https://github.com/shaadclt/house-price-prediction-decisiontreeregressor
This project involves the prediction of house prices in Bengaluru city using Decision Tree Regression in Jupyter Notebook. Through this analysis, we aim to build a regression model that accurately predicts house prices based on the given input features.
https://github.com/shaadclt/house-price-prediction-decisiontreeregressor
decision-tree-regression
Last synced: 7 months ago
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This project involves the prediction of house prices in Bengaluru city using Decision Tree Regression in Jupyter Notebook. Through this analysis, we aim to build a regression model that accurately predicts house prices based on the given input features.
- Host: GitHub
- URL: https://github.com/shaadclt/house-price-prediction-decisiontreeregressor
- Owner: shaadclt
- Created: 2022-10-12T05:32:52.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-07-08T07:39:50.000Z (over 2 years ago)
- Last Synced: 2025-02-02T09:41:23.863Z (9 months ago)
- Topics: decision-tree-regression
- Language: Jupyter Notebook
- Homepage:
- Size: 144 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# House Price Prediction in Bengaluru City using Decision Tree Regression
This project involves the prediction of house prices in Bengaluru city using Decision Tree Regression in Jupyter Notebook. The dataset contains features such as location, total square feet area, number of bedrooms, bathrooms, and more. Through this analysis, we aim to build a regression model that accurately predicts house prices based on the given input features.
## Dataset
The house price dataset used for this analysis includes various features related to houses in Bengaluru city, such as location, total square feet area, number of bedrooms, number of bathrooms, and the target variable: house price.
## Prerequisites
Before running the code, make sure you have the following dependencies installed:
- Python (3.x)
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- scikit-learn
## Getting Started
To get started, follow the steps below:
1. Clone the repository:
```bash
git clone https://github.com/shaadclt/House-Price-Prediction-DecisionTreeRegressor.git
```
2. Change into the project directory:
```bash
cd House-Price-Prediction-DecisionTreeRegressor
```
3. Install the required dependencies:
4. Run Jupyter Notebook:
```bash
jupyter notebook
```
5. Open the `House Price Prediction.ipynb` notebook in Jupyter.
6. Run the notebook cells to load the dataset, perform data preprocessing, train the Decision Tree Regression model, and evaluate its performance.
## Analysis Overview
The notebook provides a step-by-step guide to predict house prices in Bengaluru city using Decision Tree Regression. The analysis includes the following tasks:
- Loading and understanding the dataset
- Data cleaning and preprocessing
- Feature selection and transformation
- Splitting the dataset into training and testing sets
- Training the Decision Tree Regression model
- Evaluating the model's performance using metrics such as mean squared error and R-squared score
- Making predictions on new data points
## Results and Insights
After training the model and evaluating its performance, you will gain insights into how well the Decision Tree Regression model predicts house prices based on the given input features. The notebook includes performance metrics and visualizations to assess the accuracy of the model. Feel free to refer to the notebook for detailed results and interpretations.
## Customization
You can customize the analysis to suit your specific requirements. For example, you can experiment with different feature engineering techniques, try different regression algorithms, or incorporate additional features from the dataset to improve the model's accuracy.
## License
This project is licensed under the MIT License. See the `LICENSE` file for more information.
## Acknowledgments
- This analysis is inspired by the need to predict house prices in Bengaluru city, which can be useful for real estate agents, buyers, and sellers in making informed decisions.
## Contributing
Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.