The work behind the skills.

I prepared the football ecommerce revenue dataset for executive analysis.

The final dashboard gives a clear executive view of revenue performance, product growth, channel efficiency, football moment impact, and regional opportunity.

I cleaned and prepared the insurance data.

The final dashboard gives a clear view of total claim amount, claim frequency, customer count, income patterns, insurance type, car model performance, and demographic claim trends.

I cleaned and prepared the hotel bookings data.

The final dashboard gives a clear view of booking trends, cancellation patterns, market segment performance, occupancy rate, lead time, and average daily rate.

I cleaned and prepared the HR employee dataset.

The final dashboard gives a clear view of employee attrition, workforce structure, salary trends, satisfaction scores, and HR performance indicators.

I cleaned and prepared the IT help desk ticket dataset.

The final dashboard gives a clear view of help desk performance, ticket workload, SLA compliance, agent productivity, resolution speed, and customer satisfaction.

I cleaned and prepared the LEGO sets dataset.

The final dashboard gives a clear view of LEGO set growth, theme popularity, pricing patterns, minifigure distribution, piece count trends, and detailed set performance.

I cleaned and prepared the toy sales dataset.

The final dashboard gives a clear view of sales revenue, profit, orders, product performance, and store performance.

I cleaned and prepared the Shark Tank dataset.

The final dashboard gives a clear view of Shark Tank deal trends, top industries, startup performance, investor behavior, pitcher distribution, and investment patterns.

I cleaned and prepared the Spotify music streaming dataset.

The final dashboard gives a clear view of Spotify track performance, top artists, streaming trends, chart presence, playlist performance, and cross-platform comparisons.

I cleaned and prepared the Udemy course dataset.

The final dashboard gives a clear view of Udemy course performance, subject distribution, revenue trends, course popularity, price distribution, and subscriber behavior.

The dataset was cleaned, transformed, and modeled in Power BI

The final dashboard provides a user-friendly interface for exploring global mortality patterns.

Loaded and explored the demographic dataset with Pandas.

Delivered a clean Python analysis that explains important demographic patterns.

Connected to the SQLite email database.

Produced insights about email volume, senders, recipients, recipient types, and common topics.

Prepared customer churn data for machine learning.

Created a churn prediction workflow that supports customer retention analysis.

Cleaned and explored restaurant order data.

Created a clear restaurant order analysis that supports menu and sales decisions.

Prepared Airbnb listing data for analysis.

Delivered an Airbnb listing analysis that explains pricing and listing patterns.

Prepared housing price data for machine learning.

Created a housing price prediction workflow that estimates property prices from structured data.

Prepared health and lifestyle data for classification.

Created a predictive model for obesity risk classification.

Cleaned and prepared telecom customer data.

Created a churn prediction workflow for telecom customer retention.

Prepared text data for sentiment analysis.

Delivered a sentiment analysis dashboard that turns text feedback into clear insights.

Prepared movie data for recommendation analysis.

Created a working movie recommendation project that demonstrates personalization and recommendation system thinking.