My Data Science & Machine Learning Projects

๐Ÿ‡ฎ๐Ÿ‡น Corporate Financial Risk Assessment System

A machine learningโ€“driven system that assesses corporate bankruptcy risk using financial ratios and key economic indicators. The system enables early identification of high-risk companies, allowing organizations to reduce financial exposure and prevent potential losses. The model achieved 85% accuracy, delivering a reliable, data-driven solution for proactive financial risk management.

Who this is for: Financial analysts, risk managers, credit assessment teams, and investment decision-makers.

Prediction Accuracy Assessment (Confusion Matrix)

Italian Bankruptcy Prediction Confusion Matrix

Technologies:

Python Pandas Matplotlib Seaborn Scikit-learn(Decision tree classifier and RandomForest) Imbalanced-learn Pickle Accuracy metrics-Precision and recall

๐Ÿ”— View Project on GitHub

Customer Intelligence & Segmentation Platform

An analytics platform that segments customers based on behavioral and purchasing patterns using K-Means clustering and PCA. By replacing generic marketing approaches with data-driven segmentation, the system enables targeted campaigns and personalized recommendations. Model effectiveness was validated using Silhouette Score and inertia, ensuring actionable and meaningful customer groups.

Who this is for: Marketing teams, growth analysts, product managers, and customer strategy teams.

K-Means Clustering Output: 3 Customer Segments

Customer_segmentation.png

Technologies:

Python Pandas NumPy Matplotlib Seaborn Plotly Scikit-learn(KMeans clustering and PCA) Accuracy Metrics- Silhouette score and inertia errors Pickle

๐Ÿ”— View Project on GitHub

Market Insight & Trend Analysis Engine

A data-driven engine built with FastAPI that analyzes historical and live stock data to model market trends and forecast volatility using GARCH models. The system provides reliable insights that support informed trading and investment decisions, with performance validated using AIC, BIC, and backtesting for consistency and robustness.

Who this is for: Traders, quantitative analysts, portfolio managers, and financial researchers.

Conditional Volatility Analysis of Apple Stock (ยฑ2ฯƒ Bands)

Apple daily returns vs 2SD conditional volatility.png

Technologies:

Python FastAPI Pydantic SQLite AlphaVantage API arch (GARCH) Pandas Joblib Requests OS & Glob .env configuration

๐Ÿ”— View Project on GitHub

๐Ÿ“ž Customer Retention Intelligence System

An ML-powered system that identifies customers at risk of churn and supports proactive retention strategies. Feature importance was analyzed using odds ratios, enabling clear interpretation of churn drivers and actionable business insights. The model achieved 0.80 training accuracy and 0.82 test accuracy, supporting effective customer retention planning.

Who this is for: Customer success teams, business analysts, telecom operators, and subscription-based businesses..

Telco Churn Drivers: Odds Ratio Feature Importance

ODDS RATIO HORIZONTAL BAR TELCOCHURN.png

Technologies:

Python Pandas Matplotlib Seaborn Scikit-learn(Logistic Regression) Imbalanced-learn Accuracy Metrics-Accuracy scores,GridSearchCV Pickle

๐Ÿ”— View Project on GitHub

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Insurance Risk Evaluation & Decision Support App

An interactive decision-support application that evaluates insurance risk profiles using Random Forest Regression and advanced feature engineering. The system supports accurate, data-driven financial planning and underwriting decisions, with performance evaluated using MAE, MSE, and Rยฒ to ensure dependable predictions.

Who this is for: Insurance analysts, underwriters, actuaries, and financial planning teams.

Insurance Cost Prediction App โ€“ User Interface

app.png

Technologies:

Python Streamlit Scikit-learn (RandomForest) Pandas Matplotlib Seaborn NumPy Category Encoders Pickle Feature Engineering Accuracy metrics-MAE,MSE,r2_score
View App

๐Ÿ”— View Project on GitHub