COMCAST TELECOM CONSUMER COMPLAINTS

Introduction: I worked on this project titled “Comcast Telecom Consumer Complaints” to fulfil mandatory criteria towards “Data Science with Python” module of my PGP course from Simplilearn. I did not create any machine learning model in this project. I performed data analysis in python using various tools such as Pandas, Numpy, Matplotlib, Seaborn, Worldcloud.

Objective: Comcast is an American global telecommunication company. The given dataset serves as a repository of public customer complaints filed against Comcast. My task was to analyse given data and provide the trend chart for the number of complaints at monthly/ daily granularity levels and present further insight.

Project Description:

Analysis Task: To perform these tasks, you can use any of the different Python libraries such as NumPy, SciPy, Pandas, scikit-learn, matplotlib, and BeautifulSoup.

Import data into Python environment.
Provide the trend chart for the number of complaints at monthly and daily granularity levels.
Provide a table with the frequency of complaint types. • Which complaint types are maximum i.e., around internet, network issues, or across any other domains.
Create a new categorical variable with value as Open and Closed. Open & Pending is to be categorized as Open and Closed & Solved is to be categorized as Closed.
Provide state wise status of complaints in a stacked bar chart. Use the categorized variable from Q3. Provide insights on: • Which state has the maximum complaints • Which state has the highest percentage of unresolved complaints
Provide the percentage of complaints resolved till date, which were received through the Internet and customer care calls.

The analysis results to be provided with insights wherever applicable.

Tools used: This project was done in Python language and I have performed data analysis using various tools such as Pandas, Numpy, Matplotlib, Seaborn, Worldcloud.