Add chapter-04-data-preprocessing/lab-04-data-cleaning.py

chapter-04-data-preprocessing/lab-04-data-cleaning.py

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+"""
+Lab 4: Data Cleaning & Preprocessing
+=====================================
+BIM5021 - Nhà kho dữ liệu và Tích hợp
+Chương 4: Tiền xử lý dữ liệu
+
+Mục tiêu:
+- Đánh giá chất lượng dữ liệu (Data Quality Assessment)
+- Xử lý Missing Values (nhiều phương pháp)
+- Phát hiện và xử lý Outliers (IQR, Z-score)
+- Normalization (Min-Max, Z-Score, Robust)
+- PCA (Principal Component Analysis)
+- Feature Engineering cho Olist dataset
+
+Yêu cầu: pip install pandas numpy scikit-learn matplotlib seaborn
+"""
+
+import pandas as pd
+import numpy as np
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler
+from sklearn.decomposition import PCA
+from sklearn.impute import KNNImputer
+import warnings
+warnings.filterwarnings('ignore')
+
+
+# ==============================================================================
+# PHẦN 1: Tạo Sample Dataset (mô phỏng Olist)
+# ==============================================================================
+
+def create_sample_olist():
+    """Tạo sample dataset mô phỏng Olist với các vấn đề data quality."""
+    np.random.seed(42)
+    n = 1000
+    
+    # Tạo dữ liệu với các vấn đề data quality có chủ đích
+    df = pd.DataFrame({
+        'order_id': [f'ORD_{i:05d}' for i in range(n)],
+        'price': np.concatenate([
+            np.random.lognormal(4, 1, n-10),   # Normal prices
+            np.array([-50, -10, 0, 0, 0,        # Negative/zero (errors)
+                      5000, 8000, 12000, 15000, 20000])  # Outliers
+        ]),
+        'freight_value': np.concatenate([
+            np.random.exponential(30, n-5),
+            np.array([np.nan, np.nan, np.nan, -5, 500])  # Missing + errors
+        ]),
+        'review_score': np.random.choice([1, 2, 3, 4, 5, np.nan], n,
+                                          p=[0.05, 0.05, 0.1, 0.3, 0.4, 0.1]),
+        'product_weight_g': np.concatenate([
+            np.random.lognormal(6, 1.2, n-3),
+            np.array([np.nan, np.nan, 0])  # Missing + zero
+        ]),
+        'product_length_cm': np.random.uniform(5, 100, n),
+        'product_height_cm': np.random.uniform(2, 80, n),
+        'product_width_cm': np.random.uniform(5, 60, n),
+        'customer_state': np.random.choice(
+            ['SP', 'RJ', 'MG', 'RS', 'PR', 'BA', 'SC', None],
+            n, p=[0.3, 0.15, 0.1, 0.1, 0.08, 0.07, 0.05, 0.15]
+        ),
+        'delivery_days': np.concatenate([
+            np.random.exponential(10, n-5),
+            np.array([np.nan, np.nan, -2, 0, 120])  # Missing + errors + outlier
+        ]),
+        'order_status': np.random.choice(
+            ['delivered', 'shipped', 'canceled', 'processing', 'DELIVERED', 'Delivered'],
+            n, p=[0.6, 0.1, 0.05, 0.05, 0.1, 0.1]
+        ),
+    })
+    
+    # Shuffle
+    df = df.sample(frac=1, random_state=42).reset_index(drop=True)
+    
+    print("=" * 70)
+    print("  SAMPLE DATASET CREATED")
+    print("=" * 70)
+    print(f"  Shape: {df.shape}")
+    print(f"  Columns: {list(df.columns)}")
+    print(f"\n  First 5 rows:")
+    print(df.head().to_string())
+    
+    return df
+
+
+# ==============================================================================
+# PHẦN 2: Data Quality Assessment
+# ==============================================================================
+
+def assess_data_quality(df: pd.DataFrame):
+    """Đánh giá chất lượng dữ liệu toàn diện."""
+    
+    print("\n" + "=" * 70)
+    print("  DATA QUALITY ASSESSMENT")
+    print("=" * 70)
+    
+    # 2.1 Missing Values
+    print("\n--- 1. Missing Values ---")
+    missing = df.isnull().sum()
+    missing_pct = (missing / len(df) * 100).round(2)
+    missing_report = pd.DataFrame({
+        'Missing Count': missing,
+        'Missing %': missing_pct,
+        'Data Type': df.dtypes
+    })
+    missing_report = missing_report[missing_report['Missing Count'] > 0].sort_values(
+        'Missing %', ascending=False
+    )
+    print(missing_report.to_string())
+    
+    # 2.2 Duplicates
+    print("\n--- 2. Duplicates ---")
+    dup_count = df.duplicated().sum()
+    dup_id = df['order_id'].duplicated().sum()
+    print(f"  Full row duplicates: {dup_count}")
+    print(f"  Duplicate order_ids: {dup_id}")
+    
+    # 2.3 Negative/Invalid Values
+    print("\n--- 3. Invalid Values ---")
+    numeric_cols = df.select_dtypes(include=[np.number]).columns
+    for col in numeric_cols:
+        neg_count = (df[col] < 0).sum()
+        zero_count = (df[col] == 0).sum()
+        if neg_count > 0 or (zero_count > 0 and col != 'review_score'):
+            print(f"  {col}: {neg_count} negative, {zero_count} zeros")
+    
+    # 2.4 Consistency check
+    print("\n--- 4. Consistency ---")
+    if 'order_status' in df.columns:
+        print(f"  order_status unique values: {df['order_status'].unique()}")
+        print(f"  → Inconsistency: mixed case (delivered, DELIVERED, Delivered)")
+    
+    # 2.5 Outliers (IQR method)
+    print("\n--- 5. Outliers (IQR Method) ---")
+    for col in ['price', 'freight_value', 'delivery_days', 'product_weight_g']:
+        if col in df.columns:
+            data = df[col].dropna()
+            Q1 = data.quantile(0.25)
+            Q3 = data.quantile(0.75)
+            IQR = Q3 - Q1
+            lower = Q1 - 1.5 * IQR
+            upper = Q3 + 1.5 * IQR
+            outliers = ((data < lower) | (data > upper)).sum()
+            print(f"  {col}: Q1={Q1:.1f}, Q3={Q3:.1f}, IQR={IQR:.1f}, "
+                  f"bounds=[{lower:.1f}, {upper:.1f}], outliers={outliers} ({outliers/len(data)*100:.1f}%)")
+    
+    # 2.6 Summary Score
+    print("\n--- 6. Quality Score ---")
+    total_cells = df.shape[0] * df.shape[1]
+    missing_cells = df.isnull().sum().sum()
+    completeness = (1 - missing_cells / total_cells) * 100
+    print(f"  Completeness: {completeness:.1f}%")
+    print(f"  Total issues found: missing={missing_cells}, duplicates={dup_count}")
+
+
+# ==============================================================================
+# PHẦN 3: Data Cleaning
+# ==============================================================================
+
+def clean_data(df: pd.DataFrame) -> pd.DataFrame:
+    """Làm sạch dữ liệu."""
+    
+    print("\n" + "=" * 70)
+    print("  DATA CLEANING")
+    print("=" * 70)
+    
+    df_clean = df.copy()
+    original_shape = df_clean.shape
+    
+    # 3.1 Standardize categorical values
+    print("\n--- Step 1: Standardize Categories ---")
+    if 'order_status' in df_clean.columns:
+        before = df_clean['order_status'].nunique()
+        df_clean['order_status'] = df_clean['order_status'].str.lower().str.strip()
+        after = df_clean['order_status'].nunique()
+        print(f"  order_status: {before} → {after} unique values")
+    
+    if 'customer_state' in df_clean.columns:
+        df_clean['customer_state'] = df_clean['customer_state'].str.upper().str.strip()
+    
+    # 3.2 Handle invalid values
+    print("\n--- Step 2: Fix Invalid Values ---")
+    # Remove negative prices
+    neg_price = (df_clean['price'] < 0).sum()
+    df_clean.loc[df_clean['price'] < 0, 'price'] = np.nan
+    print(f"  price: {neg_price} negative values → set to NaN")
+    
+    # Remove negative freight
+    neg_freight = (df_clean['freight_value'] < 0).sum()
+    df_clean.loc[df_clean['freight_value'] < 0, 'freight_value'] = np.nan
+    print(f"  freight_value: {neg_freight} negative values → set to NaN")
+    
+    # Remove negative delivery_days
+    neg_delivery = (df_clean['delivery_days'] < 0).sum()
+    df_clean.loc[df_clean['delivery_days'] < 0, 'delivery_days'] = np.nan
+    print(f"  delivery_days: {neg_delivery} negative values → set to NaN")
+    
+    # 3.3 Handle missing values
+    print("\n--- Step 3: Impute Missing Values ---")
+    
+    # Numerical: median imputation (robust to outliers)
+    for col in ['price', 'freight_value', 'product_weight_g', 'delivery_days']:
+        if col in df_clean.columns:
+            n_missing = df_clean[col].isna().sum()
+            median_val = df_clean[col].median()
+            df_clean[col].fillna(median_val, inplace=True)
+            print(f"  {col}: {n_missing} missing → filled with median ({median_val:.2f})")
+    
+    # Categorical: mode imputation
+    for col in ['customer_state']:
+        if col in df_clean.columns:
+            n_missing = df_clean[col].isna().sum()
+            mode_val = df_clean[col].mode()[0]
+            df_clean[col].fillna(mode_val, inplace=True)
+            print(f"  {col}: {n_missing} missing → filled with mode ({mode_val})")
+    
+    # review_score: forward fill or mode
+    if 'review_score' in df_clean.columns:
+        n_missing = df_clean['review_score'].isna().sum()
+        df_clean['review_score'].fillna(df_clean['review_score'].mode()[0], inplace=True)
+        print(f"  review_score: {n_missing} missing → filled with mode")
+    
+    # 3.4 Handle outliers (capping/winsorizing)
+    print("\n--- Step 4: Handle Outliers (Capping) ---")
+    for col in ['price', 'freight_value', 'delivery_days']:
+        if col in df_clean.columns:
+            Q1 = df_clean[col].quantile(0.01)
+            Q99 = df_clean[col].quantile(0.99)
+            before_outliers = ((df_clean[col] < Q1) | (df_clean[col] > Q99)).sum()
+            df_clean[col] = df_clean[col].clip(lower=Q1, upper=Q99)
+            print(f"  {col}: Capped to [{Q1:.2f}, {Q99:.2f}], {before_outliers} values adjusted")
+    
+    # 3.5 Remove duplicates
+    print("\n--- Step 5: Remove Duplicates ---")
+    before = len(df_clean)
+    df_clean = df_clean.drop_duplicates(subset=['order_id'])
+    after = len(df_clean)
+    print(f"  Rows: {before} → {after} (removed {before - after} duplicates)")
+    
+    print(f"\n  Final shape: {original_shape} → {df_clean.shape}")
+    
+    return df_clean
+
+
+# ==============================================================================
+# PHẦN 4: Normalization Comparison
+# ==============================================================================
+
+def normalize_comparison(df: pd.DataFrame):
+    """So sánh các phương pháp normalization."""
+    
+    print("\n" + "=" * 70)
+    print("  NORMALIZATION COMPARISON")
+    print("=" * 70)
+    
+    col = 'price'
+    data = df[[col]].dropna().copy()
+    
+    # Apply different scalers
+    scalers = {
+        'Original': data[col].values,
+        'Min-Max [0,1]': MinMaxScaler().fit_transform(data).flatten(),
+        'Z-Score (Standard)': StandardScaler().fit_transform(data).flatten(),
+        'Robust (IQR)': RobustScaler().fit_transform(data).flatten(),
+    }
+    
+    fig, axes = plt.subplots(2, 2, figsize=(12, 10))
+    
+    for ax, (name, values) in zip(axes.flat, scalers.items()):
+        ax.hist(values, bins=50, alpha=0.7, color='#3498db', edgecolor='white')
+        ax.set_title(f'{name}\nmean={np.mean(values):.2f}, std={np.std(values):.2f}')
+        ax.set_ylabel('Frequency')
+        ax.axvline(np.mean(values), color='red', linestyle='--', label=f'Mean={np.mean(values):.2f}')
+        ax.axvline(np.median(values), color='green', linestyle='--', label=f'Median={np.median(values):.2f}')
+        ax.legend(fontsize=8)
+    
+    plt.suptitle('Normalization Methods Comparison (price column)', fontsize=14, fontweight='bold')
+    plt.tight_layout()
+    plt.savefig('normalization_comparison.png', dpi=150, bbox_inches='tight')
+    print(f"\n  [OK] Saved: normalization_comparison.png")
+    plt.close()
+    
+    # Print statistics
+    print(f"\n  Statistics comparison for '{col}':")
+    print(f"  {'Method':<25} {'Mean':>10} {'Std':>10} {'Min':>10} {'Max':>10}")
+    print(f"  {'-'*65}")
+    for name, values in scalers.items():
+        print(f"  {name:<25} {np.mean(values):>10.3f} {np.std(values):>10.3f} "
+              f"{np.min(values):>10.3f} {np.max(values):>10.3f}")
+
+
+# ==============================================================================
+# PHẦN 5: PCA (Dimensionality Reduction)
+# ==============================================================================
+
+def pca_analysis(df: pd.DataFrame):
+    """PCA analysis trên product dimensions."""
+    
+    print("\n" + "=" * 70)
+    print("  PCA - DIMENSIONALITY REDUCTION")
+    print("=" * 70)
+    
+    # Chọn features số
+    features = ['price', 'freight_value', 'product_weight_g',
+                'product_length_cm', 'product_height_cm', 'product_width_cm',
+                'delivery_days']
+    
+    available_features = [f for f in features if f in df.columns]
+    data = df[available_features].dropna()
+    
+    print(f"  Features: {available_features}")
+    print(f"  Samples: {len(data)}")
+    
+    # Standardize
+    scaler = StandardScaler()
+    data_scaled = scaler.fit_transform(data)
+    
+    # PCA
+    pca = PCA()
+    pca_result = pca.fit_transform(data_scaled)
+    
+    # Explained variance
+    print(f"\n  Explained Variance Ratio:")
+    cumulative = 0
+    for i, (var, cum_var) in enumerate(zip(
+        pca.explained_variance_ratio_,
+        np.cumsum(pca.explained_variance_ratio_)
+    )):
+        marker = " ← 95% reached" if cum_var >= 0.95 and cumulative < 0.95 else ""
+        print(f"    PC{i+1}: {var:.4f} ({var*100:.1f}%)  "
+              f"Cumulative: {cum_var:.4f} ({cum_var*100:.1f}%){marker}")
+        cumulative = cum_var
+    
+    # Determine number of components for 95% variance
+    n_components_95 = np.argmax(np.cumsum(pca.explained_variance_ratio_) >= 0.95) + 1
+    print(f"\n  → {n_components_95} components explain 95%+ variance "
+          f"(reduced from {len(available_features)} features)")
+    
+    # Component loadings
+    print(f"\n  Component Loadings (first 3 PCs):")
+    loadings = pd.DataFrame(
+        pca.components_[:3].T,
+        index=available_features,
+        columns=[f'PC{i+1}' for i in range(3)]
+    ).round(3)
+    print(loadings.to_string())
+    
+    # Visualization
+    fig, axes = plt.subplots(1, 2, figsize=(14, 5))
+    
+    # Scree plot
+    axes[0].bar(range(1, len(pca.explained_variance_ratio_) + 1),
+                pca.explained_variance_ratio_, alpha=0.7, color='#3498db', label='Individual')
+    axes[0].plot(range(1, len(pca.explained_variance_ratio_) + 1),
+                 np.cumsum(pca.explained_variance_ratio_), 'ro-', label='Cumulative')
+    axes[0].axhline(y=0.95, color='green', linestyle='--', label='95% threshold')
+    axes[0].set_xlabel('Principal Component')
+    axes[0].set_ylabel('Explained Variance Ratio')
+    axes[0].set_title('PCA Scree Plot')
+    axes[0].legend()
+    
+    # 2D scatter plot (PC1 vs PC2)
+    scatter = axes[1].scatter(pca_result[:, 0], pca_result[:, 1],
+                              c=data['price'].values, cmap='viridis',
+                              alpha=0.5, s=10)
+    axes[1].set_xlabel(f'PC1 ({pca.explained_variance_ratio_[0]*100:.1f}%)')
+    axes[1].set_ylabel(f'PC2 ({pca.explained_variance_ratio_[1]*100:.1f}%)')
+    axes[1].set_title('PCA: PC1 vs PC2 (colored by price)')
+    plt.colorbar(scatter, ax=axes[1], label='Price')
+    
+    plt.tight_layout()
+    plt.savefig('pca_analysis.png', dpi=150, bbox_inches='tight')
+    print(f"\n  [OK] Saved: pca_analysis.png")
+    plt.close()
+
+
+# ==============================================================================
+# PHẦN 6: Feature Engineering
+# ==============================================================================
+
+def feature_engineering(df: pd.DataFrame) -> pd.DataFrame:
+    """Feature Engineering cho Olist dataset."""
+    
+    print("\n" + "=" * 70)
+    print("  FEATURE ENGINEERING")
+    print("=" * 70)
+    
+    df_feat = df.copy()
+    new_features = []
+    
+    # 1. Price features
+    if 'price' in df_feat.columns and 'freight_value' in df_feat.columns:
+        df_feat['freight_ratio'] = (df_feat['freight_value'] / df_feat['price']).round(4)
+        df_feat['total_value'] = df_feat['price'] + df_feat['freight_value']
+        df_feat['is_free_shipping'] = (df_feat['freight_value'] == 0).astype(int)
+        new_features.extend(['freight_ratio', 'total_value', 'is_free_shipping'])
+    
+    # 2. Price binning
+    if 'price' in df_feat.columns:
+        df_feat['price_category'] = pd.qcut(
+            df_feat['price'], q=5,
+            labels=['very_low', 'low', 'medium', 'high', 'very_high'],
+            duplicates='drop'
+        )
+        new_features.append('price_category')
+    
+    # 3. Product size features
+    size_cols = ['product_length_cm', 'product_height_cm', 'product_width_cm']
+    if all(c in df_feat.columns for c in size_cols):
+        df_feat['product_volume'] = (
+            df_feat['product_length_cm'] *
+            df_feat['product_height_cm'] *
+            df_feat['product_width_cm']
+        )
+        new_features.append('product_volume')
+        
+        if 'product_weight_g' in df_feat.columns:
+            df_feat['product_density'] = (
+                df_feat['product_weight_g'] / df_feat['product_volume'].replace(0, np.nan)
+            ).round(4)
+            df_feat['is_heavy'] = (df_feat['product_weight_g'] > 5000).astype(int)
+            new_features.extend(['product_density', 'is_heavy'])
+    
+    # 4. Delivery features
+    if 'delivery_days' in df_feat.columns:
+        df_feat['delivery_category'] = pd.cut(
+            df_feat['delivery_days'],
+            bins=[0, 3, 7, 14, 30, float('inf')],
+            labels=['express', 'fast', 'normal', 'slow', 'very_slow']
+        )
+        df_feat['is_late'] = (df_feat['delivery_days'] > 14).astype(int)
+        new_features.extend(['delivery_category', 'is_late'])
+    
+    # 5. Review features
+    if 'review_score' in df_feat.columns:
+        df_feat['is_positive'] = (df_feat['review_score'] >= 4).astype(int)
+        df_feat['is_negative'] = (df_feat['review_score'] <= 2).astype(int)
+        new_features.extend(['is_positive', 'is_negative'])
+    
+    # 6. State-based features
+    if 'customer_state' in df_feat.columns:
+        state_region = {
+            'SP': 'Southeast', 'RJ': 'Southeast', 'MG': 'Southeast', 'ES': 'Southeast',
+            'PR': 'South', 'SC': 'South', 'RS': 'South',
+            'BA': 'Northeast', 'PE': 'Northeast', 'CE': 'Northeast',
+            'DF': 'Central-West', 'GO': 'Central-West', 'MT': 'Central-West',
+            'AM': 'North', 'PA': 'North',
+        }
+        df_feat['region'] = df_feat['customer_state'].map(state_region).fillna('Other')
+        new_features.append('region')
+    
+    print(f"  New features created: {len(new_features)}")
+    for feat in new_features:
+        dtype = df_feat[feat].dtype
+        nunique = df_feat[feat].nunique()
+        sample = df_feat[feat].head(3).tolist()
+        print(f"    {feat}: dtype={dtype}, unique={nunique}, sample={sample}")
+    
+    print(f"\n  Shape: {df.shape} → {df_feat.shape} (+{len(new_features)} features)")
+    
+    return df_feat
+
+
+# ==============================================================================
+# MAIN
+# ==============================================================================
+
+if __name__ == '__main__':
+    print("=" * 70)
+    print("  LAB 4: DATA PREPROCESSING")
+    print("  BIM5021 - Nha kho du lieu va Tich hop")
+    print("=" * 70)
+    
+    # 1. Create sample data
+    df = create_sample_olist()
+    
+    # 2. Assess quality
+    assess_data_quality(df)
+    
+    # 3. Clean data
+    df_clean = clean_data(df)
+    
+    # 4. Normalize comparison
+    normalize_comparison(df_clean)
+    
+    # 5. PCA
+    pca_analysis(df_clean)
+    
+    # 6. Feature Engineering
+    df_features = feature_engineering(df_clean)
+    
+    print("\n" + "=" * 70)
+    print("  HOAN THANH LAB 4!")
+    print("  Files: normalization_comparison.png, pca_analysis.png")
+    print("=" * 70)