🧬 BioBatch-Quality-Predictor

Predictive Analytics for Biopharmaceutical Batch Quality & Manufacturing Efficiency

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📊 PROJECT STATUS

Phase	Title	Status	Completion	Last Updated
0	Project Initialization & Setup	✅ Complete	100%	[08-08-2026]
1	Data Exploration & ChemE Foundation	⏳ Not Started	0%	—
2	Data Cleaning & Feature Engineering	⏳ Not Started	0%	—
3	Exploratory Analysis & Pattern Discovery	⏳ Not Started	0%	—
4	Predictive Modeling	⏳ Not Started	0%	—
5	Model Diagnostics & Validation	⏳ Not Started	0%	—
6	Anomaly Detection Layer	⏳ Not Started	0%	—
7	Visualization & Dashboard	⏳ Not Started	0%	—
8	Documentation & Storytelling	⏳ Not Started	0%	—

Legend: ✅ Complete | 🔄 In Progress | ⏳ Not Started | ❌ Blocked

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🎯 Project Overview

The Problem

Biopharmaceutical batch manufacturing is complex. Batch failures waste millions in resources and time. We lack early warning signals—by the time QA catches issues, it's too late.

The Solution

Build a data-driven predictive model that:

• Predicts batch failure early (day 3 of a 10-day process)
• Identifies critical quality attributes (CQAs) driving success
• Detects anomalies even in "passing" batches
• Quantifies cost savings from prevention

The Data

• Source: Biopharmaceutical manufacturing dataset (Kaggle)
• Size: 100 complete batch records
• Variables: Temperature sensors, spectroscopy readings, off-line measurements, final QA metrics
• Time span: Multi-stage bioprocess (cell culture → purification → formulation)

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🏗️ Project Structure

BioBatch-Quality-Predictor/
├── data/
│   ├── raw/                          # Original Kaggle dataset (never modify)
│   ├── processed/                    # Cleaned, engineered datasets
│   └── data_dictionary.md            # Column definitions & units
├── code/
│   ├── src/                          # Reusable functions
│   │   ├── preprocess.py
│   │   ├── models.py
│   │   └── utils.py
│   ├── notebooks/                    # Phase-by-phase analysis
│   │   ├── 01_eda.py
│   │   ├── 02_cleaning.py
│   │   ├── 03_analysis.py
│   │   ├── 04_modeling.py
│   │   ├── 05_diagnostics.py
│   │   ├── 06_anomaly.py
│   │   └── 07_visualizations.py
│   └── scripts/                      # Utility scripts
├── output/
│   ├── plots/                        # Generated visualizations
│   ├── models/                       # Saved model artifacts (.pkl, .joblib)
│   └── reports/                      # Metrics, summaries
├── powerbi/                          # Dashboard files & screenshots
├── resources/                        # Reference materials
├── docs/
│   ├── methodology.md                # Technical approach
│   ├── executive_summary.md          # 1-page summary for non-technical readers
│   └── CHANGELOG.md                  # Phase-by-phase updates
├── .gitignore                        # Git exclusions
├── requirements.txt                  # Python dependencies
├── LICENSE                           # MIT License
└── README.md                         # This file

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🚀 Quick Start

1. Setup Environment

# Clone repository
git clone https://github.com/tanixka/BioBatch-Quality-Predictor.git
cd BioBatch-Quality-Predictor

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

2. Place Your Data

Extract the Kaggle dataset to data/raw/

3. Run Phase Analysis

Navigate to code/notebooks/ and run phase scripts in order.

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📋 Phase Breakdown

Phase 0: Project Initialization ✅

• ✅ GitHub repo setup
• ✅ Folder structure created
• ✅ Python environment configured
• ✅ Documentation framework initialized

Deliverable: This README + environment ready

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Phase 1: Data Exploration & ChemE Foundation (Target: 3-4 hours)

Goal: Understand the dataset through a chemical engineering lens

Tasks:

• Download & inspect 100-batch biopharmaceutical dataset
• Map variables to bioprocess stages
• Identify critical quality attributes (CQAs) vs process parameters
• Create time-series visualizations

Deliverables:

• EDA notebook (01_eda.py)
• Data visualization heatmap
• Summary: "What does this dataset tell us about batch variability?"

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Phase 2: Data Cleaning & Feature Engineering (Target: 4-5 hours)

Goal: Transform raw sensor noise into analytical signals

Tasks:

• Handle missing data
• Detect & flag sensor outliers
• Engineer time-series features (rate of change, rolling averages, volatility)
• Normalize/scale for modeling
• Create binary target: batch success vs deviation

Deliverables:

• Cleaned dataset (data/processed/)
• Feature engineering notebook (02_cleaning.py)
• Data quality report

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Phase 3: Exploratory Analysis & Pattern Discovery (Target: 3-4 hours)

Goal: Find signals that predict batch outcomes

Tasks:

• Segment batches by outcome
• Compare process profiles (successful vs failed)
• Identify bottleneck stages
• Correlation analysis & time-series clustering

Deliverables:

• Visualization notebook (03_analysis.py)
• Early warning signals analysis
• Narrative summary

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Phase 4: Predictive Modeling (Target: 5-6 hours)

Goal: Build models that predict batch success early

Tasks:

• Binary classification (will batch succeed?)
• Regression (predict final titer/yield)
• Train on first 50% of batch data, predict final outcome
• Cross-validation & comparison

Deliverables:

• Modeling notebook (04_modeling.py)
• ROC curves, confusion matrices, feature importance
• Model selection memo

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Phase 5: Model Diagnostics & Validation (Target: 3-4 hours)

Goal: Ensure model works in production

Tasks:

• Residual analysis
• Cohort performance breakdown
• Sensitivity analysis
• Temporal validation (time-based train/test split)

Deliverables:

• Diagnostics notebook (05_diagnostics.py)
• Risk-benefit analysis
• False alarm rate quantification

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Phase 6: Anomaly Detection Layer (Target: 3-4 hours)

Goal: Catch unusual batches that don't fit normal patterns

Tasks:

• Unsupervised learning (Isolation Forest, LOF)
• Compare supervised vs unsupervised detection
• Real-world angle: contamination, equipment drift

Deliverables:

• Anomaly detection notebook (06_anomaly.py)
• Flagged batches with explanations
• Dual-model recommendation

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Phase 7: Visualization & Dashboard (Target: 4-5 hours)

Goal: Make findings accessible to stakeholders

Tasks:

• Create interactive Power BI dashboard
• Real-time batch monitoring simulation
• Sensor heatmap + anomaly flags
• Key metrics: yield, cycle time, failure rate
• Optional: Streamlit web app

Deliverables:

• Power BI dashboard (.pbix)
• Dashboard screenshot + design write-up
• Optional: Streamlit app

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Phase 8: Documentation & Storytelling (Target: 2-3 hours)

Goal: Make this a compelling portfolio piece

Tasks:

• Comprehensive README
• 1-page executive summary
• Optional: Blog post

Deliverables:

• Polished GitHub repo
• Executive summary PDF
• LinkedIn post / blog link

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🛠️ Technologies Used

Category	Tools
Language	Python 3.9+
Data Processing	Pandas, NumPy
ML/Modeling	Scikit-learn, XGBoost, LightGBM
Visualization	Matplotlib, Seaborn, Plotly, Power BI
IDE	VS Code
Version Control	Git, GitHub

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📈 Expected Outcomes

• ✅ Predictive model achieving 75%+ accuracy in binary classification
• ✅ Early warning system (predict failures at day 3 of 10-day batch)
• ✅ Feature importance ranking (which parameters drive quality?)
• ✅ Cost-benefit analysis (false positive vs missed detection trade-off)
• ✅ Interactive dashboard for manufacturing teams
• ✅ Production-ready code structure

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📚 Resources

• Kaggle Dataset: [Big Data Biopharmaceutical Manufacturing]
• ChemE References: [To be added]
• Model Papers: [To be added]
• Dashboard Tutorials: [To be added]

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🤝 Contributing

This is a portfolio project. Feedback & suggestions welcome!

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📜 License

MIT License - See LICENSE for details

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✍️ Author

Tanishka
Chemical Engineering + Data Science
LinkedIn | GitHub

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Last Updated: [08-08-2026]
Repository: BioBatch-Quality-Predictor