This project is about analyzing personal finance data of bank clients and creating a report with visualizations and data analytics for different clients. The project was a part of a data science job interview exercise, where I had to analyze multiple interconnected spreadsheets after transforming them to pandas data frames.
The dataset provided by the bank had information about personal finances of its clients. It included data about their income, expenses and different type of financial operations. The dataset was distributed across multiple interconnected spreadsheets.
The first step was to transform the spreadsheets into pandas data frames and merge them into a single data frame. I cleaned the data by removing duplicates, handling missing values, and correcting data types.
1 Next, I analyzed the data to identify trends and patterns. I created visualizations such as pie plots, scatter plots, and line charts to visualize the data. I used seaborn and matplotlib libraries for data visualization.
I created reports with different visualizations and data analytics for each client. The dashboard included visualizations such as income vs. expenses, monthly spending patterns, etc. I also created a summary of key performance indicators for each client.
The project was successful in achieving the objective of analyzing personal finance data of bank clients and creating a dashboard with visualizations and data analytics for different clients. The project showcased my skills in data cleaning, transformation, analysis, and visualization. The interviewer was impressed with the quality of the dashboard and the insights generated from the data analysis.