Diabetes Prediction Model

This project involves building a machine learning model to predict whether a person has diabetes based on several medical parameters. Various models are tested, and the best-performing model is selected based on accuracy.

Dataset

The dataset used for this project contains several medical parameters of patients, which are used as features to predict whether the patient has diabetes or not.

Features:

• Number of Pregnancies
• Glucose Level
• Blood Pressure
• Skin Thickness
• Insulin Level
• BMI (Body Mass Index)
• Diabetes Pedigree Function
• Age

Target Variable:

• y: Whether the patient has diabetes (1) or not (0)

Project Workflow

The workflow of this project can be summarized as follows:

1. Data Exploration & Visualization

   • Number of data points and target variable distribution.
   • Visualizing the feature space.

2. Model Building

   • Several machine learning models are evaluated, including:
     • Logistic Regression
     • Support Vector Machine (SVM)
     • Decision Tree Classifier
     • K-Nearest Neighbors (KNN)
     • Random Forest Classifier
     • Naive Bayes
     • Gradient Boosting
     • XGBoost

3. Model Evaluation

   • The models are evaluated based on their accuracy score.
   • A model comparison graph is plotted to show the accuracy of each model.

4. Final Model Selection

   • The model with the highest accuracy is selected as the final model.

Files

• target_distribution.png: Distribution of the target variable (class distribution).
• feature_space.png: A 2D feature space plot (for simplicity, assuming 2D features).
• model_comparison.png: Comparison of the models based on their accuracy scores.

Steps to Run

1. Clone the repository:

   git clone https://github.com/yourusername/diabetes-prediction.git