# -*- 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()