Untitled

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score, roc_auc_score, classification_report

# Load COMPAS dataset
compas_data = pd.read_csv('compas-scores-two-years.csv')

# Data Preprocessing
# Selecting relevant features and target variable
features = ['age', 'priors_count', 'juv_fel_count', 'juv_misd_count', 'juv_other_count', 'c_charge_degree_F']
X = pd.get_dummies(compas_data[features], drop_first=True)
y = compas_data['is_recid']  # Target variable: Recidivism

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Standardizing the features
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Logistic Regression Model
log_reg = LogisticRegression()
log_reg.fit(X_train, y_train)
y_pred_log_reg = log_reg.predict(X_test)

# Neural Network Model
mlp = MLPClassifier(hidden_layer_sizes=(50,), max_iter=1000, random_state=42)
mlp.fit(X_train, y_train)
y_pred_mlp = mlp.predict(X_test)

# Evaluation
# Accuracy
accuracy_log_reg = accuracy_score(y_test, y_pred_log_reg)
accuracy_mlp = accuracy_score(y_test, y_pred_mlp)

# AUC-ROC
roc_log_reg = roc_auc_score(y_test, log_reg.predict_proba(X_test)[:, 1])
roc_mlp = roc_auc_score(y_test, mlp.predict_proba(X_test)[:, 1])

# Fairness metrics (Example: False Positive Rate and False Negative Rate)
report_log_reg = classification_report(y_test, y_pred_log_reg, target_names=['No Recidivism', 'Recidivism'])
report_mlp = classification_report(y_test, y_pred_mlp, target_names=['No Recidivism', 'Recidivism'])

# Output results
print(f"Logistic Regression - Accuracy: {accuracy_log_reg}, AUC-ROC: {roc_log_reg}")
print(f"Neural Network - Accuracy: {accuracy_mlp}, AUC-ROC: {roc_mlp}")
print("\nLogistic Regression Classification Report:\n", report_log_reg)
print("\nNeural Network Classification Report:\n", report_mlp)