Diabetes-Prediction-Project

Predict diabetes using the Pima Indians Diabetes Dataset. The dataset contains medical records of female patients, including features like Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age, and the target variable Outcome (0 for non-diabetic, 1 for diabetic). The objective is to predict diabetes risk.

Objectives

• Perform exploratory data analysis distributions and relationships
• Preprocess the data
• Train Logistic Regression
• Create a reproducible pipeline and save the model for deployment

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Steps:

1. Data loading and preprocessing
2. Feature selection and visualization
3. Data standardization
4. Train-test split
5. Model training
6. Model evaluation
7. Feature importance visualization
8. Deploy the model

Model Summary

Accuracy: 0.8246753246753247

Confusion Matrix:
[[98  9]
 [18 29]]

Classification Report:
              precision    recall  f1-score   support

           0       0.84      0.92      0.88       107
           1       0.76      0.62      0.68        47

    accuracy                           0.82       154
   macro avg       0.80      0.77      0.78       154
weighted avg       0.82      0.82      0.82       154

Feature Importance:

Feature	Coefficient	Effect
Glucose	+1.0655	Positive
BMI	+0.6818	Positive
Diabetes Pedigree	+0.2857	Positive
Age	+0.2621	Positive
Pregnancies	+0.1324	Positive
Insulin	-0.1979	Negative
Skin Thickness	-0.2912	Negative
Blood Pressure	-0.3581	Negative

🚀 Deployment

Live App: Diabetes Prediction App (Streamlit)