Dynamic and analytical guy with experience in data analysis and visualization. Skilled in extracting and manipulating large datasets using SQL and Python, and experienced in using statistical techniques to identify trends and patterns. It is always my passion to be a data analyst.
- Collect data on past airline ticket prices, including information such as date of purchase, destination, and any other relevant features
- Explore the opinions of different users after tasting different brands of wine and other alcohol such as beer.
- Determine the positive and negative sentiment of the dataset, social network analysis and further determine the polarity score also called sentiment prediction.
- Accurately predict future prices of Exxon Mobil stock using historical data.
- Implement various statistical and machine learning models using the Python programming language.
- This study can help improving investment decision-making and strategy formulation.
- The prediction for the gross income in the next three months for each branch to do the preparation for the next three months.
- Determining the peak hour based on first three months to help the supermarket provides a corresponding strategy to increase the satisfaction of customers to their services
- Discount mart is a small supermarket owned by Grant Frost. He wants a dashboard where hecan track how well Discount Mart is doing for this year (in terms of Sales, Profit and QuantitySold).
- He would also like to know how well categories are performing as well as different regions. Grant Frost assumes that most customers buy 2 or more products per basket/order but wouldlike this confirmed by the data. Grant also noted that Profit is 30% of the selling price.
- By training the model on a dataset of previously approved and rejected loan applications, I was able to identify the most important factors that influence loan approval and predict whether an applicant would be approved or rejected, with an accuracy of 80% and an F1 score of 0.86. This model has the potential to save lenders a significant amount of time and resources, by automating the decision-making process for loan applications, reducing the need for manual reviews and minimizing the risk of human error.
- The dashboard includes information on the most favorites programming language, average salary by job title and satisfaction on work-life balance. It's a wealth of information that can help guide your career decisions and give you a better understanding of the industry.
- Completed a Power BI dashboard for global coronavirus analysis
- Includes metrics such as total deaths, total recoveries, and active cases per country
- Experience in data scraping and Power BI utilization
- Data sourced from https://www.worldometers.info/coronavirus/
- Insights into global energy consumption patterns and identification of trends and shifts in the energy market.
- A better understanding of energy supply stability and security.
- The ability to make informed decisions about investment, trade, and resource allocation, supporting sustainable economic growth.