Nashville Housing Price Analysis

• Strengthened real-estate company investment decision making by analyzing housing data via machine learning.
• Improved data quality using Python pandas and NumPy for cleaning and missing value imputation; used Matplotlib and Pyplot to create a heatmap to better identify the most relevant data.
• Pinpointed which dependent variable had the greatest impact on housing prices by building predictive models (logistic regression, random forest, gradient boost).
• Demonstrated that the number of full baths increasing led to overpricing as opposed to number of bedrooms whose increase would not affect pricing accuracy.

Market Analysis of Liquor Industry                                                                                                                                                                                           

• Demonstrated that children of low-income families have a stronger desire to buy wine with an annual consumption amount $1,500 by building a SQL data warehouse to analyze the alcohol consumption market.
• Recommended retailers to focus marketing on low alcohol wine and adjust appearances of packaging and advertising to target young females after identifying their purchasing power for wine was up 36% from the previous year.
• Extracted data from population database, used “self-connection” function to connect table and “window” function to explore the changes of alcohol consumption level between age and family income level.

Analysis of Telecom Customer Churn                                                                                               

• Predicted potential customer churn for Telecom by using the customer life cycle value (CLV) algorithm in Python to identify and segment the highest value and longest life cycle customers for aiding in targeted strategy development.
• Preprocessed data with Sklearn, extracted and analyzed with Pandas, and transformed features with MinMaxScaler.
• Recommended use of preferential policies for low CLV customers and rewards for higher CLV customers.

Hospitality Food Operation Project                                                                                                   

Team Leader

• Identified most cost-efficient prices for banquet supplies (tables, chairs, ingredients) by using SQL to sort through a large collection of item types and prices as well as sponsors.
• Led a team of six in arranging an alumni association activity by creating an action plan and delegating tasks based on strengths and weaknesses.
• Identified optimal hosting site location, organized a day off activity for event staff, and recruited and trained staff on setup and food service protocol.

PROFESSIONAL EXPERIENCE

Hilton Hotel     

Data analyst

• Involved in analysis, design and documenting business requirements and data specifications; supported data warehousing extraction programs, end-user reports and queries
• Interacted with Business analysts to understand data requirements to ensure high quality data is provided to the customers
• Worked on numerous ad-hoc data pulls for business analysis and monitoring by writing SQL scripts; performed data intake, validation, cleaning, mining, modeling, visualization, and communication deliverables.
• Maximized hotel profits by analyzing company data provided by the sales department to accurately predict optimal room prices seasonally and determined the type and quantity of rooms sold in different sales channels at various prices.
• Drove strategic decision making, such as room price and sales channel control, by analyzing operational and managerial data to forecast market changes.