70 lines
1.9 KiB
Python
70 lines
1.9 KiB
Python
import pandas as pd
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import preprocessing as pp
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from sklearn.ensemble import RandomForestClassifier
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# Feature Conversion Values
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categorical_columns = ["person_home_ownership",
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"loan_intent",
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"loan_grade",
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"cb_person_default_on_file"]
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person_home_ownership_values = {
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"RENT": 1,
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"MORTGAGE": 2,
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"OWN": 3,
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"OTHER": 4,
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}
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loan_intent_values = {
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"EDUCATIONAL": 1,
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"MEDICAL": 2,
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"VENTURE": 3,
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"PERSONAL": 4,
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"DEBTCONSOLIDATION": 5
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}
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loan_grade_values = {
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"A": 1,
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"B": 2,
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"C": 3,
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"D": 4,
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"E": 5
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}
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cb_person_default_on_file_values = {
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"Y": 1,
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"N": 0,
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}
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def get_default_predictor():
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data = pd.from_csv("credit_risk_dataset.csv")
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data = convert_categories(data)
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# Imputation
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data = pp.impute_missing_values(data)
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# Training and Testing Preperation
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train_features, train_target, test_features, test_target = pp.training_test_split(data, "loan_status")
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# Normalise the data
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training_features, test_features = pp.normalise(train_features,
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test_features)
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# Init Models
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rf_model = random_forest_classifier(training_features,
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train_target)
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return rf_model
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def random_forest_classifier(training_features, training_target):
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model = RandomForestClassifier(max_features="log2",
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random_state=79,
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n_jobs=-1)
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return model
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def convert_categories(data):
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data["person_home_ownership"] = data["person_home_ownership"].map(
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person_home_ownership_values)
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data["loan_intent"] = data["loan_intent"].map(loan_intent_values)
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data["loan_grade"] = data["loan_grade"].map(loan_grade_values)
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data["cb_person_default_on_file"] = data["cb_person_default_on_file"].map(
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cb_person_default_on_file_values)
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return data
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