This project uses a Linear Regression model to predict house prices based on median income. By analyzing housing-related data, it establishes a relationship between income levels and property prices, helping users estimate housing costs.
🔹 Key Features:
✅ Data-Driven Predictions using real housing data.
✅ Interactive Web App with Streamlit.
✅ Visualization & Web Deployment.
🔹 Technologies: Python, scikit-learn, Streamlit, pandas, numpy.
🔹 How It Works:
User Input: Users enter their median income.
Model Prediction: The trained model predicts the house price.
Visualization: A line chart shows price variations.
Results: The predicted price is displayed.
This interactive Streamlit web app allows users to enter their body measurements and calculates their BMI (Body Mass Index). The app provides dynamic visualizations by resizing a silhouette image based on the user's BMI, giving a clear representation of body proportions.
🛠️ Features:
✅ User-friendly number inputs for height, weight, and body circumferences
✅ BMI Calculation with real-time feedback on health status
✅ Dynamic Image Resizing based on BMI for better visualization
✅ Health Tips based on BMI category (Underweight, Normal, Overweight, Obese)
🚀 Use Case:
This tool is ideal for individuals tracking their fitness journey, nutritionists, or health professionals who need a quick and easy way to assess body measurements and BMI.
Feb 22, 2025
digit classifier
This project uses a neural network to classify handwritten digits. It demonstrates the power of deep learning in image recognition tasks.