• Aim of the problem is to predict loan status based on certain features.

• Dataset Description Train.csv - 67463 rows x 35 columns (Includes target column as Loan Status)

Attributes:

• ID: unique ID of representative
• Loan Amount: loan amount applied
• Funded Amount:loan amount funded
• Funded Amount Investor: loan amount approved by the investors
• Term: term of loan (in months)
• Batch Enrolled: batch numbers to representatives
• Interest Rate: interest rate (%) on loan
• Grade: grade by the bank
• Sub Grade: sub-grade by the bank
• Employment Duration: duration
• Home Ownership: Owner ship of home
• Verification Status: Income verification by the bank
• Payment Plan: if any payment plan has started against loan
• Loan Title: loan title provided
• Debit to Income: ratio of representative's total monthly debt repayment divided by self reported monthly income excluding mortgage
• Delinquency - two years: number of 30+ days delinquency in past 2 years
• Inquires - six months: total number of inquiries in last 6 months
• Open Account: number of open credit line in representative's credit line 19. Public Record: number of derogatory public records
• Revolving Balance: total credit revolving balance
• Revolving Utilities: amount of credit a representative is using relative to revolving_balance
• Total Accounts: total number of credit lines available in representatives credit line
• Initial List Status: unique listing status of the loan - W(Waiting), F(Forwarded)
• Total Received Interest: total interest received till date
• Total Received Late Fee: total late fee received till date
• Recoveries: post charge off gross recovery
• Collection Recovery Fee: post charge off collection fee
• Collection 12 months Medical: total collections in last 12 months excluding medical collections
• Application Type: indicates when the representative is an individual or joint
• Last week Pay: indicates how long (in weeks) a representative has paid EMI after batch enrolled
• Accounts Delinquent: number of accounts on which the representative is delinquent
• Total Collection Amount: total collection amount ever owed
• Total Current Balance: total current balance from all accounts
• Total Revolving Credit Limit: total revolving credit limit
• Loan Status: 1 = Defaulter, 0 = Non Defaulters

Test.csv - 28913 rows x 34 columns(Includes target column as Loan Status) Sample Submission.csv - Please check the Evaluation section for more details on how to generate a valid submission.

The challenge is to predict the Loan Status

Knowledge and Skills Big dataset, underfitting vs overfitting Optimising log_loss to generalise well on unseen data

• Data source: https://www.kaggle.com/ankitkalauni/bank-loan-defaulter-prediction-hackathon

• As the values of columns Employment Duration and Home Ownership are interchanged, these columns are renamed to their correct names.
• Categorical attributes are encoded using LabelEncoder as we will using Random Forest for building the model.

• used ExtraTreesClassifier to select the best features

The competition evaluation metric used is Log-loss.

As this is a classification problem that involves prediction of whether a loan applicant will default or not, built Logistic Regression ,Random Forest and Xgboost models. performed log loss each model and Xgboost performed better with a log loss of 0.32. later performed hyperparameter tunning and train the xgboost model using Aws sagemaker instances And the f1 scores of the model improve from 0.91 to 0.94