Diabetes Prediction Using Machine Learning
About:
This repository contains a comprehensive project focused on predicting diabetes in patients using various machine learning algorithms. The aim is to provide a tool that can assist healthcare professionals in the early detection of diabetes, ultimately leading to better patient outcomes through timely intervention.
Key Features:
-->Multiple Machine Learning Models: Implemented various algorithms, including Random Forest, Gradient Boosting, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbors (KNN), XGBoost, and Decision Tree Classifier.
-->Performance Comparison: Evaluated and compared the performance of each model using metrics such as accuracy, precision, and recall to determine the best approach for diabetes prediction.
-->Data Source: Utilized a publicly available diabetes dataset that includes critical health metrics such as glucose levels, blood pressure, body mass index (BMI), and age.
Data Preprocessing: Employed techniques such as data cleaning, normalization, and feature selection to prepare the data for modeling.
-->Visualizations: Included visualizations to illustrate the model performances and insights gained from the data analysis.
Technologies Used:
-->Programming Language: Python -->Libraries:
Pandas for data manipulation
NumPy for numerical computations
Scikit-learn for machine learning algorithms and model evaluation
Matplotlib and Seaborn for data visualization
-->Installation:
-->Clone the repository: git clone https://github.com/Phoenixcoder-6/diabetes-prediction.git
-->Navigate to the project directory: cd diabetes-prediction
-->Install the required packages: pip install -r requirements.txt
Usage:
- Load the dataset and run the notebook (Diabetes_Prediction.ipynb) to execute the code for data preprocessing, model training, and evaluation.
- Explore different models and their performance metrics to understand which model works best for predicting diabetes.