Final presentation.pptx_page-0001 Final presentation.pptx_page-0002 Final presentation.pptx_page-0003 Final presentation.pptx_page-0004 Final presentation.pptx_page-0005 Final presentation.pptx_page-0006 Final presentation.pptx_page-0007 Final presentation.pptx_page-0008 Project Title: Airbnb Review Dynamics: Correlation between the Number of Reviews and Other Factors Among Airbnb Listings   Project Description: This project investigates how factors, such as amenities and description sentiment scores, influence the number of positive reviews on Airbnb listings in San Francisco. Our analysis helps Airbnb hosts optimize their listings to enhance guest experiences and improve competitiveness in a saturated market.   Key Contributions: ●     Employed advanced data analytics techniques, including text mining, sentiment analysis, and linear regression, to assess the impact of listing features on guest reviews. ●     Identified essential amenities and descriptive elements that significantly influence positive guest reviews, providing actionable insights for hosts.   Impact: The findings directly inform Airbnb hosts on how to strategically enhance their listings to attract more positive reviews, thus improving their visibility and booking rates in a competitive market. These recommendations from the project are expected to significantly boost host revenue by optimizing listing attributes that are most influential in guest satisfaction.   Technologies Employed: ●     Data Analysis: Python, R ●     Data Visualization: Tableau ●     Statistical Modeling: Linear Regression, Correlation Analysis ●     Text Mining: Sentiment Analysis with BING and AFINN Lexicons    

HireMatch Pro_page-0001 HireMatch Pro_page-0002 HireMatch Pro_page-0003 HireMatch Pro_page-0004 HireMatch Pro_page-0005 HireMatch Pro_page-0006 HireMatch Pro_page-0007 HireMatch Pro_page-0008 HireMatch Pro_page-0009 HireMatch Pro_page-0010 Project Title: HireMatch Pro   Project Description: HireMatch Pro is a sophisticated job-matching system designed to enhance hiring quality and reduce turnover by utilizing an extensive dataset of over 1.5 million rows from Glassdoor job reviews. This system leverages advanced data warehousing and ETL pipelines to match job seekers with ideal positions based on detailed company reviews and job satisfaction metrics.   Key Contributions: ●     It developed and optimized an ETL pipeline using PostgreSQL to efficiently manage and process job review data. ●     Implemented a user-friendly web interface with Flask, allowing users to input job preferences and receive tailored job matches. ●     Integrated Python Psycopg for robust database interactions, enhancing data retrieval and update operations within the PostgreSQL environment.   Impact: ●     Our system is designed to significantly enhance the precision of matching job seekers with positions. This is expected to increase job satisfaction, improve the quality of hires, reduce employee turnover-related expenses, and foster career success. ●     It provided a scalable solution that supports expanding data sources and geographical coverage, ready to adapt to the dynamic needs of the global job market.   Technologies Employed: ●     Data warehouse: Python Psycopg, pgAdmin ●     ETL pipeline: PostgreSQL ●     User interface: Flask ●     Containerization: Docker   ●     Planned future enhancements include Natural Language Processing (NLP) for deeper sentiment analysis and machine learning algorithms for predictive analytics. Demo: Step 1: Start your job search on HireMatch Pro by entering your desired company name, location, and minimum rating Step 2: Narrow your search by specifying a company, here ‘Apple’ is used as an example Step 3: If a category is not a consideration for your search, it can be skipped. Refine your search by setting the minimum company rating on a scale of 1-5; here, a minimum rating of 2 is selected Step 4: View your search results below; each listing provides details on job title, location, ratings in various categories, and more

Boba Boom_page-0001 Boba Boom_page-0002 Boba Boom_page-0003 Boba Boom_page-0004 Boba Boom_page-0005 Boba Boom_page-0006 Boba Boom_page-0007 Boba Boom_page-0008 Boba Boom_page-0009 Boba Boom_page-0010 Project Title: Boba Boom: ExpandingChatime’s Reach through Strategic Location Analysis   Project Description: Boba Boom is adata-driven initiative aimed at boosting Chatime’s market share in the U.S.Despite substantial global growth, Chatime has fewer U.S. locations than itscompetitors. The project utilizes detailed location analytics with Tableau toidentify high-demand but underserved areas, integrating market trends andcompetitive analysis to devise a strategic expansion plan.   Key Contributions: Data Analysis: Utilized Tableau for comprehensive dataintegration and market analysis. Strategic Planning: Developed a business model to reducefranchise fees and optimize site selection, enhancing Chatime’s appeal topotential franchisees. Performance Metrics: Established KPIs to measure and adapt ourstrategy, ensuring continuous improvement and growth.   Impact: Boba Boom positionsChatime for significant U.S. bubble tea market growth by transforming complexdata into actionable strategies. This project demonstrates our innovativeapproach and analytical depth, showcasing our ability to drive substantialbusiness results through sophisticated data visualization and strategicanalysis.   Technologies Employed: Tableau  

Pawformance Boost_page-0001 Pawformance Boost_page-0010 Pawformance Boost_page-0009 Pawformance Boost_page-0008 Pawformance Boost_page-0007 Pawformance Boost_page-0006 Pawformance Boost_page-0005 Pawformance Boost_page-0004 Pawformance Boost_page-0003 Pawformance Boost_page-0002 Pawformance Boost Project Title: Pawformance Boost   Project Description: Pawformance Boost is a predictive analytics project aimed at enhancing the effectiveness of dog food advertising strategies to boost market share. Our team analyzed customer behavior and ad impact and developed a model to identify potential customers likely to purchase dog food post-ad exposure, enhancing targeting and ad efficiency.   Key Contributions: ●     Designed and implemented predictive models using Logistic Regression, SVM, Random Forest, and KNN, achieving up to 70% accuracy in predicting customer purchasing behavior. ●     Conducted extensive feature selection using univariate methods to pinpoint key determinants of ad effectiveness. ●     Developed the WOOF scoring system to prioritize high-potential customers for targeted advertising campaigns.   Impact: ●     Facilitated a five-fold increase in ad exposure for the client, significantly improving the precision of customer targeting. ●     Enabled the client to realign their marketing strategy, focusing on high-impact customer segments, potentially increasing market share.   Technologies Employed: ●     Programming Language: Python ●     Predictive Modeling: Logistic Regression, Support Vector Machine, Random Forest, K-nearest Neighbors ●     Feature Selection: Univariate Selection, SelectKBest ●     Data Handling and Analysis: Pandas, NumPy ●     Data Visualization: Tableau