Python (Data Science and Analysis) Projects

This Python-based portfolio showcases a diverse range of projects highlighting expertise in data wrangling, exploratory data analysis, predictive modeling, and advanced machine learning techniques to solve real-world problems. By leveraging Python libraries like Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn, I have cleaned, preprocessed, and analyzed datasets to extract actionable insights, identifying trends, anomalies, and correlations. Projects demonstrate proficiency in implementing and optimizing machine learning models such as Random Forest, Decision Trees, and XGBoost, along with advanced techniques like NLP and collaborative filtering. Business applications include customer sentiment analysis, product recommendations, and financial analysis, with findings presented through impactful visualizations and clear reporting. This portfolio reflects a strong ability to translate complex data into meaningful insights, showcasing my passion for leveraging data science methodologies to deliver impactful solutions tailored to complex analytical challenges.