Developed a sentiment analysis tool using Python's Object-Oriented Programming (OOP) principles. The project classifies text data as positive, negative, or neutral by leveraging NLP techniques. Implemented clean, modular classes for data preprocessing, feature extraction, and model training, ensuring scalability and code reusability. This project demonstrates a structured approach to machine learning workflows. 

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Conducted an in-depth analysis of IPL (Indian Premier League) data to uncover insights and trends using Python. Extracted, cleaned, and visualized match statistics, player performances, and team strategies through libraries like Pandas, Matplotlib, and Seaborn. Delivered actionable insights, such as win probabilities and key player contributions, showcasing expertise in data manipulation and visualization techniques. 

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Analyzed WhatsApp chat data to extract meaningful insights such as message frequency, user activity patterns, and peak conversation times. Utilized Python libraries like Pandas and Matplotlib for data processing and visualization. The project highlighted user engagement trends and conversation dynamics, demonstrating expertise in text analysis and data storytelling. 

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Built a robust solution to extract and process data from APIs and web pages efficiently. Used libraries like Requests and BeautifulSoup for web scraping, combined with JSON parsing for API integration. The project automated data collection, enabling real-time updates and seamless data storage, showcasing skills in data extraction and API handling.

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Developed a sentiment analysis system integrated with MLOps practices for streamlined deployment and monitoring. Automated the ML lifecycle, including data preprocessing, model training, and deployment using tools like Docker and CI/CD pipelines. Implemented model tracking with MLflow, ensuring scalability and continuous improvement. This project demonstrates expertise in combining machine learning with operational efficiency. 

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Designed a predictive model to estimate taxi trip durations in New York City using advanced machine learning techniques. Leveraged a rich dataset with features like pickup/drop-off locations, timestamps, and weather conditions. Employed feature engineering, data preprocessing, and algorithms like Gradient Boosting to achieve high accuracy. The project highlights expertise in predictive modeling and real-world data application. 

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Developed an AI-powered emotion detection system capable of identifying emotions such as happiness, sadness, anger, and more from text or facial expressions. Utilized deep learning models like CNNs or NLP techniques for accurate emotion classification. This project demonstrates expertise in machine learning, feature extraction, and real-time emotion analysis for impactful applications. 

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Built a data-driven system to recommend properties and predict real estate prices with enhanced accuracy. Leveraged machine learning algorithms to analyze user preferences, property features, and market trends. Deployed on AWS for scalability, achieving a 30% increase in user engagement and a 15% improvement in price prediction accuracy. This project showcases advanced analytics and real-world problem-solving expertise. 

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Performed comprehensive EDA to uncover patterns, trends, and correlations in datasets, transforming raw data into actionable insights. Engineered new features by leveraging domain knowledge and statistical techniques to enhance model performance. This project highlights expertise in data visualization, preprocessing, and creating high-impact features for predictive modeling. 

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