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P2Pdeliquency.py

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+#import necessary libraries and utilities
+import pickle
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.preprocessing import StandardScaler,PolynomialFeatures
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import Ridge
+from sklearn.model_selection import GridSearchCV
+from sklearn.decomposition import PCA
+from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error
+
+
+#def read_data(url = 'https://github.com/amaysood/Cybersprint/raw/main/loans_clean_schema.csv'):
+    #data=pd.read_csv(url)
+    #return data
+
+#Fucntion that fetches Dataframe from required csv
+def read_data():
+    data=pd.read_csv('loans_clean_schema.csv')
+    return data
+
+#removing missing values from the dataset
+def data_clean(df):
+    df.dropna(axis = 0, inplace=True)
+    return df
+    
+#defining fucntion for onehotencode to use later
+def onehot_encode(df, column, prefix):
+    df = df.copy()
+    dummies = pd.get_dummies(df[column], prefix = prefix)
+    df = pd.concat([df, dummies], axis = 1)
+    df = df.drop(column, axis = 1)
+    return df
+    
+#encoding the categorical data in the dataset to numerical
+def data_encoding(data): 
+    
+    # Converting type of columns to category
+    data['emp_title']=data['emp_title'].astype('category')
+    
+    
+    #Assigning numerical values and storing it in another columns
+    data['emp_title']=data['emp_title'].cat.codes
+    
+    #Onehot encoding
+    df = onehot_encode(data, 'homeownership', prefix = "ho")
+    df = onehot_encode(df, 'loan_purpose', 'lp')
+
+    return df
+    
+#Scaling the data 
+def data_normalization(data):
+    #Splitting the data into dependant and independant variables
+    y=data['account_never_delinq_percent'].copy()
+    X=data.drop('account_never_delinq_percent',axis=1).copy()
+    #Scaling
+    scaling=StandardScaler()
+    X=pd.DataFrame(scaling.fit_transform(X),columns=X.columns)
+    #carrying out PCA to reduce dimensionality 
+    pca = PCA(n_components=26)
+    X = pca.fit_transform(X)
+    return X,y
+
+
+#Preprocessing inputs to train model
+def preprocessing_inputs(data):
+    df=read_data()
+    data=data_clean(df)
+    data1=data_encoding(data)
+    X,y=data_normalization(data1)
+    return X,y
+
+#training the model
+def train(data):
+    #preprocess inputs
+    X,y=preprocessing_inputs(data)
+    #split the given dataset into train and test set
+    X_train,X_test,y_train,y_test=train_test_split(X,y,train_size=0.9,random_state=42)
+    #using Ridge regression with cross validation
+    model=Ridge()
+    #Adding a Polynomial degree to inputs to eliminate problems with linearity
+    poly = PolynomialFeatures(degree=2)
+    X_train_poly = poly.fit_transform(X_train)
+    X_test_poly = poly.transform(X_test)
+    #Carrying out cross-validation for hyperparameter optimization in Ridge Regression 
+    param_grid = {'alpha': np.logspace(-3, 3, 10)}
+    grid_search = GridSearchCV(model, param_grid, cv=5)
+    grid_search.fit(X_train_poly,y_train)
+    #print the best alpha and score
+    print('Best alpha:', grid_search.best_params_)
+    print('Best score:', grid_search.best_score_)
+    #Train Ridge model with best value of alpha
+    best_ridge = grid_search.best_estimator_
+    best_ridge.fit(X_train_poly, y_train)
+    # save the trained model as a pickle file
+    with open('model.pickle', 'wb') as f:
+        pickle.dump(best_ridge, f)
+    return X_test_poly,best_ridge,y_test
+
+
+#carrying out predictions
+def predict(X_test_poly,model,y_test):
+    y_pred=model.predict(X_test_poly)
+    y_pred=y_pred.clip(None,100)
+    return y_pred
+    #scoring metrics
+    #print( r2_score(y_test, y_pred))
+    #print( mean_absolute_error(y_test, y_pred))
+    #print( mean_squared_error(y_test, y_pred))
+
+if __name__ == '__main__':
+    data=read_data()
+    X_test_poly,model,y_test=train(data)
+    predict(X_test_poly,model,y_test)
+    
+
+
+
+
+
+
+

model.pickle

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b5a14b25dbcc00fba54fa5bd000fe8ceed2078fafb9b21c3650da8907900546a
+size 3496