Prepaid-Plan-Analysis

Data Analysis on profitability between two prepaid plans from hypothetical telecom company Megaline. Which prepaid plan is more profitable?

Background

This project is part of the Data Scientist training program from Practicum by Yandex. More info in link below:

https://practicum.yandex.com/data-scientist

Objective

You work as an analyst for the telecom operator Megaline. The company offers its clients two prepaid plans, Surf and Ultimate. The commercial department wants to know which of the plans is more profitable in order to adjust the advertising budget.

You are going to carry out a preliminary analysis of the plans based on a relatively small client selection. You'll have the data on 500 Megaline clients: who the clients are, where they're from, which plan they use, and the number of calls they made and text messages they sent in 2018. Your job is to analyze clients' behavior and determine which prepaid plan is more profitable.

Description of the data

Megaline rounds seconds up to minutes, and megabytes to gigabytes. For calls, each individual call is rounded up: even if the call lasted just one second, it will be counted as one minute. For web traffic, individual web sessions are not rounded up. Instead, the total for the month is rounded up. If someone uses 1025 megabytes this month, they will be charged for 2 gigabytes.

The users table (data on users):

• user_id — unique user identifier
• first_name — user's name
• last_name — user's last name
• age — user's age (years)
• reg_date — subscription date (dd, mm, yy)
• churn_date — the date the user stopped using the service (if the value is missing, the calling plan was being used when this database was extracted)
• city — user's city of residence
• plan — calling plan name

The calls table (data on calls):

• id — unique call identifier
• call_date — call date
• duration — call duration (in minutes)
• user_id — the identifier of the user making the call

The messages table (data on texts):

• id — unique text message identifier
• message_date — text message date
• user_id — the identifier of the user sending the text

The internet table (data on web sessions):

• id — unique session identifier
• mb_used — the volume of data spent during the session (in megabytes)
• session_date — web session date
• user_id — user identifier
• The plans table (data on the plans):
• plan_name — calling plan name
• usd_monthly_fee — monthly charge in US dollars
• minutes_included — monthly minute allowance
• messages_included — monthly text allowance
• mb_per_month_included — data volume allowance (in megabytes)
• usd_per_minute — price per minute after exceeding the package limits (e.g., if the package includes 100 minutes, the 101st minute will be charged)
• usd_per_message — price per text after exceeding the package limits
• usd_per_gb — price per extra gigabyte of data after exceeding the package limits (1 GB = 1024 megabytes)

Libraries Used

• pandas
• matplotlib.pyplot
• seaborn
• numpy
• scipy