This repository showcases my expertise in leveraging Python’s Machine Learning, Statistical, and Data Visualization capabilities to solve complex business problems. Focused on high-value domains like Financial Risk, E-commerce, and Customer Analytics, these projects demonstrate an ability to translate raw data into actionable insights for strategic decision-making.
- Fraud Detection: Developed and evaluated classification models (e.g., Logistic Regression, Decision Trees) to identify high-risk transactions. The analysis provides key features and thresholds for real-time risk scoring, directly supporting Financial Risk Mitigation strategies.
- Loan Repayment Prediction: Implemented supervised learning techniques to predict customer loan default risk. This project demonstrates proficiency in feature engineering, model optimization, and delivering insights crucial for credit policy and underwriting decisions.
- Credit Card Segmentation: Applied unsupervised learning (Clustering) to segment cardholders based on usage patterns and financial behavior. The results inform targeted marketing strategies and enhance product personalization for different customer groups.
- Sentiment Analysis of Amazon Alexa Reviews: Used Natural Language Processing (NLP) with libraries like NLTK to classify customer feedback (positive/negative) and identify key themes in product reviews. The insights support product development and drive improvements in user satisfaction.
- Product Recommendation System: Built a recommendation engine using collaborative filtering and content-based methods for a grocery dataset. This demonstrates the ability to enhance user engagement and increase Customer Lifetime Value (CLV) through personalized suggestions.
- Netflix User Analysis / Movie Recommendation: Performed comprehensive Exploratory Data Analysis (EDA) on user and content data to uncover viewing trends and inform platform strategy.
- Time Series Forecasting: Applied time series techniques (e.g., ARIMA, Prophet) to forecast a variable (e.g., sales, demand). This project validates skills in working with temporal data crucial for budgeting and operational planning.
- Tesla Stock Price Analysis: Extracted and cleaned financial data (Yahoo Finance), performing EDA and preparing the data for time-series forecasting. Demonstrates proficiency in handling and analyzing high-frequency financial data.
| Category | Skills & Techniques | Business Value |
|---|---|---|
| Predictive Modeling | Machine Learning (Classification, Regression, Clustering), Hyperparameter Tuning, Model Evaluation | Predicting customer behavior (churn, default), forecasting demand, and supporting risk assessment. |
| Data Engineering | Python (Pandas, NumPy), Data Cleaning, Preprocessing, Feature Engineering, API integration (Web Scraping) | Ensuring data quality and transforming raw data into reliable datasets for BI and reporting. |
| Advanced Analytics | NLP, Statistical Analysis, Time Series Forecasting, Exploratory Data Analysis (EDA) | Extracting non-obvious trends from unstructured data and informing future-oriented planning. |
| Visualization & Reporting | Matplotlib, Seaborn, Clear narrative structure in Jupyter Notebooks | Translating complex analytical findings into actionable, easily digestible insights for diverse audiences. |
| Technical Proficiency | Expertise in Python's data science ecosystem, integrated use of libraries, and structured code. | Providing reliable, maintainable analytical solutions that are ready for deployment and integration. |
This comprehensive portfolio reflects a strong foundation in data science methodologies and a practical ability to deploy advanced Python techniques to solve diverse analytical challenges. The focus on financial risk, segmentation, and predictive modeling makes me a highly valuable asset for roles requiring both technical depth and sharp business acumen.