Internship Project . Aim was to build a model to help the bank to identify the potential customers who have higher probability of purchasing the loan. Also to predict the likelihood of a liability customer buying personal loans . And to build a model that will help in identifying the potential customers who have a higher probability of purchasing the loan , Thus increasing the success ratio while at the same time reduce the cost of the campaign . So to build the model We used Machine learning techniques and libraries(like pandas , numpy , scikit , matplotlib ) to plot the graph and make various models to check which sector and what type of people are more prone to take loans . We used different algorithm (random forest , knn , Decision tree , logistic regression ) to find accuracy of test and train data which were split in 70:30 ratio . And finally combining all analysis of data to reduce the cost of campaign along with searching of potential customers.
swapnilkr/Analysis-of-Bank-Data-for-Marketing-Campaigns
Internship Project . Aim was to build a model to help the bank to identify the potential customers who have higher probability of purchasing the loan.
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