Delete loan.py

loan.py

@@ -1,212 +0,0 @@
-# -*- coding: utf-8 -*-
-"""loan.py"""
-
-# Import necessary libraries
-import numpy as np
-import pandas as pd
-import seaborn as sns
-import matplotlib.pyplot as plt
-import warnings
-from sklearn.preprocessing import OneHotEncoder, StandardScaler
-from sklearn.compose import ColumnTransformer
-from sklearn.pipeline import Pipeline
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LogisticRegression
-from imblearn.over_sampling import SMOTE
-from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score
-import gradio as gr
-from imblearn.pipeline import Pipeline as ImbPipeline
-import joblib
-from datasets import load_dataset  # Import the Hugging Face dataset library
-
-# Suppress specific FutureWarnings
-warnings.simplefilter(action='ignore', category=FutureWarning)
-
-# Load dataset directly from Hugging Face
-dataset = load_dataset("AnguloM/loan_data")
-
-# Access the train and test data
-df_train = dataset['train']
-
-
-# Convert dataset to pandas DataFrame
-df_train = pd.DataFrame(df_train)
-
-from sklearn.model_selection import train_test_split
-
-df_train, df_test = train_test_split(df_train, test_size=0.2, random_state=42)
-
-
-# Create a summary DataFrame with data types and non-null counts
-info_df = pd.DataFrame({
-    "Column": df_train.columns,
-    "Data Type": df_train.dtypes,
-    "Non-Null Count": df_train.notnull().sum(),
-    "Total Count": len(df_train)
-})
-
-# Calculate the percentage of non-null values in each column
-info_df['Non-Null Percentage'] = (info_df['Non-Null Count'] / info_df['Total Count'] * 100).round(2).astype(str) + '%'
-
-# Style the table
-info_df_styled = info_df.style.set_properties(**{'text-align': 'left'}).set_table_styles(
-    [{'selector': 'th', 'props': [('background-color', '#d9edf7'), ('color', '#31708f'), ('font-weight', 'bold')]}]
-)
-
-# Apply background gradient only to numerical columns
-info_df_styled = info_df_styled.background_gradient(subset=['Non-Null Count', 'Total Count'], cmap="Oranges")
-
-# Create a widget to display the styled table
-table_widget = widgets.Output()
-with table_widget:
-    display(info_df_styled)
-
-# Widget for the missing values message
-message_widget = widgets.Output()
-with message_widget:
-    print(f"\033[1;31mMissing values detected in any columns:\033[0m\n{df_train.isnull().sum()}")
-
-# Display both widgets (table and missing values message) side by side
-widgets.HBox([table_widget, message_widget])
-
-# Convert relevant columns to categorical if necessary
-df_train['not.fully.paid'] = df_train['not.fully.paid'].astype('category')
-
-# Select only numeric columns for correlation matrix calculation
-df_numeric = df_train.select_dtypes(include=[float, int])
-
-# Create a 1x2 grid for the plots
-plt.figure(figsize=(12, 6))
-
-# Create subplots for the correlation matrix and target distribution
-fig, axes = plt.subplots(1, 2, figsize=(14, 6))
-
-# Plot Correlation Matrix
-sns.heatmap(df_numeric.corr(), annot=True, cmap='coolwarm', ax=axes[0], fmt='.2f')
-axes[0].set_title('Correlation Matrix')
-
-# Plot Distribution of Loan Repayment Status (Target Variable)
-sns.countplot(x='not.fully.paid', data=df_train, ax=axes[1])
-axes[1].set_title('Distribution of Loan Repayment Status')
-
-# Show the plots
-plt.tight_layout()  # Adjusts the layout to avoid overlapping
-plt.show()
-
-# OneHotEncoding for categorical columns and scaling for numeric columns
-# Prepare data for training
-data = df_train.copy()
-
-# Separate features (X) and target (y)
-X = data.drop('credit.policy', axis=1)  # Drop the target column
-y = data['credit.policy']  # Target variable
-
-# Split the data into training (80%) and testing (20%) sets
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-# Preprocessing pipeline (scaling numeric features and encoding categorical features)
-preprocessor = ColumnTransformer(
-    transformers=[
-        ('num', StandardScaler(), ['int.rate', 'installment', 'log.annual.inc', 'dti', 'fico',
-                                   'days.with.cr.line', 'revol.bal', 'revol.util', 'inq.last.6mths',
-                                   'delinq.2yrs', 'pub.rec']),
-        ('cat', OneHotEncoder(), ['purpose'])  # Ensure 'purpose' is included in categorical transformations
-    ]
-)
-
-# Create an imbalanced-learn pipeline that includes SMOTE and Logistic Regression
-imb_model_pipeline = ImbPipeline(steps=[
-    ('preprocessor', preprocessor),  # First, preprocess the data (scale numeric, encode categorical)
-    ('smote', SMOTE(random_state=42, sampling_strategy=0.5)),  # Apply SMOTE to balance the dataset
-    ('classifier', LogisticRegression(max_iter=1000000))  # Logistic Regression classifier
-])
-
-# Train the model with the full pipeline (preprocessing + SMOTE + model training)
-imb_model_pipeline.fit(X_train, y_train)
-
-# Make predictions on the test data
-y_pred = imb_model_pipeline.predict(X_test)
-y_pred_proba = imb_model_pipeline.predict_proba(X_test)[:, 1]  # Get probabilities for the positive class
-
-# Adjust the decision threshold to improve recall of the positive class
-threshold = 0.3
-y_pred_adjusted = (y_pred_proba >= threshold).astype(int)
-
-# Evaluate the model using classification report
-classification_rep = classification_report(y_test, y_pred_adjusted, output_dict=True)
-
-# Convert the classification report to a DataFrame for display as a table with styles
-classification_df = pd.DataFrame(classification_rep).transpose()
-classification_df_styled = classification_df.style.set_properties(**{'text-align': 'center'}).set_table_styles(
-    [{'selector': 'th', 'props': [('background-color', '#d9edf7'), ('color', '#31708f'), ('font-weight', 'bold')]}]
-)
-
-# Display the classification report as a styled table in a widget
-table_widget = widgets.Output()
-with table_widget:
-    display(classification_df_styled)
-
-# Calculate the AUC-ROC score
-auc_roc = roc_auc_score(y_test, y_pred_proba)
-
-# Widget for the AUC-ROC
-auc_widget = widgets.Output()
-with auc_widget:
-    print("\033[1;31mAUC-ROC:\033[0m", f"{auc_roc:.4f}")
-
-# Display both widgets (table and AUC-ROC message) side by side
-display(widgets.VBox([table_widget, auc_widget]))
-
-# Display the confusion matrix
-cm = confusion_matrix(y_test, y_pred_adjusted)
-sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')
-plt.title("Confusion Matrix")
-plt.xlabel("Predicted")
-plt.ylabel("Actual")
-plt.show()
-
-from huggingface_hub import hf_hub_download
-import joblib
-
-
-model_path = hf_hub_download(repo_id="AnguloM/LoanSmart_Predict_Loan_Approval_with_Confidence", filename="loan_approval_pipeline.pkl")
-
-
-pipeline = joblib.load(model_path)
-# Prediction function
-def predict_approval(int_rate, installment, log_annual_inc, dti, fico,
-                     days_with_cr_line, revol_bal, revol_util, inq_last_6mths,
-                     delinq_2yrs, pub_rec, purpose):
-    # Prepare the input as a DataFrame
-    input_data = pd.DataFrame([[int_rate, installment, log_annual_inc, dti, fico,
-                               days_with_cr_line, revol_bal, revol_util,
-                               inq_last_6mths, delinq_2yrs, pub_rec, purpose]],
-                             columns=['int.rate', 'installment', 'log.annual.inc',
-                                      'dti', 'fico', 'days.with.cr.line', 'revol.bal',
-                                      'revol.util', 'inq.last.6mths', 'delinq.2yrs',
-                                      'pub.rec', 'purpose'])
-    # Make loan approval prediction
-    result = pipeline.predict(input_data)
-    return result[0]
-
-
-# Create input components for the Gradio interface
-inputs = [
-    gr.Slider(0.0, 25.0, step=0.1, label="Interest Rate (%)"),
-    gr.Slider(0.0, 1000.0, step=10.0, label="Installment Amount"),
-    gr.Slider(0.0, 15.0, step=0.1, label="Log of Annual Income"),
-    gr.Slider(0.0, 50.0, step=0.1, label="Debt-to-Income Ratio"),
-    gr.Slider(300, 850, step=1, label="FICO Credit Score"),
-    gr.Slider(0.0, 50000.0, step=100.0, label="Days with Credit Line"),
-    gr.Slider(0.0, 100000.0, step=500.0, label="Revolving Balance"),
-    gr.Slider(0.0, 150.0, step=0.1, label="Revolving Utilization (%)"),
-    gr.Slider(0, 10, step=1, label="Recent Inquiries (Last 6 Months)"),
-    gr.Slider(0, 10, step=1, label="Delinquencies in Last 2 Years"),
-    gr.Slider(0, 5, step=1, label="Public Records"),
-    gr.Dropdown(["credit_card", "debt_consolidation", "educational",
-                 "home_improvement", "major_purchase", "small_business",
-                 "other"], label="Loan Purpose")
-]
-
-# Create the Gradio interface for loan approval prediction
-gr.Interface(fn=predict_approval, inputs=inputs, outputs="text").launch(share=True)