Telecom prepaid plan analytics

Chukwuemeka Okoli
Practicum by Yandex Project 3
April 30, 2021

Project description
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 brings in more revenue 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 brings in more revenue.

Guiding Question
Which prepaid plan brings in more revenue?

Table of contents

1. Objectives
2. Data Source
3. Technology Used
4. Structure of Notebook
5. Executive Summary
6. Accomplishments

Objectives

The objective of this project is to:

• Analyze clients' behavior and determine which prepaid plans brings in more revenue
• Use analytics to assists the comercial department in making adjustment in advertising budget
• Apply Statistical Data Analysis to a real-life analytical case study.

Data Source

Description of the data

The dataset contains the following fields:

• price
• model_year
• model
• condition
• cylinders
• fuel — gas, diesel, etc.
• odometer — the vehicle's mileage when the ad was published
• transmission
• paint_color
• is_4wd — whether the vehicle has 4-wheel drive (Boolean type)
• date_posted — the date the ad was published
• days_listed — from publication to removal

Technology Used

• Python
• Jupyter Notebook
• Pandas
• Numpy
• Matplotlib
• Seaborn
• ggplot2

Structure of Notebook

1. Open the data file and study the general information
2. Data preprocessing
• Processing missing values
• Data type replacement
• Processing duplicates
3. Make calculations and add them to the table
4. Carry out exploratory data analysis
5. Answer the business question
• Is there a connection between ...?
• Is there a connection between ...?
• Is there a connection between ...?
• How do different ... affect car price?
6. Conclusion

Executive Summary

Introduction

Methods

Key Findings

Deployment and Application

Future Development

Accomplishments