Loan Terms & Cashflow EDA

[RigelAlgebar]

Issue 2 – Loan Terms & Cashflow EDA with OOP + Per-Column Functional Summary

Goal
Analyze how loan terms and cashflows behave and how they relate to loan_status.

You must use:

• OOP: create a class that encapsulates loan-terms EDA.
• Functional programming: build a per-column numeric summary using a dict comprehension or map.

---

Columns in scope

You are responsible for the following columns:

Loan structure & pricing

• loan_amnt
• funded_amnt
• funded_amnt_inv
• term
• int_rate
• installment
• grade
• sub_grade
• initial_list_status

Cashflow / lifecycle

• out_prncp
• out_prncp_inv
• total_pymnt
• total_pymnt_inv
• total_rec_prncp
• total_rec_int
• total_rec_late_fee
• recoveries
• collection_recovery_fee
• last_pymnt_d
• last_pymnt_amnt
• next_pymnt_d

Shared target

• loan_status (used for default-rate calculations)

---

Files to edit

• src/eda_loan_terms.py
• notebooks/eda_loan_terms_demo.ipynb

---

1. Implement the LoanTermsEDA class

In src/eda_loan_terms.py, create and implement:

"""
import pandas as pd
from typing import Dict, Any, List

NUMERIC_TERM_COLS = [
"loan_amnt", "funded_amnt", "funded_amnt_inv",
"int_rate", "installment",
"out_prncp", "out_prncp_inv",
"total_pymnt", "total_pymnt_inv",
"total_rec_prncp", "total_rec_int",
"total_rec_late_fee", "recoveries", "collection_recovery_fee",
"last_pymnt_amnt",
]

CATEGORICAL_TERM_COLS = [
"term", "grade", "sub_grade", "initial_list_status",
]

class LoanTermsEDA:
def init(self, df: pd.DataFrame, target_col: str = "loan_status"):
"""
Store the full DataFrame and the name of the target column.
"""
self.df = df
self.target_col = target_col

def numeric_terms_summary(self) -> pd.DataFrame:
    """
    Return df[NUMERIC_TERM_COLS].describe().T
    (one row per numeric term/cashflow column).
    """
    ...

def distribution_stats_for_column(self, col: str) -> Dict[str, float]:
    """
    For a given numeric column, return stats in a dict:
    - mean
    - std
    - min
    - max
    - q25 (25% quantile)
    - q50 (median)
    - q75 (75% quantile)
    """
    ...

def default_rate_by_term(self) -> pd.Series:
    """
    Default rate (mean of target) per 'term' category.
    """
    ...

def default_rate_by_grade(self) -> pd.Series:
    """
    Default rate per 'grade' (or per 'sub_grade', if you prefer).
    """
    ...

"""

---

2. Functional per-column numeric summary

Add a function that uses functional programming over numeric columns:

"""
def summarize_numeric_terms(eda: LoanTermsEDA) -> Dict[str, Dict[str, float]]:
"""
For each column in NUMERIC_TERM_COLS, call
eda.distribution_stats_for_column(col) and return a dict:
col_name -> stats_dict.

Implement this using a dict comprehension or map
to clearly show functional style.
"""
...

"""

Example idea:

"""
def summarize_numeric_terms(eda: LoanTermsEDA) -> Dict[str, Dict[str, float]]:
return {col: eda.distribution_stats_for_column(col) for col in NUMERIC_TERM_COLS}
"""

---

3. Create the demo notebook

In notebooks/eda_loan_terms_demo.ipynb:

1. Load the dataset:

"""
import pandas as pd
from src.eda_loan_terms import LoanTermsEDA, summarize_numeric_terms

df = pd.read_csv("data/loan_sample.csv") # or the correct path
"""

2. Instantiate the EDA class:

"""
eda = LoanTermsEDA(df, target_col="loan_status")
"""

3. Run EDA:

"""
numeric_summary = eda.numeric_terms_summary()
per_column_stats = summarize_numeric_terms(eda)
default_by_term = eda.default_rate_by_term()
default_by_grade = eda.default_rate_by_grade()
"""

4. Display at least:

"""
numeric_summary # describe() table of numeric loan terms
per_column_stats # dict of stats per numeric column
default_by_term # default rate by term (e.g., 36 vs 60 months)
default_by_grade # default rate by grade/sub_grade
"""

Optionally add a histogram of int_rate or loan_amnt and a short markdown comment.
(But no modeling, only EDA.)

---

Acceptance Criteria ✅

• LoanTermsEDA:

  • Initializes correctly with a DataFrame.
  • numeric_terms_summary() returns a DataFrame with one row per numeric term column.
  • distribution_stats_for_column(col) returns a stats dict for that column.
  • default_rate_by_term() and default_rate_by_grade() return Series with default rates.

• Functional part:

  • summarize_numeric_terms(eda) uses a functional style (e.g. dict comprehension or map) to iterate over NUMERIC_TERM_COLS and call a method for each.

• Notebook:

  • Runs top-to-bottom with no errors.
  • Shows numeric summaries, per-column stats, and default-rate-by-category analyses.
  • Contains only EDA (no model training).