- Business Case: Life Safety is a health insurance company that plans to implement a cross sell strategy. For that it needs to prioritize those with higher propensity to purchase the product.
- Solution: In this learn to rank problem, I used Gradient Boosting to classify and return a propensity score. This score was used to sort the customer base by the higher propensity to purchase and the predictions can be accessed by Google Spreadsheet
- Results: at 20% customer base reached out, the model hit 2.89x more interested customers and reduced CAC in 35%.
- Tools: Python, Flask, Ensemble Methods(XGBoost, Random Forest, KNN), Render Cloud, Google Scriptss
- Business Case: A CFO of drugstore company needs to have a forecast of sales for the next six weeks, so he will know how much to invest in renovation of the stores.
- Solution: In this time series problem, I used ensemble methods for regression tasks to predict the six weeks sales, and developed a Telegram Bot to access these predictions
- Results: (on going)
- Tools: Python, Flask, Ensemble Methods(XGBoost, Random Forest Regression), Render Cloud
- Business Problem: A CEO of real-state company, that consists in purchasing properties and reeseling them, asked for data team to select the best transactions that could maximize the profits
- Solution: developed a webapp with the recomended properties(buy and sell), disposed at tables and interactive maps with the distribuition os properties
- Results:
- Tools: Python, Streamlit Cloud
- Business Problem: Two partners decided to enter the US fashion market, building their own e-commerce called Star Jeans but they lack of knowledge to define the product market fit.
- Solution: Through a ETL process, the data will be scrapped from H&M e-commerce and some metrics will be presented at a dashboard to answer the business questions
- Results: (on going)
- Tools: Python, PowerBI, ETL
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