- Developed a Python program simulation aimed at generating production-level recommendations based on a set of specified parameters.
- Employed NumPy to create random samples of demand instances, utilizing both a normal (Gaussian) distribution and a 50% Uniform / 50% normal distribution.
- Utilized Matplotlib to craft a scatterplot visualization, incorporating a color scale to highlight optimal production ranges.
- Conducted a series of analyses and data visualizations to either validate or refute statements put forth by Subway Restaurant leadership.
- Head of Customer Service: “Our ratings are gradually improving, and we will soon reach 4.5/5.”
- Head of Store Operations: “Sandwiches are a tricky business. All sandwich chains suffer from poor customer ratings.”
- Head of Social Media: “The goal of 4.5/5 is unreasonable for national chains like us. Only small, local, and boutique restaurants can achieve such high ratings.”
- Chief Data Scientist: “It is well known that customers make the effort to give a rating only when they are either extremely angry or absolutely delighted with the service. So online ratings are not reliable.”
- Used matplotlib, seaborn, and pandas to generate line charts, bar charts, and grouped-by DataFrames.
-
The advent of Call of Duty Warzone in 2019 brought about a shift where merely aiming at your target was no longer adequate. Depending on the distance and the weapon in use, leading your projectile became imperative.
-
Crafted a set of data visualizations depicting the impact of an individual weapon's bullet velocity and distance to the target on the trajectory of the projectile. Explored how these factors influence the likelihood of hitting a moving target accurately.
-
Used matplotlib, seaborn, and pandas to generate line charts, heatmaps, and DataFrames.
- Conducted a series of analyses and crafted data visualizations to investigate the trend of Netflix movie durations over time.
- Employed tools such as matplotlib, pandas, and seaborn to generate diverse visual representations, including line charts, bar charts, histograms, scatterplots, and grouped-by dataframes.
- Performed a series of analyses and data visualizations to conclude the top puplic schools and boroughs located in New York City based on average SAT scores and schools per borough.
- Used matplotlib, seaborn, and pandas to generate a heatmap, line chart, bar charts and grouped-by DataFrames.
- Utilized PostgreSQL techniques including joins, set theory, and subqueries to extract comprehensive data from multiple tables, enabling an insightful analysis to pinpoint the Golden Years of gaming, while providing valuable insights into market trends and consumer preferences.
- Applied SQL aggregation functions and sorting methods to identify the industry's top-performing video games over the years based on games sold, critic reviews, and user ratings.
Project 7: Promotional Campaign Analysis for Quick Dosa's Takeout Business: Leveraging A/B Testing and Statistical Analysis
- Utilized pivot tables, Excel filtering, and chi-square analysis to compare Promotion A and Promotion B, assessing revenue, promotional costs, ad-exposure costs, click-through rate, and statistical significance. This enabled strategic decision-making for revenue maximization and cost minimization.
- Employed chi-square analysis alongside return on advertising and click-through rate, to determine the optimal promotion for wider rollout, ensuring decisions are based on a comprehensive evaluation of promotional effectiveness and advertising efficiency.
Project 8: Predicting Future Boston Blue Bike Subscribers and Customers: Optimizing Station Bike Numbers
- Forecasted future subscriber and customer bike rentals, leveraging predictive analytics techniques to anticipate demand trends accurately and optimize resource allocation.
- Utilized comprehensive data analysis to identify the most frequented bike stations across the city of Boston. Developed a dynamic dashboard integrating real-time data to optimize bike availability on an hourly basis for each month individually, ensuring an unparalleled customer experience while maintaining optimal stock levels.
- Crafted marketing strategies tailored to specific zip codes within Boston, enabling the precise targeting of demographics to maximize customer acquisition and retention.
