DiabetesPrediction

🏥 Diabetes Prediction using Local Dataset and Jupyter Notebook

This repository contains a machine learning project for predicting diabetes using a local dataset. The project is implemented in Python using Jupyter Notebook and includes data preprocessing, exploratory data analysis (EDA), feature selection, and model training using various classification algorithms. This project aims to predict diabetes based on various health parameters using a machine learning model. The dataset is loaded locally, preprocessed, analyzed, and used to train predictive models in a Jupyter Notebook environment.

📌 Features

• Data preprocessing and handling missing values
• Exploratory Data Analysis (EDA) with visualizations
• Feature selection and correlation analysis
• Machine learning model training and evaluation
• Comparison of different classification algorithms (e.g., Logistic Regression, Random Forest, SVM)

📂 Dataset

The dataset used in this project is stored locally in CSV format. It contains medical predictor variables such as glucose levels, BMI, age, blood pressure, etc., with a target variable indicating the presence of diabetes.

🛠️ Technologies Used

• Python
• Jupyter Notebook
• Pandas, NumPy (Data Handling)
• Matplotlib, Seaborn (Data Visualization)
• Scikit-learn (Machine Learning)
• TensorFlow/Keras (Deep Learning, if applicable)

🚀 How to Run the Project

1. Clone this repository:

   git clone https://github.com/Jayaprakashsuseelam/DiabetesPrediction.git
   cd DiabetesPrediction