Upload stringleveldigits_159.py

stringleveldigits_159.py

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+# -*- coding: utf-8 -*-
+"""stringleveldigits.159
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1PYxiyOc2syUh3LwBeNHT7Ks2uQcfVk_n
+"""
+
+import numpy as np
+import pandas as pd
+
+import os
+
+for dirnam, _, filenames in os.walk('financial_risk_assessment.csv'):
+  for filename in filenames:
+    print(os.path.join(dirname, filename))
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.impute import SimpleImputer
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import classification_report, confusion_matrix
+
+sns.set(style="whitegrid")
+
+df = pd.read_csv('financial_risk_assessment.csv')
+
+df.head()
+
+df.info()
+
+df.describe(include=[np.number])
+
+df.describe(include=[object])
+
+df.isnull().sum()
+
+plt.figure(figsize=(8,6))
+sns.countplot(x='Risk Rating', data=df)
+plt.title('Distribution of Risk Ratings')
+plt.show()
+
+num_features = ['Age', 'Income', 'Credit Score', 'Loan Amount', 'Years at Current Job',
+                'Debt-to-Income Ratio', 'Assets Value', 'Number of Dependents', 'Previous Defaults']
+df[num_features].hist(figsize=(15,12), bins=30, edgecolor='black')
+plt.suptitle('Histograms of Numerical Features')
+plt.show()
+
+plt.figure(figsize=(15,10))
+for i, feature in enumerate(num_features):
+  plt.subplot(3, 3, i+1)
+  sns.boxplot(x='Risk Rating', y=feature, data=df)
+  plt.title(f'Boxplot of {feature}')
+plt.tight_layout()
+plt.show()
+
+plt.figure(figsize=(12,10))
+correlation_matrix = df[num_features].corr()
+sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', vmin=-1, vmax=1)
+plt.title('Correlation Heatmap')
+plt.show()
+
+for column in['Gender', 'Education Level', 'Marital Status', 'Loan Purpose', 'Employment Status', 'Payment History', 'City', 'State', 'Country']:
+  print('f{column} unique values:')
+  print(df[column].value_counts())
+  print()
+
+X = df.drop('Risk Rating', axis=1)
+y = df['Risk Rating']
+
+numeric_features = ['Age', 'Income', 'Credit Score', 'Loan Amount', 'Years at Current Job', 'Debt-to-Income Ratio', 'Assets Value', 'Number of Dependents', 'Previous Defaults', 'Marital Status Change']
+categorical_features = ['Gender', 'Education Level', 'Marital Status', 'Loan Purpose', 'Employment Status', 'Payment History', 'City', 'State', 'Country']
+
+numeric_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())])
+categorical_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='most_frequent')), ('onehot', OneHotEncoder(handle_unknown='ignore'))])
+preprocessor = ColumnTransformer(transformers=[('num', numeric_transformer, numeric_features),('cat', categorical_transformer, categorical_features)])
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+model = Pipeline(steps=[('preprocessor', preprocessor), ('classifier', RandomForestClassifier(n_estimators=100, random_state=42))])
+
+model.fit(X_train, y_train)
+
+y_pred = model.predict(X_test)
+
+print("Classification Report:")
+print(classification_report(y_test, y_pred))
+
+conf_matrix = confusion_matrix(y_test, y_pred)
+plt.figure(figsize=(10,7))
+sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Low', 'Medium', 'High'], yticklabels=['Low','Medium', 'High'])
+plt.xlabel('Predicted')
+plt.ylabel('Actual')
+plt.title('Confusion Matrix')
+plt.show()