I was provided with a series of csv files about the banking habits of customers: information about their chequing accounts, saving accounts and general other transaction data. I was also given information about the demographics of the customers - generic descriptors such as age, marital status, yearly income, gender and city/state. At this point i had to clean the data i was provided with - find and rectify typos in the messy data, impute missing values, encode the categorical variables and so on.

I obtained generated retail data from Keith Galli, the dataset obtained had about 200,000 orders with varying different aspects about them. From this, i performed feature engineering and data cleaning in extracting date, time data and locations to give me further insights on the best revenue generating months/cities. Under certain assumptions, I was able to model the optimal time to advertise. Furthermore, i went and extracted key business insights such as the best selling products and the best complements of products bought (i.