Financial Data Science Project

Overview

An internship engagement using Python and SQL to prepare, validate, and model anonymized financial datasets, with a focus on data quality and interpretable evaluation.

Business problem

The team wanted a defensible predictive workflow on financial data with clean inputs, honest evaluation, and reporting that non-technical reviewers could follow.

Responsibilities

• Prepared and validated financial datasets with Python and SQL
• Engineered features and built predictive models with scikit-learn
• Evaluated models with cross-validation and interpretable metrics
• Improved data quality through cleaning, validation, and reconciliation
• Delivered reporting and visualizations to communicate findings

Dataset

Anonymized financial datasets covering transactions, account attributes, and outcome labels used to drive supervised modeling.

Approach

Profiled the raw data in Pandas, built SQL validation layers, engineered financial features, and compared several scikit-learn classifiers with cross-validation before locking a candidate.

Solution

A notebook-driven pipeline that produces a validated dataset, a chosen model with calibrated metrics, and a short reporting artifact summarizing performance and drivers.

Results

Predictive performance comfortably beat the baseline, and the reporting made model tradeoffs legible to a non-technical reader.

Challenges

Financial datasets bring class imbalance and correlated features that require careful preprocessing to avoid misleading metrics.

Limitations

The workflow was scoped to a snapshot; production use would need refreshed training data and drift monitoring.

Lessons learned

Data validation was the single largest lever on model quality; investing there paid back across every downstream step.

Future improvements

Add calibrated probability outputs, SHAP-based interpretation, and automated retraining with drift monitoring.