Need a Python developer who can write production ready code

Part 1 -

1. What is the most common (i.e the one that appears the most in this data) type of post?

2. On average, which type of post received most ‘Page total likes’? Does this agree with the post ‘like’ metric?

3. On average, do paid video posts (Paid=1) receive more shares than the unpaid ones? Given that this data is for a Facebook page of a cosmetics brand, what are the reasons for the results you obtain? (i.e. if no, then why not?)

4. ‘Lifetime post consumers’ is the number of people who clicked anywhere in a post. ‘Lifetime post consumption’ is the number of clicks anywhere in a post. For each post, approximate the ‘consumption per user’ and determine the post type and month that, on average, receive the most ‘consumptions per user’. Is this result different from the total ‘Lifetime post consumers’ by post type and month?

5. For the ‘Photo’ type posts, which month show the greatest absolute skew in the distribution of the ‘consumptions per user’. Please define/comment ‘skew’ you think is appropriate for this data (e.g. it can be any numeric or visual estimate).

Part 2 - Modelling Lifetime Post Consumers

Build a model that can predict ‘Lifetime post consumers’ using any or all of the following features: Category, Type, Page total likes, Post Month, Post Weekday, Post Hour, Paid. Note that Category 1-3 correspond to {action, product, inspiration}. Please derive new features from the listed seven.

Please apply few modelling approaches after trying various train-test splits. Please provide the model performance estimates.

Skills: Python, Amazon Web Services, Google Cloud Platform

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