exploratory-data-analysis---customer-loans-in-finance

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Loan Data Dictionary - remember that these are currently the old data types, remember to provide a column with the new datatypes

Field	Description	Type
id	Unique ID of the loan	string
member_id	ID of the member who took out the loan	string
loan_amount	Amount of loan the applicant received	float
funded_amount	The total amount committed to the loan at that point in time	float
funded_amount_inv	The total amount committed by investors for that loan at that point in time	float
term	The number of monthly payments for the loan	integer
int_rate (APR)	Annual (APR) interest rate of the loan	float
instalment	The monthly payment owed by the borrower, inclusive of interest	float
grade	Loan company (LC) assigned loan grade	string
sub_grade	LC assigned loan sub grade	string
employment_length	Employment length in years	integer
home_ownership	The home ownership status provided by the borrower	string
annual_inc	The annual income of the borrower	float
verification_status	Indicates whether the borrower's income was verified by the LC or the income source was verified	string
issue_date	Issue date of the loan	date
loan_status	Current status of the loan	string
payment_plan	Indicates if a payment plan is in place for the loan (indicating the borrower is struggling to pay)	string
purpose	A category provided by the borrower for the loan request	string
dti	Debt-to-income ratio: calculated using the borrower's total monthly debt payments on the total debt obligations, excluding mortgage and the LC loan, divided by the borrower’s self-reported monthly income	float
delinq_2yr	The number of 30+ days past-due payments in the borrower's credit file for the past 2 years	integer
earliest_credit_line	The month the borrower's earliest reported credit line was opened	date
inq_last_6mths	The number of inquiries in the past 6 months (excluding auto and mortgage inquiries)	integer
mths_since_last_record	The number of months since the last public record	integer
open_accounts	The number of open credit lines in the borrower's credit file	integer
total_accounts	The total number of credit lines currently in the borrower's credit file	integer
out_prncp	Remaining outstanding principal for the total amount funded	float
out_prncp_inv	Remaining outstanding principal for the portion of the total amount funded by investors	float
total_payment	Payments received to date for the total amount funded	float
total_rec_int	Interest received to date	float
total_rec_late_fee	Late fees received to date	float
recoveries	Post charge-off gross recovery	float
collection_recovery_fee	Post charge-off collection fee	float
last_payment_date	Date on which last payment was received	date
last_payment_amount	Last total payment amount received	float
next_payment_date	Next scheduled payment date	date
last_credit_pull_date	The most recent month LC pulled credit for this loan	date
collections_12_mths_ex_med	Number of collections in the past 12 months, excluding medical collections	integer
mths_since_last_major_derog	Months since most recent 90-day or worse rating	integer
policy_code	Publicly available policy code: 1 for new products, 2 for products not publicly available	integer
application_type	Indicates whether the loan is an individual application or a joint application with two co-borrowers	string

1. Columns with High Null Percentage (> 50%) Columns:

mths_since_last_record (88.60%) mths_since_last_major_derog (86.17%) next_payment_date (60.13%) mths_since_last_delinq (57.17%)

We deal with these columns by dropping them.

2. Columns with Moderate Null Percentage (5-10%) Columns:

int_rate (9.53%) term (8.80%) funded_amount (5.54%) Approach:

Imputation: Given that these columns have a moderate percentage of null values, imputation is likely the best approach. You can impute int_rate with the mean or median interest rate. For term, you might fill in the mode (most common loan term). funded_amount could be imputed with the mean, median, or even a predicted value based on other available data.

3. Columns with Low Null Percentage (< 5%) Columns:

employment_length (3.91%) last_payment_date (0.13%) collections_12_mths_ex_med (0.09%) last_credit_pull_date (0.01%) Approach:

Imputation: Since these columns have very few missing values, imputation is straightforward. For employment_length, you might fill in with the median or mode. For last_payment_date and last_credit_pull_date, consider using the mode or some forward/backward fill. The collections_12_mths_ex_med might be filled with 0 if it represents the count of collections and nulls could indicate no collections.