Added Risk Predection System with PDF generation

Author: Asim-Sidd02

risk_predection_system/ML_Model_Report2.pdf

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+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+data = pd.read_csv('german_credit_data.csv')
+
+print(data.head())
+print(data.info())
+print(data.isnull().sum())
+
+sns.countplot(x='Risk', data=data)
+plt.title('Distribution of Risk')
+plt.show()

risk_predection_system/dataExploration.py

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+from sklearn.preprocessing import LabelEncoder, StandardScaler
+import pandas as pd
+
+data = pd.read_csv('german_credit_data.csv')
+
+label_encoders = {}
+for column in data.select_dtypes(include=['object']).columns:
+    label_encoders[column] = LabelEncoder()
+    data[column] = label_encoders[column].fit_transform(data[column])
+
+scaler = StandardScaler()
+numerical_features = data.select_dtypes(include=['int64', 'float64']).columns
+data[numerical_features] = scaler.fit_transform(data[numerical_features])
+print(data.head())

risk_predection_system/featureEngineering.py

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+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import LabelEncoder, StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
+from matplotlib.backends.backend_pdf import PdfPages
+
+data = pd.read_csv('german_credit_data.csv')
+
+label_encoders = {}
+for column in data.select_dtypes(include=['object']).columns:
+    label_encoders[column] = LabelEncoder()
+    data[column] = label_encoders[column].fit_transform(data[column])
+
+scaler = StandardScaler()
+numerical_features = data.select_dtypes(include=['int64', 'float64']).columns
+data[numerical_features] = scaler.fit_transform(data[numerical_features])
+
+print(data.head())
+print("Unique values in 'Risk' column:", data['Risk'].unique())
+
+
+X = data.drop('Risk', axis=1)
+y = data['Risk']  
+y = y.astype(int)  
+
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+
+
+model = RandomForestClassifier(random_state=42)
+model.fit(X_train, y_train)
+
+
+y_pred = model.predict(X_test)
+
+
+print("Accuracy:", accuracy_score(y_test, y_pred))
+print("Classification Report:\n", classification_report(y_test, y_pred))
+print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
+
+
+importances = model.feature_importances_
+feature_importance_df = pd.DataFrame({'Feature': X.columns, 'Importance': importances})
+feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)
+
+
+with PdfPages('ML_Model_Report2.pdf') as pdf:
+    plt.figure(figsize=(10, 6))
+    sns.countplot(x='Risk', data=data)
+    plt.title('Distribution of Risk')
+    pdf.savefig()  
+    plt.close()
+    plt.figure(figsize=(10, 6))
+    sns.barplot(x='Importance', y='Feature', data=feature_importance_df)
+    plt.title('Feature Importance')
+    pdf.savefig()
+    plt.close()
+    
+    fig, ax = plt.subplots(figsize=(10, 6))
+    ax.text(0.01, 1.25, str('Classification Report:\n'), {'fontsize': 10}, fontproperties='monospace')
+    ax.text(0.01, 0.05, str(classification_report(y_test, y_pred)), {'fontsize': 10}, fontproperties='monospace')
+    ax.axis('off')
+    pdf.savefig()
+    plt.close()
+    
+    fig, ax = plt.subplots(figsize=(10, 6))
+    sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, fmt="d", ax=ax)
+    ax.set_title('Confusion Matrix')
+    pdf.savefig()
+    plt.close()
+
+print("PDF report 'ML_Model_Report2.pdf' has been generated successfully.")

risk_predection_system/german_credit_data.csv

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+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
+
+data = pd.read_csv('german_credit_data.csv')
+
+label_encoders = {}
+for column in data.select_dtypes(include=['object']).columns:
+    label_encoders[column] = LabelEncoder()
+    data[column] = label_encoders[column].fit_transform(data[column])
+
+X = data.drop('Risk', axis=1)
+y = data['Risk']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+
+model = RandomForestClassifier(random_state=42)
+model.fit(X_train, y_train)
+
+y_pred = model.predict(X_test)
+
+print("Accuracy:", accuracy_score(y_test, y_pred))
+print("Classification Report:\n", classification_report(y_test, y_pred))
+print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))