverallQual`: Overall material and finish quality (ordinal)
- `Neighborhood`: Categorical, represents house neighborhood
- `HouseStyle`: Categorical, represents house architectural style
- `GarageCars`: Number of cars that can fit in the garage
## Results
The linear regression model performs reasonably well, with an R-squared score of around 0.82 after cross-validation. The model is able to capture the relationship between house prices and key features such as square footage, number of bedrooms, and quality indicators.
A scatter plot of actual vs predicted prices is generated to visualize the model's performance.
## Future Improvements
1. **Feature Engineering**: Further engineering of features such as creating interaction terms between key variables (e.g., `OverallQual` and `GrLivArea`).
2. **Advanced Models**: Explore more advanced models such as Ridge, Lasso, or Decision Trees to improve accuracy.
3. **Hyperparameter Tuning**: Use `GridSearchCV` or `RandomizedSearchCV` to fine-tune hyperparameters for better performance.
4. **Handling Missing Data**: Explore more sophisticated imputation techniques or drop features with a significant amount of missing data.
## Contributing
Contributions are welcome! Feel free to submit a pull request or open an issue if you have any suggestions or find any bugs.
1. Fork the repository
2. Create a feature branch (`git checkout -b feature-branch`)
3. Commit your changes (`git commit -am 'Add new feature'`)
4. Push to the branch (`git push origin feature-branch`)
5. Open a pull request
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.