https://github.com/shaadclt/height-prediction-polynomialregression
This project involves predicting height based on age using polynomial regression in Jupyter Notebook. Polynomial regression is a variation of linear regression that models the relationship between the independent variable (age) and the dependent variable (height) as an nth-degree polynomial.
https://github.com/shaadclt/height-prediction-polynomialregression
linear-regression polynomial-features polynomial-regression
Last synced: 6 months ago
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This project involves predicting height based on age using polynomial regression in Jupyter Notebook. Polynomial regression is a variation of linear regression that models the relationship between the independent variable (age) and the dependent variable (height) as an nth-degree polynomial.
- Host: GitHub
- URL: https://github.com/shaadclt/height-prediction-polynomialregression
- Owner: shaadclt
- Created: 2022-10-19T09:44:45.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-07-08T11:31:32.000Z (about 2 years ago)
- Last Synced: 2025-04-10T01:12:15.341Z (6 months ago)
- Topics: linear-regression, polynomial-features, polynomial-regression
- Language: Jupyter Notebook
- Homepage:
- Size: 6.84 KB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Height Prediction using Polynomial Regression
This project involves predicting height based on age using polynomial regression in Jupyter Notebook. Polynomial regression is a variation of linear regression that models the relationship between the independent variable (age) and the dependent variable (height) as an nth-degree polynomial. Through this project, we aim to explore and understand how polynomial regression can be used to predict height based on age.
## Dataset
The dataset used for this project consists of height and age measurements. The dataset should be in a CSV (Comma Separated Values) format with two columns: "Height" and "Age". Each row represents a sample with the corresponding height and age values. Make sure to preprocess and clean the dataset before using it for modeling.
## Getting Started
To get started with the project, follow the steps below:
1. Clone the repository:
```bash
git clone https://github.com/shaadclt/Height-Prediction-PolynomialRegression.git
```
2. Change into the project directory:
```bash
cd Height-Prediction-PolynomialRegression
```
3. Install the required dependencies:
4. Place your preprocessed dataset in the project directory.
5. Run Jupyter Notebook:
```bash
jupyter notebook
```
6. Open the `Height Prediction.ipynb` notebook in Jupyter.
7. Follow the instructions in the notebook to load the dataset, preprocess the data, train the polynomial regression model, and make predictions.
## Project Overview
The notebook provides an overview of the steps involved in height prediction using polynomial regression. The steps include:
1. Data Loading: Loading the dataset into a pandas DataFrame.
2. Data Preprocessing: Handling missing values, removing outliers, and splitting the dataset into training and testing sets.
3. Polynomial Regression: Fitting the polynomial regression model to the training data.
4. Model Evaluation: Evaluating the model performance using metrics such as mean squared error (MSE) or R-squared.
5. Prediction: Using the trained model to make height predictions on new data.
The notebook includes explanations, code snippets, and visualizations to aid in understanding the height prediction process using polynomial regression.
## Results and Insights
The project aims to predict height based on age using polynomial regression. The results and insights gained from this project include:
- Understanding the relationship between age and height through visualizations.
- Assessing the performance of the polynomial regression model using evaluation metrics.
- Interpreting the model coefficients to understand the impact of age on height.
The insights gained from this project can help understand the relationship between age and height and provide predictions for height based on age.
## Customization
You can customize the project by modifying the dataset, exploring different degrees of polynomial regression, or adding additional analyses. This project serves as a starting point for predicting height based on age using polynomial regression, and you can extend it further to suit your needs.
## License
This project is licensed under the MIT License. See the `LICENSE` file for more information.
## Acknowledgments
- This project is created for the purpose of exploring height prediction using polynomial regression in Jupyter Notebook.
## Contributing
Contributions are welcome! If you find any issues, have suggestions for improvements, or want to add more features, please open an issue or submit a pull request.