This repository contains the analysis that i did for the final project for the Ironhack Bootcamp, focused on diabetes prediction using machine learning techniques. The project intend to predict an individual's risk of developing diabetes based on a variety of health and lifestyle factors. The following sections provide an overview of the project's components and key findings.
Its primary focus was to create a practical tool for predicting an individual's risk of developing diabetes. This involved a comprehensive analysis of a dataset containing health and lifestyle data. The heart of the project layd in developing a machine learning model. This model was trained on historical data to provide accurate predictions of diabetes risk.
- To develop a data-driven solution for accurate diabetes prediction.
- To apply machine learning algorithms and data analysis techniques to gain insights.
- To create an accessible and engaging tool for diabetes risk assessment.
- Data Preprocessing: The dataset was cleaned, and missing values were handled to prepare it for analysis.
- Exploratory Data Analysis (EDA): Extensive EDA was performed to understand the data, visualize patterns, and identify potential relationships.
- Machine Learning Model: A machine learning model was trained using historical data to predict diabetes risk.
- Web Application: A user-friendly web application was developed to input user data and obtain risk predictions.
- Visualization: Visualizations were created to communicate insights and model performance.
- Python
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-Learn
- Pickle
- Streamlit (for the web application)
Looking ahead, there is room for growth. Expanding the dataset, optimizing the application, and dive deeper into model interpretability.