Bias-Mitigation-Techniques-in-Supervised-Learning-A-Comparative-Study

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Bias-Mitigation-Techniques-in-Supervised-Learning-A-Comparative-Study

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Bias Mitigation Techniques in Supervised Learning: A Comparative Study

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# Bias-Mitigation-Techniques-in-Supervised-Learning-A-Comparative-Study
Bias Mitigation Techniques in Supervised Learning: A Comparative Study
# -*- coding: utf-8 -*-
"""Bais Mitigation Techniques in Supervised Learning: A Comparative Study.ipynb

"""

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, roc_auc_score, classification_report
import matplotlib.pyplot as plt
import seaborn as sns

# Load dataset
df = pd.read_csv(r"C:\Users\rohan\Downloads\bias_mitigation_dataset.csv")

# Preprocess data
# Encode categorical variables
le = LabelEncoder()
categorical_cols = ['gender', 'race', 'education_level', 'employment_status']
for col in categorical_cols:
    df[col] = le.fit_transform(df[col])

# Define features and target
X = df.drop('target', axis=1)
y = df['target']

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Scale numerical features
scaler = StandardScaler()
numerical_cols = ['age', 'income', 'prior_offenses', 'credit_score']
X_train[numerical_cols] = scaler.fit_transform(X_train[numerical_cols])
X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])

# Train Random Forest model
rf = RandomForestClassifier(n_estimators=100, random_state=42)
rf.fit(X_train, y_train)

# Predict and evaluate
y_pred = rf.predict(X_test)
print("=== Model Performance ===")
print(f"Accuracy: {accuracy_score(y_test, y_pred):.4f}")
print(f"Precision: {precision_score(y_test, y_pred):.4f}")
print(f"Recall: {recall_score(y_test, y_pred):.4f}")
print(f"AUC-ROC: {roc_auc_score(y_test, y_pred):.4f}")
print("\nClassification Report:\n", classification_report(y_test, y_pred))

# Check fairness (demographic parity by gender)
X_test['predicted'] = y_pred
X_test['gender'] = df.loc[X_test.index, 'gender']  # Revert to original gender labels
parity = X_test.groupby('gender')['predicted'].mean()
print("\n=== Fairness Check (Demographic Parity by Gender) ===")
print(parity)

# Visualize fairness
plt.figure(figsize=(8, 5))
sns.barplot(x=parity.index, y=parity.values)
plt.title("Predicted Demographic Parity by Gender")
plt.ylabel("Proportion of Predicted Target=1")
plt.savefig("predicted_parity.png")
plt.show()

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder

# Load dataset
df = pd.read_csv(r"C:\Users\rohan\Downloads\bias_mitigation_dataset.csv")

# Encode categorical variables
le = LabelEncoder()
categorical_cols = ['gender', 'race', 'education_level', 'employment_status']
for col in categorical_cols:
    df[col] = le.fit_transform(df[col])

# Define features and target
X = df.drop('target', axis=1)
y = df['target']

# Train Random Forest to get feature importance
rf = RandomForestClassifier(n_estimators=100, random_state=42)
rf.fit(X, y)

# Feature importance
importance = pd.Series(rf.feature_importances_, index=X.columns).sort_values(ascending=False)

# Correlation matrix
corr_matrix = df.corr()

# Visualize feature importance
plt.figure(figsize=(10, 5))
plt.subplot(1, 2, 1)
sns.barplot(x=importance.values, y=importance.index)
plt.title("Feature Importance (Random Forest)")
plt.xlabel("Importance")

# Visualize correlation matrix
plt.subplot(1, 2, 2)
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f')
plt.title("Correlation Matrix")

plt.tight_layout()
plt.savefig("feature_analysis.png")
plt.show()

# Print key findings
print("=== Feature Importance ===")
print(importance)
print("\n=== Top Correlations with Target ===")
print(corr_matrix['target'].sort_values(ascending=False))

import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder, StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns

# Load dataset
df = pd.read_csv(r"C:\Users\rohan\Downloads\bias_mitigation_dataset.csv")

# Check for missing values
print("=== Missing Values ===")
print(df.isnull().sum())

# Handle missing values (if any, impute with median for numerical, mode for categorical)
for col in ['age', 'income', 'prior_offenses', 'credit_score']:
    df[col].fillna(df[col].median(), inplace=True)
for col in ['gender', 'race', 'education_level', 'employment_status']:
    df[col].fillna(df[col].mode()[0], inplace=True)

# Encode categorical variables
le = LabelEncoder()
categorical_cols = ['gender', 'race', 'education_level', 'employment_status']
for col in categorical_cols:
    df[col] = le.fit_transform(df[col])

# Scale numerical features
scaler = StandardScaler()
numerical_cols = ['age', 'income', 'prior_offenses', 'credit_score']
df[numerical_cols] = scaler.fit_transform(df[numerical_cols])

# Detect outliers using IQR
def detect_outliers(df, col):
    Q1 = df[col].quantile(0.25)
    Q3 = df[col].quantile(0.75)
    IQR = Q3 - Q1
    lower_bound = Q1 - 1.5 * IQR
    upper_bound = Q3 + 1.5 * IQR
    outliers = df[(df[col] < lower_bound) | (df[col] > upper_bound)][col]
    return len(outliers)

print("\n=== Outlier Count ===")
for col in numerical_cols:
    print(f"{col}: {detect_outliers(df, col)} outliers")

# Visualize distributions
plt.figure(figsize=(15, 10))
for i, col in enumerate(numerical_cols, 1):
    plt.subplot(2, 2, i)
    sns.histplot(df[col], kde=True)
    plt.title(f"Distribution of {col}")
plt.tight_layout()
plt.savefig("distributions.png")
plt.show()

# Summary report
print("\n=== Data Summary ===")
print(df.describe())

"""# New Section"""

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

# Load dataset
df = pd.read_csv(r"C:\Users\rohan\Downloads\bias_mitigation_dataset.csv")

# Income by race and gender
plt.figure(figsize=(12, 6))
sns.boxplot(x='race', y='income', hue='gender', data=df)
plt.title("Income Distribution by Race and Gender")
plt.xticks(rotation=45)
plt.savefig("income_by_race_gender.png")
plt.show()

# Employment status by education level
employment_edu = pd.crosstab(df['education_level'], df['employment_status'])
employment_edu.plot(kind='bar', stacked=True, figsize=(10, 6))
plt.title("Employment Status by Education Level")
plt.xlabel("Education Level")
plt.ylabel("Count")
plt.savefig("employment_by_education.png")
plt.show()

# Age vs. credit score by target
plt.figure(figsize=(10, 6))
sns.scatterplot(x='age', y='credit_score', hue='target', size='target', data=df)
plt.title("Age vs. Credit Score by Target")
plt.savefig("age_vs_credit_score.png")
plt.show()

# Interactive plot with Plotly (income by age and race)
fig = px.scatter(df, x='age', y='income', color='race', size='credit_score',
                 hover_data=['gender', 'employment_status'], title="Income by Age and Race")
fig.write_to_file("interactive_eda.html")
fig.show()

# Print summary statistics
print("=== Summary by Race ===")
print(df.groupby('race')[['income', 'credit_score']].mean())
print("\n=== Summary by Gender ===")
print(df.groupby('gender')[['income', 'credit_score']].mean())
print("\n=== Summary by Employment Status ===")
print(df.groupby('employment_status')[['income', 'credit_score']].mean())

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