Comcast is an American global telecommunication company. The firm has been providing terrible customer service. They continue to fall short despite repeated promises to improve. Only last month (October 2016) the authority fined them a $2.3 million, after receiving over 1000 consumer complaints. The existing database will serve as a repository of public customer complaints filed against Comcast. It will help to pin down what is wrong with Comcast's customer service.
Ticket #: Ticket number assigned to each complaint Customer Complaint: Description of complaint Date: Date of complaint Time: Time of complaint Received Via: Mode of communication of the complaint City: Customer city State: Customer state Zipcode: Customer zip Status: Status of complaint Filing on behalf of someone 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.
Imported data into Python environment.
Provided the trend chart for the number of complaints at monthly and daily granularity levels.
Provided a table with the frequency of complaint types.
• Identified which complaint types are maximum i.e., around internet, network issues, or across any other domains.
Created 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.
Provided state wise status of complaints in a stacked bar chart. Use the categorized variable from Q3. Provide insights on:
• Spotted the states that have the maximum complaints and have the highest percentage of unresolved complaints.
Provided the percentage of complaints resolved till date, which were received through the Internet and customer care calls and the analysis was shown with insights wherever applicable.