https://github.com/rafiqamar/weight-change-prediction-app
An app leveraging machine learning to estimate weight changes based on lifestyle inputs, offering actionable feedback.
https://github.com/rafiqamar/weight-change-prediction-app
exploratory-data-analysis git machine-learning-algorithms python streamlit-webapp
Last synced: 3 months ago
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An app leveraging machine learning to estimate weight changes based on lifestyle inputs, offering actionable feedback.
- Host: GitHub
- URL: https://github.com/rafiqamar/weight-change-prediction-app
- Owner: RafiQamar
- Created: 2024-11-09T14:18:43.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-03T18:56:15.000Z (over 1 year ago)
- Last Synced: 2025-05-20T05:08:39.113Z (about 1 year ago)
- Topics: exploratory-data-analysis, git, machine-learning-algorithms, python, streamlit-webapp
- Language: Jupyter Notebook
- Homepage: https://weight-change-prediction-app.streamlit.app/
- Size: 2.48 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Weight Change Prediction App
An interactive web app built with Streamlit that predicts weight change based on user input, such as age, caloric intake, activity level, sleep quality, and more. It utilizes a machine learning model to provide personalized advice and insights based on the predicted weight change.
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## Features
* Predict weight change based on user input of health metrics and lifestyle factors
* Tailored insights and advice for health improvement based on prediction
* User-friendly interface with adjustable parameters for calorie intake, activity level, and stress level
* Scalable design, supporting further enhancements or additional health metrics
## How to Use the App
* Input your data: Enter your age, gender, current weight, BMR, physical activity level, and other metrics.
* View predictions: The model will output a predicted weight change along with personalized advice.
* Interpret the advice: Use the insights to make informed decisions on lifestyle changes.
## Model and Data
* The model used is trained on a dataset with factors like caloric intake, stress level, and sleep quality.
* Weight change prediction is based on a Yeo-Johnson transformed regression model.