mathewmanueljm / comcast-telecom-consumer-complaints-analysis

This project was completed as a part of assessment for Data Science with Python module. We used different Python libraries such as NumPy, SciPy, Pandas, scikit-learn and matplotlib to complete the given analysis and visualization tasks

Comcast-Telecom-Consumer-Complaints-Analysis

This project was completed as a part of assessment for Data Science with Python module. We used different Python libraries such as NumPy, SciPy, Pandas, scikit-learn and matplotlib to complete the given analysis and visualization tasks

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

1. Import data into Python environment.
2. Provide the trend chart for the number of complaints at monthly and daily granularity levels.
3. Provide a table with the frequency of complaint types.
4. Which complaint types are maximum i.e., around internet, network issues, or across any other domains.
5. 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.
6. Provide state wise status of complaints in a stacked bar chart. Use the categorized variable from Q5. Provide insights on:
7. Which state has the maximum complaints
8. Which state has the highest percentage of unresolved complaints
9. Provide the percentage of complaints resolved till date, which were received through the Internet and customer care calls.