ewang58 / personal-loan-analysis Goto Github PK

• This project is created with the intention to determine which factor(s) influences clients to apply and accept a personal loan from the bank.
• We found that an individual's annual income and the number of family members are the most important factors in determining whether an individual accepts a personal loan from the bank.
• The dataset can be obtained from Kaggle.
• The libraries involved in the project include: Pandas, Matplotlib, Seaborn, and Plotly
• Link to this Project on Github

What is the most influential factors to receiving a personal loan from the bank?

• Some nominal variables are eliminated (such as "ID" and "Zip Code").

• No missing data is found.

• Anomaly values, such as negative values in "Experience", are replaced with the mean

  

• As depicted in the graph, there is a large number of outliers in "income".

• The correlation in the attributes is explored.

  

  Analyzing The Data

• There are more people with an "undergraduate" degree, however, the distribution of educational level attained is relatively equivalent among the people who received a loan.

  

  

• Most individuals who receive a personal loan do not hold or invest in any securities or bank deposits in their investment account

  

• Most people with an higher income are approved for a personal loan from the bank.

  

• Individuals who have family size 3 or greater with a higher income between 100k to 200k are more likely to apply for a loan.

  

• "Income" and "number of family member" are positively correlated with personal loan.
• "Education level" and investments has the least influence or impact on whether an individual decides to accept the loan
• Further investigation is recommended to determine the correlation between "income" and other factors such as "mortgage" or "CCAvg" etc.
• You can access the Jupyter notebook here.

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