import pandas as pd
from sklearn.impute import KNNImputer

dataframe = pd.read_csv("credit_risk_dataset.csv")

# Feature Conversion Values
person_home_ownership_values = {
    "RENT": 1,
    "MORTGAGE": 2,
    "OWN": 3,
    "OTHER": 4,
}
loan_intent_values = {
    "EDUCATIONAL": 1,
    "MEDICAL": 2,
    "VENTURE": 3,
    "PERSONAL": 4,
    "DEBTCONSOLIDATION": 5
    }
loan_grade_values = {
    "A": 1,
    "B": 2,
    "C": 3,
    "D": 4,
    "E": 5
}
cb_person_default_on_file_values = {
    "Y": 1,
    "N": 0,
}

dataframe["person_home_ownership"] = dataframe["person_home_ownership"].map(person_home_ownership_values)
dataframe["loan_intent"] = dataframe["loan_intent"].map(loan_intent_values)
dataframe["loan_grade"] = dataframe["loan_grade"].map(loan_grade_values)
dataframe["cb_person_default_on_file"] = dataframe["cb_person_default_on_file"].map(cb_person_default_on_file_values)

imputer = KNNImputer(n_neighbors=9, weights="uniform", metric="nan_euclidean")
imputed_data = imputer.fit_transform(dataframe)
pd.DataFrame(imputed_data,
             columns=dataframe.columns).to_csv("imputed_data.csv", index=False)