AI • Machine Learning • Data Analytics • Full-Stack Data Science
AI-Powered Data Scientist & Business Analytics Platform
Flagship Project
A full-stack platform guiding users from raw datasets through cleaning, EDA, machine learning, predictions, AI-supported explanations, and reporting.
- Python
- FastAPI
- scikit-learn
- PostgreSQL
- Next.js
- Ollama
Project overview
A full-stack data science and business analytics platform designed to take users from raw business data to cleaned datasets, exploratory analysis, machine learning models, predictions, AI-supported explanations, and professional reports through a guided workflow.
Python and machine learning libraries calculate the metrics and predictions. The AI layer explains structured, verified analytical results rather than inventing or recalculating them.
Key features
- CSV and Excel dataset upload
- Automatic dataset profiling
- Recommended and manual data cleaning
- Automated exploratory data analysis
- Intelligent target detection with user confirmation
- Classification and regression workflows
- Automatic model training and comparison
- Logistic and Linear Regression
- Decision Trees
- Random Forest
- Gradient Boosting
- XGBoost
- K-Nearest Neighbors
- Cross-validation and baseline comparison
- Data leakage detection
- Feature importance
- Individual predictions
- Batch prediction
- Interactive visualizations
- AI-powered insights
- Grounded AI chat
- PDF report generation
- Guided end-to-end workflow
- Authentication and project management
Implementation / workflow
- Upload CSV or Excel data, profile the dataset, and apply recommended or manual cleaning.
- Explore the data and confirm the prediction target before training classification or regression models.
- Compare models using cross-validation and baselines, check leakage, and inspect feature importance.
- Generate individual or batch predictions, explore grounded AI explanations, and export PDF reports.