This project was sparked by a casual chat with a friend, who playfully wondered about identifying her top five money-senders. What began as a quest to spot regular senders evolved into a full-fledged analytics undertaking. The journey, triggered by a simple question, led to an unexpected deep dive into the realm of data analytics.

For context, M-Pesa (short for Mobile Money in Swahili) is a mobile phone-based financial service that originated in Kenya and has since expanded to other countries. M-Pesa allows users to perform a variety of financial transactions using their mobile phones, even if they don't have access to traditional banking services.

Project Goal

The goal was to identify spending patterns, control expenses, and make informed decisions for improved budgeting and saving.

How I Uncovered the Insights

The file comes in PDF format. Once I received the file, I removed the password and converted it to an Excel file to start the analysis. You will notice that there are watermarks that you can remove too.

Merging the different sheets

Once I was past this, I uploaded the dataset to Excel. I noticed the data was in different sheets and I needed to merge all sheets into one for analysis. I then cleaned the data. Removed inconsistent data, duplicates, and empty rows. After this, I uploaded the dataset to Google Data Studio for visualization.

Here are the insights I uncovered

a. Top 5 people sending money

This was the main reason why this project kicked off. Here I was filtering by the Top 5 sources of income. The initials and name have been used for the purpose of concealing. However, the recipient got the dataset with full names.

b. Expenditure breakdown

I went further to understand where the money was being used. If you are using Looker Studio, create a filter, If you are using Tableau calculated fields are your best friend.

c. Monthly Paid in trend

Here I was getting the monthly distribution of money received (paid in)

d. Monthly withdrawing trend

I was getting the monthly distribution of money withdrawn-leaving the account. I wanted to see the month with the highest number and the least.

e. Percentage of money coming in and money leaving the account for both years

This was to offer a comparison between 2022 and 2023

Conclusion

Upon further analysis, it became evident that spending habits lacked control and a saving culture was absent. The frequency of loan transactions was notably high, which could lead to financial strain.

Recommendation

To address this, I recommended implementing a structured budgeting plan that allocates specific amounts to different expense categories. Additionally, fostering a savings habit through automatic transfers to a dedicated account can build financial resilience. Exploring alternatives to frequent loan dependence, such as setting up an emergency fund, could offer a more stable financial footing.