https://github.com/prajakta1321/kenko
Kenko is a small self-research-based project that predicts individual health consumption patterns using demographic and lifestyle data.
https://github.com/prajakta1321/kenko
data-science healthcare-application logistic-regression machine-learning machine-learning-algorithms python3 research-project svm-classifier
Last synced: 6 months ago
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Kenko is a small self-research-based project that predicts individual health consumption patterns using demographic and lifestyle data.
- Host: GitHub
- URL: https://github.com/prajakta1321/kenko
- Owner: prajakta1321
- Created: 2023-08-11T07:17:13.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-31T13:42:08.000Z (12 months ago)
- Last Synced: 2024-10-31T14:37:01.579Z (12 months ago)
- Topics: data-science, healthcare-application, logistic-regression, machine-learning, machine-learning-algorithms, python3, research-project, svm-classifier
- Language: Jupyter Notebook
- Homepage:
- Size: 9.99 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
🏥 ***Kenko: Individual’s Health Consumption Prediction Using Machine Learning***
📖 **Project Overview:**
🎯 Kenko is a small self-research-based machine learning project aimed at predicting an individual’s health consumption patterns based on various factors, including demographic data, lifestyle choices, and medical history. This project seeks to provide insights that can aid in personalized healthcare recommendations and improve resource allocation within healthcare systems.
✅ The data collection for this project was conducted independently and was not sourced from any external databases.
🛠️ **Technologies Used**
✅ Programming Language: Python
✅ Machine Learning Libraries: Scikit-learn, Pandas, NumPy
✅ Data Visualization: Matplotlib, Seaborn
✅ Development Environment: Jupyter Notebook, Google Colab
📊 **Data Collection**
The project utilizes a self created dataset containing information on health consumption behaviors, including:
Demographic details (age, gender)
Lifestyle choices (diet, exercise, smoking, etc.)
🧠 **Machine Learning Techniques**
The project implements various machine learning algorithms, including:
✅ Regression Models: To estimate health consumption costs based on input features.
✅ Clustering: To identify patterns and segments in health consumption behavior.
🔍 **Key Features**
Predictive analytics to forecast future health consumption.
Visualization of data trends and insights for better understanding.
A user-friendly interface for healthcare professionals to make informed decisions.