Hi there! Welcome to my portfolio. I am a Data Analyst skilled in SQL, MS-Excel, Tableau, PowerBI, and Python. Let's connect on LinkedIn at Chinonye Clare Ohuakanwa
In this project, I conducted a comprehensive data exploration of the Covid-19 pandemic, utilizing a dataset encompassing global Covid-19 deaths and vaccination statistics within SQL Server.
The primary objective of the analysis was to gain valuable insights into the patterns, trends, and associations present in the Covid-19 data, with a particular focus on mortality rates and vaccination distribution worldwide.
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This project focuses on the New York City Taxi and Limousine Commission's comprehensive dataset, entailing 28 million Green Taxi trips conducted within the city from 2017 to 2020. The dataset comprises vital information related to 265 distinct zone locations, encompassing essential details such as pickup and dropoff locations, fare amounts, and other relevant features. Through the analysis of this extensive dataset, valuable insights were extracted, shedding light on patterns, trends, and factors influencing taxi trips and fare dynamics in New York City.
I created a series of engaging and informative interactive dashboards using Tableau! These accessible and aesthetically pleasing dashboards includes a diverse range of topics, including the "Covid Impact on Airport Traffic," "World Energy Consumption," and "Olist Store E-Commerce Analysis," among others. Each of these visualizations serves as powerful tools to present complex data in a user-friendly manner, enabling stakeholders to gain valuable insights and make data-driven decisions effectively.
BeeBeauty sales analysis explores a comprehensive examination of sales data, customer behavior, and product performance within the beauty retail industry. By analyzing key metrics and trends within specified timeframes, this analysis identifies top-selling products, peak sales periods, and customer preferences. These insights empower the store to optimize inventory, tailor marketing strategies, and enhance the overall shopping experience, ultimately driving revenue growth and customer satisfaction.
This project explores the prices of mobile phones based on their specifications. The main objective was to devise a predictive model capable of estimating new prices based on the most suitable approach. To achieve this, I used a variety of supervised learning models, including Random Forest, Logistic Regression, Decision Tree, and Support Vector Classification (SVC).
Newcastle, United Kingdom