Diabetes-Prediction-Challenge — Playground Series (S5E12)

A hands-on Jupyter Notebook project for exploring and modeling diabetes prediction. This repository contains exploratory data analysis (EDA), preprocessing, modeling, evaluation, and basic explainability workflows carried out as a part of a Playground Series episode (S5E12).

Language: Jupyter Notebook (100%)

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Table of contents

• Project Overview
• What's included
• Dataset
• Getting started
  • Prerequisites
  • Installation
  • Running the notebooks
• Notebooks & workflow
• Modeling & Evaluation
• Results & Insights
• Reproducibility
• Contributing
• License & Contact

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Project overview

This repository demonstrates a complete, reproducible workflow to build a model that predicts the presence (or risk) of diabetes for individuals using tabular data. The goal is to illustrate good EDA, preprocessing, model selection, validation, and quick explainability techniques using Jupyter notebooks.

This is a learning/playground project — ideal for demonstration, experimentation, and tutorial-style walkthroughs.

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What's included

• One or more Jupyter notebooks containing:
  • Exploratory data analysis (visualizations, summary statistics)
  • Data cleaning and preprocessing (missing values, encoding, scaling)
  • Feature engineering and selection
  • Model training (baseline models and one or more stronger models)
  • Model evaluation (cross-validation, hold-out test, metrics)
  • Basic explainability (feature importance, SHAP or partial dependence)
• (Optional) Example visualizations and model artifacts saved by notebooks

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Dataset

This repository assumes the use of a diabetes-related tabular dataset (for example, the Pima Indians Diabetes Dataset or a similar public dataset). If you have a specific dataset you'd like to use, place it in a data/ directory and update the notebook paths accordingly.

If your dataset is large or private, keep it out of the repo and document how to obtain it here.

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Getting started

Prerequisites

• Python 3.8+ (recommended)
• Jupyter Notebook or JupyterLab
• Typical Python data stack: numpy, pandas, scikit-learn, matplotlib, seaborn
• Optionally: xgboost / lightgbm, shap

Installation

1. Clone the repository:

git clone https://github.com/Vivek-ML001/Diabetes-Prediction-Challenge-Playground-Series-S5E12.git
cd Diabetes-Prediction-Challenge-Playground-Series-S5E12

2. Create a virtual environment and install dependencies. If there is no requirements.txt yet, install common packages:

python -m venv .venv
source .venv/bin/activate   # macOS / Linux
.venv\Scripts\activate      # Windows

pip install --upgrade pip
pip install jupyterlab numpy pandas matplotlib seaborn scikit-learn xgboost shap

(Recommendation: add a requirements.txt or environment.yml to the repo for reproducibility.)

Running the notebooks

Start Jupyter:

jupyter lab
# or
jupyter notebook

Open the notebook(s) in the repository and run the cells in order. The notebooks are organized to be runnable end-to-end for a clean environment if dataset paths are set correctly.

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Notebooks & workflow

Typical notebook sections you will find or can expect to implement:

1. Data loading and overview
2. EDA — distributions, correlations, missingness
3. Data cleaning and imputation
4. Feature engineering and transformation (scaling, encoding)
5. Model training (baseline classifiers, tree-based models)
6. Hyperparameter search (GridSearchCV / RandomizedSearchCV)
7. Model evaluation: confusion matrix, ROC AUC, precision/recall, F1
8. Feature importance and explainability (SHAP or permutation importance)
9. Conclusions and next steps

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Modeling & evaluation

Suggested models and evaluation strategy:

• Baselines: Logistic Regression, Decision Tree
• Stronger models: RandomForest, XGBoost / LightGBM
• Validation: Stratified K-Fold cross-validation and/or a holdout test set
• Metrics: ROC AUC, accuracy, precision, recall, F1, confusion matrix
• Explainability: feature importance, SHAP values for local/global explanations

Include consistent random seeds for reproducibility.

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Results & insights

Summarize key findings in the notebook:

• Best-performing model and its validation metrics
• Important predictive features
• Observations about class imbalance (if present) and how it was handled
• Potential next steps to improve the model (more features, better sampling, ensembling)

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Reproducibility

To make experiments reproducible:

• Commit notebooks after major changes
• Save model artifacts and results (e.g., models/ and outputs/)
• Provide requirements.txt or environment.yml
• Record random seeds and dataset versions

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Contributing

Contributions are welcome. Here are a few ways to help:

• Add a requirements.txt or environment.yml
• Provide a small sample dataset or instructions to fetch the dataset
• Add tests or a CI workflow for running notebooks (nbval)
• Improve notebooks with clearer explanations, visualizations, or advanced modeling

License & contact

This repository is provided for educational purposes. Add a license (e.g., MIT) if you want to allow reuse.

Author / Contact: Vivek-ML001

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