✓ This NYSE Stock Analysis & Investment Recommender project uses time-series analysis and data-driven insights to evaluate historical stock performance (2010–2021) across key sectors like Technology, Healthcare, and Finance. ✓ By identifying trends (e.g., AMZN's 25% returns) and calculating risk metrics, it generates tailored portfolios—low-risk for conservative investors (JNJ, UNH) and high-growth for aggressive investors (AMZN, FB)—with validated returns of 10–25%. ✓ Built with Python (Pandas, Plotly), this tool transforms complex market data into actionable strategies, empowering investors to make informed decisions.

✓ This project develops a lead scoring model for X Education to identify high-potential "Hot Leads" most likely to convert into paying customers. ✓ By analyzing historical data, cleaning and preprocessing features, and training a logistic regression model, the solution achieves 81.6% accuracy and an AUC of 0.88, enabling the sales team to prioritize efforts efficiently. ✓ Key insights reveal that time spent on the website, lead source (Google/Direct Traffic), and professional background significantly influence conversions, helping optimize marketing strategies and boost enrollment rates.

✓ This project leverages machine learning clustering techniques to identify the world's most vulnerable countries based on economic and health indicators. ✓ By analyzing GDP, child mortality, and income data, we pinpoint nations in dire need of humanitarian aid, helping HELP International NGO allocate resources effectively. ✓ The top priority countries include Burundi, Liberia, and Sierra Leone, where urgent intervention can save lives and foster sustainable development.

✓ This project analyzes bike-sharing data to predict demand and help a US-based company recover from pandemic losses. ✓ Using Linear Regression, we identified weather, seasonality, and time trends as key factors influencing rentals. ✓ The model provides actionable insights to boost revenue through strategic promotions and operational adjustments. ✓ Explore the code and findings to see how data science can drive business recovery!

✓ This project predicts term deposit subscriptions for a Portuguese bank using a Decision Tree model. ✓ By analyzing 45,211 customer records, we identified call duration and past campaign success as critical drivers of conversions. ✓ Despite high accuracy, class imbalance remains a challenge. ✓ Explore the code to see how data science can refine marketing strategies and boost campaign ROI!

✓ This project analyzes 73,996 tweets using NLP to decode public sentiment. ✓ By comparing manual labels with TextBlob's polarity scores, we uncovered hidden trends and extreme opinions. ✓ Explore the code to see how NLP transforms social media data into actionable business insights!