Hamza Murshad

This was a collaborative data analytics hackathon project completed as part of the Code Institute’s Data Analytics with AI program.Our team was tasked with exploring a healthcare insurance-related dataset and presenting insights through a clear, interactive dashboard.The project focused on analysing healthcare factors such as BMI and Age to find the greatest cause of healthcare insurance cost, identifying key trends, and investigating relationships between them.Working collaboratively in a team environment, I contributed to:Cleaning and organizing the dataset using Pandas and NumPyConducting exploratory data analysis (EDA) to uncover trends and correlationsCreating data visualizations in Matplotlib and Seaborn to communicate findings effectivelyBuilding a Power BI dashboard to present results interactivelyUsing GitHub Projects (Kanban board) and branch management in VS Code for workflow coordinationPresenting findings and data insights to students of Code Institute and our facilitatorThe hackathon experience helped me strengthen not only my technical skills but also my team collaboration, version control, and project communication abilities which resembled a real-world data analytics environment.

This collaborative data analytics hackathon project explored a dataset from The Movie Database (TMDb) to uncover insights into film trends, audience preferences, and revenue drivers within the movie industry.Our goal was to analyse relationships between movie attributes such as budget, revenue, genres, and ratings to determine what factors most influence a film’s success. The project combined data cleaning, visual storytelling, and dashboard creation to communicate actionable insights.As part of a team, I contributed to:Cleaning and preprocessing the TMDb dataset using Pandas and NumPyConducting exploratory data analysis (EDA) to identify correlations between variables like budget, revenue, popularity, and runtimeCreating visualisations in Matplotlib and Seaborn to highlight key insightsDesigning an interactive Power BI dashboard summarizing top-grossing genres, rating distributions, and release trends over timeCollaborating through GitHub Projects (Kanban) for workflow management and Git branching for efficient version controlPresenting findings as part of a group presentation simulating a professional data team environment

Electronic Sales is a data analytics project that explores the factors influencing customer behaviour and sales performance in an electronics retail dataset. The dataset (sourced from Kaggle) contains ~20,000 records and 16 features (e.g. customer demographics, product category, loyalty status, payment method). The goal was to uncover trends and validate hypotheses around loyalty programs, spending patterns, product categories, seasonal sales, and customer segmentation. I cleaned the data, tested hypotheses using visualisations, built interactive charts, and provided business-facing insights.I also documented my entire workflow using Jupyter Notebooks.