MY PROJECTS
This project involved a comprehensive analysis of a phone sales dataset, performed in Jupyter Notebooks using Python and libraries such as Pandas, NumPy, Seaborn, and Matplotlib.
The data was cleaned and processed to handle missing values, and key metrics such as total sales revenue, average selling price, and market share by brand were calculated.
Visualizations were created to provide clear insights into the distribution of selling prices, the most popular phone colors, and the relationship between discounts and sales volumes.
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This project involves a comprehensive analysis of Bolt Ride operations, utilizing data from two tables representing activities in January and February.
The dataset encompasses various operational metrics, including Order Time, Pickup Address, Ride Price, Booking Fee, Tip, Payment Method, Payment Time, Distance, and State, among others.
Key insights were extracted through detailed analysis and subsequently visualized using a range of charts. These visualizations were then consolidated into an interactive dashboard to facilitate data-driven decision-making.
This Data Analysis & Visualization project aims to provide insights into the Healthcare performance of several health facilities in the United States.
By analyzing and visualizing various parts of the healthcare data, I sought to identify trends and gain a deeper understanding of the healthcare performance by analysing information about the healthcare systems' patients' names, ages, genders, medical conditions, insurance providers, blood groups, billing amounts, doctors, days of admission and hospitals attended.
This Excel project analyzed the sales performance of a supermarket's three major branches over a three-month period. The analysis focused on identifying the highest performing branch, preferred payment methods, top selling products, and overall sales trends.
The goal was to uncover key insights that would inform decision-making and improve sales efficiency.
Through exploratory data analysis (EDA), I provided a detailed understanding of branch performance and customer preferences. The project highlights opportunities for growth and offers actionable recommendations to enhance profitability across the supermarket’s branches.
In this project, I developed a Python-based web scraping solution to extract and structure tabular Kenya Certificate of Secondary Education (KCSE) data from the webpage of Moi Kapsowar Girls.
The primary objective was to automate the data collection process, clean and structure the extracted data, and prepare it for further analysis.
In this project, I delved into a detailed analysis of a vehicle loan dataset for an Asset Financing Company.
I performed an extensive analysis of the dataset using BigQuery to run a significant number of SQL queries.
In the analysis, I explored key areas such as demographics, financial stability, employment trends, risk assessment, and profitability.
Then finally, I visualized the findings in Looker Studio, enabling data-driven strategic decisions for optimizing loan offerings and managing risks effectively.
