This project analyzes a dataset of consumer complaints related to financial products and services. The goal is to identify key trends, common issues, and potential areas for improvement in the industry.

The dataset was obtained from Kaggle. A sample of the data is included in this repository. The full dataset can be downloaded from Kaggle.

• Python
• Pandas
• Matplotlib
• Seaborn
• Google Colab

• The most frequent complaint category is "Debt collection," indicating significant issues in this area.
• There's a noticeable increase in complaints related to "Credit reporting, credit repair services, or other personal consumer reports" over time.
• The most common company responses are "Closed with explanation" and "Closed with monetary relief."

The analysis included the following visualizations:

• A bar chart showing the distribution of complaint categories, with "Debt collection" having the highest number of complaints.
• A time series plot illustrating the trend of complaints over time, showing an increase in "Credit reporting, credit repair services, or other personal consumer reports" complaints.
• A bar chart displaying the top 10 sub-issues for "Incorrect information on your report," with "Information belongs to someone else" being the most frequent.
• A bar chart displaying the top 10 products with the most complaints, with "Checking or savings account" having the highest number of complaints.
• A bar chart displaying the top 10 issues with the most complaints, with "Managing an account" having the highest number of complaints.
• A stacked bar chart showing the relationship between submission method and timely response, with "Web" submissions having the highest number of timely responses.
• A bar chart displaying the top 10 company responses to consumers, with "Closed with explanation" having the highest count.
• A line plot illustrating the complaint volume over time, showing a peak in 2022.

1. Clone this repository.
2. Download the full dataset from Kaggle and place it in the same directory as the notebook to run the full analysis. Alternatively, use the sample dataset provided.
3. Open and run the Consumer_complaint.ipynb file in Google Colab or your local Python environment.

GbemiAbe