Comcast Telecom Consumer Complaints Analysis Project Overview This project analyzes a dataset of customer complaints filed against Comcast, a major American telecommunication company, to uncover trends and insights regarding the quality of its customer service. The dataset serves as a public record of complaints submitted against Comcast, with the goal of identifying key issues and areas for improvement in Comcast’s customer support practices.

The project focuses on various analyses, including complaint trends, frequency by type, resolution status, and state-wise breakdown, utilizing Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn.

Data Dictionary The dataset contains the following fields:

Ticket #: Unique ticket number for each complaint. Customer Complaint: Text description of the complaint. Date: Date the complaint was filed. Time: Time the complaint was filed. Received Via: Mode of communication (e.g., Internet, Customer Care Call). City: Customer’s city. State: Customer’s state. Zipcode: Customer’s postal code. Status: Complaint status (e.g., Open, Closed, Pending). Filing on behalf of someone: Indicates if the complaint is filed on behalf of someone else. Analysis Tasks The project performs the following analysis tasks:

Data Import: Import the data into a Python environment for analysis.

Trend Analysis:

Create trend charts showing the number of complaints filed at both monthly and daily levels. Complaint Frequency Table:

Generate a table listing the frequency of each type of complaint, identifying the most common issues (e.g., internet issues, network issues). Complaint Status Categorization:

Create a new categorical variable for complaint status: Open: Includes both Open and Pending statuses. Closed: Includes both Closed and Solved statuses. State-wise Complaint Status Visualization:

Display a stacked bar chart showing the status of complaints by state. Insights: Identify the state with the highest number of complaints. Determine which state has the highest percentage of unresolved complaints. Complaint Resolution via Internet and Customer Care:

Calculate the percentage of complaints resolved through the Internet and customer care calls. Libraries Used Pandas for data manipulation and analysis NumPy for numerical computations Matplotlib and Seaborn for data visualization scikit-learn for additional analysis if needed BeautifulSoup for web scraping (if necessary for data enrichment) Insights Throughout the analysis, insights are provided to highlight key findings, such as:

The most frequent types of complaints. Trends in complaint volumes over time. Resolution status and effectiveness by state and communication channel.