🧬 Life Expectancy Prediction using Random Forest Regression

📌 Overview

This project predicts life expectancy based on various health and lifestyle factors using a Random Forest Regression Model. It processes user-inputted health data, applies machine learning for prediction, and provides personalized health advisory tips based on individual conditions.

The app is built using Flask, and it features a web interface with multiple pages to guide users through the prediction process.

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✨ Features

• ✅ Predicts Life Expectancy based on health & lifestyle attributes
• ✅ Machine Learning Model: Random Forest Regressor
• ✅ Dynamic Health Advisory System with personalized tips
• ✅ Interactive Flask Web App with user-friendly interface
• ✅ Data Preprocessing & Model Training with real-world health data
• ✅ Custom age-group-based prediction rules for improved accuracy

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📊 Dataset

The project utilizes a pre-processed CSV dataset (modified_life_expectancy_dataset.csv) that includes:

• 📌 Health Parameters: Age, BMR, Blood Pressure, Height, Weight, etc.
• 📌 Disease History: Diabetes, Cancer, HIV, Stroke, Heart Disease, etc.
• 📌 Lifestyle Factors: Smoking, Alcohol Consumption

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⚙️ Model Implementation

🔹 Data Preprocessing & Feature Engineering

• Handles missing values
• Encodes categorical variables
• Selects relevant features for better model accuracy

🔹 Feature Selection

The model uses 16 key features, including:

• 🏥 Health Factors: Age, Blood Pressure, Height, Weight, BMR
• ⚕️ Diseases: Diabetes, Stroke, Kidney Failure, Tuberculosis, HIV, Cancer
• 🚬 Lifestyle: Smoking, Alcohol Consumption

🔹 Model Training & Prediction

• 🎯 Random Forest Regression model is trained on historical health data.
• 📈 Performance Metrics:
  • Mean Squared Error (MSE)
  • Root Mean Squared Error (RMSE)
  • R² Score for model accuracy evaluation

🔹 Health Advisory System

• 🏥 Personalized health tips are generated based on user inputs to provide lifestyle recommendations and disease management advice.

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🖥️ Web Application Workflow

🌐 App Structure

The project includes four main pages in the Flask web app:

1️⃣ age_group.html → User selects an age group for tailored predictions.
2️⃣ index.html → Inputs health parameters (age, BMI, disease history, etc.).
3️⃣ result.html → Displays predicted life expectancy with model accuracy scores.
4️⃣ healthtips.html → Provides personalized health recommendations based on user inputs.