This project provides a web interface for predicting heart disease based on various health metrics. It includes data cleaning, analysis, and machine learning model building functionalities integrated with Flask.
The Heart Disease Prediction project aims to predict the likelihood of heart disease using machine learning techniques. It features a Flask-based web interface for user interaction, data cleaning and preprocessing to prepare the dataset, exploratory data analysis (EDA) to understand patterns in the data, and model building to predict heart disease based on input features.
- Flask Web Interface: Allows users to input health metrics and receive a prediction for heart disease.
- Data Cleaning: Preprocesses raw data to handle missing values, outliers, and ensure data quality.
- Exploratory Data Analysis (EDA): Analyzes and visualizes data to gain insights into correlations and patterns.
- Machine Learning Model: Builds and deploys a machine learning model to predict heart disease.
- Model Evaluation: Assesses the model's performance using appropriate metrics and techniques.
- Python
- Flask
- Pandas
- NumPy
- Matplotlib
- Scikit-learn
- Jupyter Notebook
- Clone the repository:
git clone https://github.com/FirasKahlaoui/heart-disease-prediction.git
cd heart-disease-prediction