This project explores the Titanic dataset to identify key patterns in passenger survival. The analysis includes data cleaning, handling missing values, outlier detection, and exploratory visualizations. Survival rates were examined by gender and passenger class, along with age and fare distributions. Correlation analysis and heatmaps were used to understand feature relationships. The study shows that female and first-class passengers had significantly higher survival rates. Tools used: Python, Pandas, NumPy, Matplotlib, Seaborn, and Jupyter Notebook. 

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This project analyzes the Netflix Titles dataset to uncover trends in content type, genres, release years, and country-wise distribution. The analysis includes data cleaning, grouping, visual exploration, and feature breakdown using Python, Pandas, NumPy, Matplotlib, and Seaborn. Key findings show that most content was added between 2019–2020, with Drama and International Movies being the most common genres. The USA contributes the highest number of titles, and the platform is dominated by Movies over TV Shows. 

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This project presents an interactive Super Shop Sales Dashboard built using Power BI to analyze revenue, sales quantity, and product performance. The dashboard highlights total revenue, total sales, top 5 products by profit, and market-wise performance. Users can filter data by year, quarter, month, and day for dynamic insights. Visualizations include bar charts, KPIs, and market comparison analytics. This dashboard helps identify high-performing products, key markets, and sales trends, supporting data-driven business decisions. 

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The Multifunctional Robotics Car project represents a comprehensive integration of diverse robotic functionalities using the Arduino Uno microcontroller platform. This endeavor aims to create an advanced robotic car that adeptly follows lines, evades obstacles, responds to an IR remote, and can be remotely operated through a mobile application