Life Expectancy Predictor

A real-time application that estimates a person's life expectancy based on age and gender predictions from facial images. This project leverages deep learning models for age and gender detection and integrates a pre-trained regression model to dynamically predict life expectancy.

Features

• Real-time Face Detection: Utilizes OpenCV's deep learning-based face detector to locate faces in video streams.
• Age and Gender Prediction: Implements deep learning models to predict the age and gender of the detected face.
• Life Expectancy Estimation: Uses a pre-trained regression model to estimate life expectancy based on the predicted age and gender.
• Dynamic Interface: Displays the predicted age, gender, and life expectancy in a visually appealing and responsive interface.

Data Preparation and Model Training

1. Data Source:

   • The dataset used for training the life expectancy model is sourced from Kaggle, titled "Life Expectancy Data.csv".

2. Data Processing:

   • Data Cleaning:
     • Removed unnecessary spaces from column names.
     • Checked and handled missing values by either dropping rows or filling them with mean values.
   • Feature Engineering:
     • Encoded categorical variables such as country status (Developing or Developed).
     • Selected relevant features for the prediction model, excluding non-predictive columns like Country and Year.

3. Model Training:

   • Model Selection:
     • Used RandomForestRegressor from scikit-learn for its robustness and accuracy in regression tasks.
   • Training and Evaluation:
     • Split the data into training and testing sets.
     • Trained the model on the training set and evaluated it on the testing set using metrics like Mean Squared Error (MSE) and R-squared (R²).
   • Saving the Model:
     • Saved the trained model along with the feature names for future use.

Implementation in Real-time Application

The trained life expectancy model is integrated into a real-time video processing application:

• Face Detection: Identifies faces in the video stream.
• Age and Gender Prediction: Predicts the age range and gender for each detected face.
• Life Expectancy Prediction: Estimates the life expectancy dynamically based on the predicted age and gender using the pre-trained regression model.

How to Use

1. Clone the Repository:

   git clone https://github.com/Smohanta23/Life-Expectancy-Predictor.git
   cd Life-Expectancy-Predictor

2. requirements:

   opencv-python-headless==4.5.5.64
   scikit-learn==0.24.2
   pandas==1.3.3
   joblib==1.0.1
   numpy==1.21.2

3. Install the "requirements.txt" file
4. Run the Application:

   python main.py