Description

You have been hired as a data scientist from a big multi-category online store. You and your team have been required to perform an analysis of the customer behavior in the store. Each row in the dataset represents an event, which catches different interactions (views, a product added/removed to/from the cart, purchases) of customers with your e-commerce. All events are related to products and users.

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## Before starting
Among all numerous things and good practises a data scientist needs to do before running any analysis, there is one the is of uttermost importance: __get data and understand it__!

Here you find the list of tasks you need to perform before digging into the world of e-commerce.

* __Get your data!__ Go to [this
* __Understand your data.__ Read the legend of each column to understand what it refers to. Additional information about the labels can be found in the description of the data section on the web page. Please, be sure that you’ve understood the data before start coding.
* __Handling data.__ The data are provided in two `.csv` files, with the same columns present in both files. For this reason, in order to answer the RQs, we kindly suggest you to import the `.csv` files as `pandas DataFrame` object and then, based on what you want to analyze, perform the necessary operations.

Remember, **Google is your best friend!**

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# VERY VERY IMPORTANT
1. __!!! Read the entire homework before coding anything!!!__
2. _My solution it’s not better than yours and yours is not better than mine_. In any data analysis task, there __is not__ a unique way to answer to RQs. For this reason it is crucial (__necessary and mandatory__) that you describe any single decision you take and all the steps you do. 3. Once performed any exercise, comments about the obtained results are **mandatory**. We are not always explicit where to focus your comments, but we will always want some brief sentences about your discoveries.

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# Research questions

### Exploratory Data Analysis

– What’s the operation users repeat more on average within a session? Produce a plot that shows the average number of times users perform each operation (view/removefromchart etc etc).
– How many times, on average, a user views a product before adding it to the cart?
– What’s the probability that products added once to the cart are effectively bought?
– What’s the average time an item stays in the cart before being removed? – How much time passes on average between the first view time and a purchase/addition to cart?
2. [**RQ2**] *What are the categories of the most trending products overall?* For each month visualize this information through a plot showing the number of sold products per category.
– Plot the most visited subcategories.
– What are the 10 most sold products per category?
3. [**RQ3**] For each category, what’s the brand whose prices are higher on average?
– Write a function that asks the user a category in input and returns a
plot indicating the average price of the products sold by the brand.
– Find, for each category, the brand with the highest average price.
Return all the results in ascending order by price.
Create a plot that for each day of the week show the hourly average of visitors your store has.
6. [**RQ6**] The conversion rate of a product is given by the number of times a product has been bought over the number of times it has been visited.
What’s the conversion rate of your online store?
– Find the overall conversion rate of your store.
– Plot the number of purchases of each category and show the conversion rate of each category in decreasing order.
7. [**RQ7**] The Pareto principle states that for many outcomes roughly 80% of consequences come from 20% of the causes. Also known as 80/20 rule, in ecommerce simply means that most of your business, around 80%, likely comes from about 20% of your customers.
– Prove that the pareto principle applies to your store.

### Bonus points