rkafambe / bank_loanprediction

internship studio project for The classification goal is to predict the likelihood of a liability customer buying personal loans.

Bank_loanPrediction

Project Title: Marketing Campaign for Banking Products Internship studio project for "The classification goal is to predict the likelihood of a liability customer buying personal loans". Completed by Kshitiz Raj Patel

Steps and tasks:

1. Import the datasets and libraries, check datatype, statistical summary, shape, null values etc
2. Check if you need to clean the data for any of the variables
3. EDA: Study the data distribution in each attribute and target variable, share your findings. ● Number of unique in each column? ● Number of people with zero mortgage? ● Number of people with zero credit card spending per month? ● Value counts of all categorical columns. ● Univariate and Bivariate analysis
4. Apply necessary transformations for the feature variables
5. Normalise your data and split the data into training and test set in the ratio of 70:30 respectively
6. Use the Logistic Regression model to predict the likelihood of a customer buying personal loans.
7. Print all the metrics related for evaluating the model performance
8. Build various other classification algorithms and compare their performance
9. Give a business understanding of your model

Note : Reload the "Bank_loan_prediction_by_kshitizrajpatel.ipynb" file or refresh the window tab(2-3 times) if error occurs while opening the file.