Jivafit is a deep learning project focused on predicting disease risk. It utilizes a Multi-Layer Perceptron (MLP), a type of feedforward neural network, to analyze and learn from medical and health datasets. The goal is to provide a predictive tool for potential health risks.

🌟 Project Overview

Jivafit is a deep learning model designed to predict the risk of various diseases. It is built using a Multi-Layer Perceptron (MLP), a fundamental type of neural network capable of learning complex, non-linear relationships from structured data. The goal of this project is to develop a predictive tool that can analyze a wide range of health-related data points, such as symptoms, lab results, and demographic information, to assess an individual's potential risk for specific health conditions.

🧾 Dataset

Source: https://www.kaggle.com/datasets/nasirayub2/human-vital-sign-dataset

File: human_vital_signs_dataset_2024.csv

✨ Features

• Multi-Layer Perceptron Architecture: The model's core is an MLP, a robust architecture for classification and regression tasks.

• Disease Risk Prediction: Analyzes input data to output a probability or risk score for a target disease.

• Scalable: The model can be trained on diverse datasets and can be expanded to predict various diseases.

• Customizable: The number of layers, neurons, and activation functions can be easily adjusted to optimize performance for different datasets.

🛠️ Technologies Used

• Python: The core programming language for the entire project.

• TensorFlow: The primary deep learning framework used to build and train the neural network.

• Keras: A high-level API for TensorFlow, used for its simplicity in building the model architecture.

• Scikit-learn: Used for data preprocessing and splitting the dataset into training and testing sets.

• NumPy: The foundational library for numerical operations, essential for handling data arrays.

• Pandas: Used for data manipulation and analysis, primarily for working with structured datasets.

• Jupyter Notebooks: The development environment for building and experimenting with the model.

• Git & GitHub: Used for version control and collaborating on the project.