Projects
120 Years of Olympic Games: Analyzing Athletes, Sports, and Events
In this project, I analyzed historical data from the modern Olympic Games to identify trends and patterns in athlete participation, country representation, and performance across various Olympic editions. The primary goal was to explore how athlete participation and country representation have evolved over the years, with a special focus on male-female participation trends.
Tools Used:
- Excel: For data loading, cleaning, formatting, and visualization
Using Excel, I conducted univariate analysis on key variables and developed an interactive dashboard showcasing historical trends in athlete and country participation. The dashboard also highlights the top countries and athletes with the most medals, the medal tally for leading nations, and the evolution of male and female participation over time. I incorporated slicers for filtering relevant insights, enabling users to focus on specific aspects of the data, and provided Key Performance Indicators (KPIs) for a quick overview.
Exploratory Data Analysis of Netflix Movies and TV Shows
This project involved an exploratory data analysis (EDA) of Netflix’s movie and TV show dataset to gain insights into various aspects of Netflix’s content library. The main objective was to answer key questions such as content trends over the years, popular titles in India, movie durations, and more.
Tools Used:
- Jupyter Notebook: For conducting the entire analysis
- Python Libraries:
- Pandas: For data cleaning and manipulation
- Matplotlib: For data visualization
Using these tools, I explored the dataset to answer the outlined questions, visualizing key findings such as content growth over time and popular content trends in India. While this project focuses on basic EDA, it lays the foundation for future exploration, where more advanced techniques could be applied to identify factors affecting ratings.
Global Terrorism Analysis: Uncovering Patterns and Trends (1970-2017)
In this project, I comprehensively analyzed the historical Global Terrorism Database to uncover key insights about terrorist attacks worldwide. The primary objective was to identify geographical hotspots, analyze trends over the years, determine the most active terrorist groups, understand their preferred attack methods, and assess the scale of casualties, among other factors.
Tools Used:
- Jupyter Notebook: For Python programming and analysis
- Python (Pandas): For data cleaning and formatting
- Tableau: For creating dynamic visualizations and dashboards
Using these tools, I developed an interactive dashboard in Tableau that provides a clear visualization of global terrorism trends. The dashboard highlights significant insights, such as the most affected regions, the evolution of attacks over time, and the involvement of specific groups in different types of attacks. These visualizations provide a detailed overview, helping users quickly understand critical trends and patterns in global terrorism, which can inform policy decisions.
Hotel Cancellation Rate Analysis and Revenue Optimization
In this project, I conducted a comprehensive analysis of hotel booking data to uncover patterns influencing cancellation rates. The primary objective was to identify actionable insights that could help optimize revenue by reducing these cancellation rates.
Tools Used:
- Jupyter Notebook: For Python programming and analysis
- Python Libraries:
- Pandas: Data exploration and manipulation
- Seaborn & Matplotlib: Data visualization
- Google Slides: For generating the final report
By using these tools, I identified key factors contributing to cancellations, such as booking lead time, pricing, and weather conditions. Based on these insights, I proposed targeted strategies aimed at optimizing bookings and enhancing revenue management, including dynamic pricing, effective lead time management, and improved customer engagement to minimize cancellations and boost overall revenue.
Navigating Economic Progress: Key Indicators for Achieving Sustainable Growth
This project focuses on analyzing the World Economic Indicator dataset to uncover the factors contributing to sustainable growth in various countries and regions. By examining a range of economic indicators, I aimed to provide actionable insights into how nations can achieve long-term economic stability.
Tools Used:
- Excel: For data loading, cleaning, formatting, and visualization
- Google Sheets: For generating reports
Using these tools, I explored critical questions surrounding sustainable growth. Through an in-depth analysis of factors such as GDP per capita, population, energy consumption, CO2 emissions, etc., I identified key drivers that significantly impact long-term growth, including energy consumption, healthcare expenditure per capita, and the internet-savvy population. Scatter plots were built for bivariate analysis, particularly to understand the relationship between GDP per capita and other variables, alongside correlation coefficients to measure the strength of these relationships.