✒️ About Project

✍️ Diabetes Prediction Project

🗒️Project Overview

This project focuses on classification task for predicting diabetes using the Pima Indians Diabetes Dataset. The goal is to accurately identify individuals who are likely to have diabetes, aiding early diagnosis and preventive healthcare.

Hugging Face Link: Click

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🗒️Dataset

• Source: Pima Indians Diabetes Dataset
• Features: Numeric and categorical features including:
  • Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age
• Target: Outcome (0 = non-diabetic, 1 = diabetic)
• Total samples: ~768
• Imbalance: Fewer positive cases (~268) than negative (~500)

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🗒️Data Preprocessing

• Missing and zero values handled with median imputation.
• Outliers in numeric features were clipped (1%–99% quantiles).
• Numeric features scaled using StandardScaler.
• Categorical features encoded using OneHotEncoder.
• Pipelines used to integrate preprocessing and ensure reproducibility.

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🗒️Model Selection

• Primary model: Logistic Regression
• Alternative models evaluated: SVC, KNN
• Evaluation metric: Recall for positive class (Outcome=1) prioritized due to medical significance.

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🗒️Model Training and Evaluation

• Pipeline created using ColumnTransformer and preprocessing.
• Models trained on stratified train-test split to handle class imbalance.
• Cross-validation (5-fold) applied to assess model robustness.
• Metrics recorded: Accuracy, Precision, Recall, F1-score (positive class).

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🗒️Key Findings

• Logistic Regression with class_weight="balanced" achieved highest recall for diabetic cases.
• KNN showed higher F1 but lower recall.
• Weighted metrics can be misleading due to dataset imbalance; positive class recall is primary metric.

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🗒️Deployment

• Final pipeline saved as .pkl file.
• Front-end can pass input as dictionary or DataFrame.
• Column order flexibility maintained; column names must match pipeline requirements.

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🗒️Technologies & Libraries

• Python 3.x
• Pandas, NumPy
• scikit-learn
• Matplotlib / Seaborn (EDA & visualization)
• Joblib / Pickle (pipeline serialization)

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✍️Project Setup & Run Instructions

This guide explains how to set up the Python environment and run the project.

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1. Create Python Environment

Create a new virtual environment for this project:

python -m venv aiml_env

2. Activate Environment

Activate the newly created environment:

aiml_env\Scripts\activate

3. Install Dependencies

Install all required packages from requirements.txt:

pip install -r requirements.txt

4. Run the application

Start projet by typing:

python app.py

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🗒️References

• Pima Indians Diabetes Dataset – Kaggle
• scikit-learn documentation: Pipeline & ColumnTransformer

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If you faced any kind of issue feel free to Report:
Name: MD Rohan Mulla
🎓University: Rabindra Maitree University
📨E-mail: mdrohanislam444@gmail.com
facebook: https://www.facebook.com/MullaRohan