https://github.com/eugen-goebel/predictive-analytics-agent
Automated ML pipeline — data profiling, preprocessing, model training, and evaluation report generation
https://github.com/eugen-goebel/predictive-analytics-agent
automation data-science docker machine-learning predictive-analytics python scikit-learn streamlit
Last synced: 3 months ago
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Automated ML pipeline — data profiling, preprocessing, model training, and evaluation report generation
- Host: GitHub
- URL: https://github.com/eugen-goebel/predictive-analytics-agent
- Owner: eugen-goebel
- License: mit
- Created: 2026-03-26T21:45:25.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2026-03-29T04:36:57.000Z (3 months ago)
- Last Synced: 2026-03-29T08:31:51.150Z (3 months ago)
- Topics: automation, data-science, docker, machine-learning, predictive-analytics, python, scikit-learn, streamlit
- Language: Python
- Size: 27.3 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Predictive Analytics Agent
An automated machine learning pipeline that profiles datasets, preprocesses data, selects features, trains and compares multiple models, and generates a professional evaluation report — all without requiring an API key.
## Features
- **Auto-Detection**: Automatically identifies the target column and task type (classification or regression)
- **Data Profiling**: Analyzes data quality, distributions, missing values, and column statistics
- **Smart Preprocessing**: Handles missing values, encodes categoricals, and scales features
- **Feature Selection**: Applies variance thresholding and statistical feature selection (SelectKBest)
- **Model Comparison**: Trains 4 models with 5-fold cross-validation and selects the best
- **Evaluation**: Generates confusion matrices, feature importance charts, model comparison plots, and overfitting detection
- **Report Generation**: Creates a professional DOCX report with all results and visualizations
- **Web Interface**: Interactive Streamlit app for uploading data and exploring results
- **No API Key Required**: Runs entirely locally using scikit-learn
## Architecture
The project uses a multi-agent architecture where each agent handles one pipeline phase:
```
MLPipelineOrchestrator
├── DataProfiler → Dataset analysis & target detection
├── PreprocessorAgent → Cleaning, encoding, scaling
├── FeatureEngineerAgent → Feature selection & ranking
├── ModelTrainerAgent → Training & cross-validation (4 models)
├── EvaluatorAgent → Metrics, charts, overfitting detection
└── ReportGenerator → Professional DOCX report
```
### Models Used
**Classification:**
- Logistic Regression
- Random Forest Classifier
- Gradient Boosting Classifier
- K-Nearest Neighbors
**Regression:**
- Linear Regression
- Random Forest Regressor
- Gradient Boosting Regressor
- K-Nearest Neighbors Regressor
## Quick Start
### Installation
```bash
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
pip install -r requirements.txt
```
### CLI Usage
```bash
# Run with sample dataset
python main.py
# Run with your own data
python main.py path/to/your_data.csv
# Specify output directory
python main.py data.csv --output reports/
```
### Web Interface
```bash
streamlit run app.py
```
Upload a CSV/Excel file or use the built-in sample dataset. The app displays:
- Data profile with quality metrics
- Preprocessing steps applied
- Model comparison table with scores
- Evaluation charts (confusion matrix, feature importance, etc.)
- Download button for the full DOCX report
## Sample Dataset
Includes a customer churn dataset (`data/sample_customers.csv`) with 80 rows and 11 features:
| Feature | Description |
|---------|-------------|
| age | Customer age |
| income | Annual income |
| credit_score | Credit score |
| years_customer | Years as customer |
| num_products | Number of products |
| has_mortgage | Has mortgage (0/1) |
| has_online_banking | Uses online banking (0/1) |
| monthly_charges | Monthly charges |
| total_charges | Total charges |
| support_calls | Number of support calls |
| **churn** | **Target** — whether customer churned (0/1) |
## Testing
```bash
pytest tests/ -v
```
35 tests covering all agents and the end-to-end pipeline.
## Project Structure
```
predictive-analytics-agent/
├── agents/
│ ├── data_profiler.py # Dataset profiling & analysis
│ ├── preprocessor.py # Data cleaning & transformation
│ ├── feature_engineer.py # Feature selection & ranking
│ ├── model_trainer.py # Model training & comparison
│ ├── evaluator.py # Model evaluation & charts
│ └── orchestrator.py # Pipeline coordinator
├── utils/
│ └── report_generator.py # DOCX report generation
├── tests/
│ ├── test_profiler.py
│ ├── test_preprocessor.py
│ ├── test_feature_engineer.py
│ ├── test_model_trainer.py
│ ├── test_evaluator.py
│ └── test_pipeline.py
├── data/
│ └── sample_customers.csv
├── app.py # Streamlit web interface
├── main.py # CLI entry point
└── requirements.txt
```
## Tech Stack
- **scikit-learn** — Machine learning models, preprocessing, evaluation
- **pandas** — Data manipulation
- **matplotlib** — Chart generation
- **Streamlit** — Web interface
- **python-docx** — Report generation
- **Pydantic** — Data validation with typed models
## License
MIT