Financial_Analysis-Python-Pandas

Data cleansing, analysis graphs using Python language. Graphs using Seaborn library.

The project goal was to present, on a dashboard, 10 indicators obtained from the dataset. The dataset was a result of interviews made with google forms during the pandemic:

1. Gender distribution of participants
2. Age distribution of participants
3. How many participants have any investment avenues? Distributed by gender.
4. How many participants invest in the stock market? Distributed by gender.
5. What is the main factor considered for investing?
6. What is the relationship between the age of the participants and the purpose of the investments?
7. The duration in which the participants prefer to invest.
8. The frequency of the monitorization of the investments.
9. Preferences for investment
10. Preferences for investment by gender

Python Code:

• Queries to access and filter database
• Seaborn Library to create the charts

Libraries needed to run the code:

• Pandas
• Numpy
• Seaborn
• Matplotlib.pyplot

Dataset used:

Dataset source: https://www.kaggle.com/datasets/nitindatta/finance-data?select=Finance_data.csv