I was working on this project analyzing customer complaints filed against Comcast, a global telecommunications company known for poor customer service. Despite repeated promises to improve, the company continues to fall short and was even fined $2.3 million last month after receiving over 1000 consumer complaints. Here, my task was to use Python libraries such as NumPy, SciPy, Pandas, scikit-learn, matplotlib, and BeautifulSoup to analyze the data in the existing database of complaints.

My responsibilities included importing the data into the Python environment and providing trend charts for the number of complaints at monthly and daily granularity levels. I also provided a table with the frequency of complaint types and identified which types are the most common, such as internet or network issues.

I created a new categorical variable with values of Open and Closed, categorizing complaints as Open and Pending as [Open] and Closed and Solved as [Closed]. I also provided a stacked bar chart showing the state-wise status of complaints and used the categorized variable from Q3 to provide insights on which states have the maximum complaints and highest percentage of unresolved complaints.

Finally, I provided the percentage of complaints resolved till date that were received through the internet and customer care calls.