Tools: Python, Jupyter Notebook, Visual Studio Code, SQL, Tableau, Git, Command Line, Excel

Libraries: NumPy, Pandas, Scikit-learn, matplotlib, seaborn, nltk, BeautifulSoup, PyTorch


Here I present selected projects that show my Data Science skills for Marketing and Business problems.

For full list of the projects I worked on see Certificates section.


Clusterisation results based on Conversion Rates

Target Audience for Starbucks Rewards App

In this project, I analyzed the customer behavior in the Starbucks Rewards Mobile App. After signing up for the app, customers receive promotions every few days. The task was to identify which customers are influenced by promotional offers the most and what types of offers to send them in order to maximize the revenue. I used PCA and K-Means clustering to arrive at 3 customer segments (Disinterested, BOGO, Discount) based on Average Conversion Rates and explored their demographic profiles and shopping habits.

Code + Presentation

Technical Report

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Retention Rates

Marketing Analytics

In a series of Marketing Analytics projects, I used Online Retail II dataset to create cohorts based on monthly data, calculated retention rates and visualized them via a heatmap. Then I created RFM (Recency, Frequency, Monetary) segments, calculated RFM Score for each customer and segmented into 3 custom segments ‘Top’, ‘Middle’ and ‘Low’ based on the total RFM Score. Finally, I calculated the revenue-based CLV (Customer Lifetime Value) for each customer.

Code for Cohort Analysis

Code for RFM Analysis

Code for CLV Prediction

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Purchase Analytics

In this project, I analyzed purchase behavior of customers that bought 5 different brands of chocolate bars in a physical FMCG store during 2 years. In total, they made 58,693 transactions, captured through the loyalty cards they used at checkout. Based on the results of customer segmentation, I explored the segments sizes and answered the following business questions: 1. How often do people from different segments visit the store? 2. What brand do customer segments prefer on average? 3. How much revenue each customer segment brings?


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Tableau Dashboard

Tableau Dashboard for Watershed Properties

In this project, I performed data analysis to recommend short-term renting strategy for Watershed, a residential rental properties firm. To do this, I extracted relevant data from a real estate MySQL database, analyzed data in Excel to identify the best opportunities to increase revenue and maximize profits and created a Tableau dashboard to show the results of a sensitivity analysis.


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Facebook Ad

Digital Marketing for Udacity

In these projects, I run ad campaigns that advertised Udacity products on Facebook and Google Search. My Facebook Ad campaign took place between May 24-May 27, 2020 (3 days) and had a total budget of $100. The objective was to generate leads and collect new email addresses from prospective Digital Marketing Nanodegree students. The results exceeded expectations - 1056 emails collected with CTR of 1.38%, average CPC $0.07 and final cost of $76.29, saving 24% of the original budget.The Google AdWords search campaign advertised free course ‘Differential Equations in Action’ in India and run for 5 days with budget of $10/day. Campaign did not result into conversions (measured as # of enrollments into the course) but generated 93 clicks with average CTR of 3.46% and average CPC of $0.55.

Facebook Ad Campaign Report

Google AdWords Campaign Report

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IBM data science platform

Content Recommendations for IBM users

In this project, I implemented different recommendation engines for users of the IBM Watson Studio platform.
- Rank Based Recommendations: recommended the most popular articles based on the highest user interactions
- User-User Based Collaborative Filtering: recommended unseen articles that were viewed by most similar users
- Content Based Recommendations: recommended articles based on similarity of content


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