diff --git a/.github/.gitattributes b/.github/.gitattributes
new file mode 100644
index 0000000..dfe0770
--- /dev/null
+++ b/.github/.gitattributes
@@ -0,0 +1,2 @@
+# Auto detect text files and perform LF normalization
+* text=auto
diff --git a/homework/m1_numpy.py b/homework/m1_numpy.py
index 5ff41ab..91e59a2 100644
--- a/homework/m1_numpy.py
+++ b/homework/m1_numpy.py
@@ -15,23 +15,25 @@
 # ============================================================
 # 🟢 送分題（每題 10 分，共 30 分）
 # ============================================================
-
 def green_mean():
     """建立 [10, 20, 30, 40, 50]，回傳所有元素的平均值 (float)"""
     # TODO: 你的程式碼
-    pass
+    arr = np.array([10, 20, 30, 40, 50])
+    return float(arr.mean())
 
 
 def green_double():
     """建立 [10, 20, 30, 40, 50]，回傳所有元素乘以 2 的 ndarray"""
     # TODO: 你的程式碼
-    pass
+    arr = np.array([10, 20, 30, 40, 50])
+    return arr * 2
 
 
 def green_filter():
     """建立 [10, 20, 30, 40, 50]，回傳大於 25 的元素 (ndarray)"""
     # TODO: 你的程式碼
-    pass
+    arr = np.array([10, 20, 30, 40, 50])
+    return arr[arr > 25]
 
 
 # ============================================================
@@ -42,7 +44,7 @@ def green_filter():
 def yellow_expensive_count(prices):
     """回傳單價 > 1000 的商品數量 (int)"""
     # TODO: 你的程式碼
-    pass
+    return int(np.sum(prices > 1000))
 
 
 def yellow_top3_stock_indices(stocks):
@@ -51,7 +53,7 @@ def yellow_top3_stock_indices(stocks):
     提示：np.argsort
     """
     # TODO: 你的程式碼
-    pass
+    return np.argsort(stocks)[::-1][:3]
 
 
 def yellow_restock_cost(prices, stocks):
@@ -60,7 +62,8 @@ def yellow_restock_cost(prices, stocks):
     提示：布林遮罩 + .sum()
     """
     # TODO: 你的程式碼
-    pass
+    mask = prices < 500
+    return float(np.sum(prices[mask] * 50))
 
 
 # ============================================================
@@ -77,4 +80,10 @@ def red_double11_prices(prices, stocks):
     提示：np.where 可以巢狀使用
     """
     # TODO: 你的程式碼
-    pass
+    return np.where(
+        stocks >= 100, prices * 0.7,
+        np.where(
+            stocks >= 20, prices * 0.9,
+            prices
+        )
+    )
diff --git a/homework/m2_pandas_cleaning.py b/homework/m2_pandas_cleaning.py
index fa36bff..a4a2a2b 100644
--- a/homework/m2_pandas_cleaning.py
+++ b/homework/m2_pandas_cleaning.py
@@ -20,16 +20,16 @@ def green_read_csv():
     提示：pd.read_csv()
     """
     # TODO: 你的程式碼
-    pass
+    return pd.read_csv('datasets/ecommerce/orders_raw.csv')
 
 
 def green_shape(df):
     """
     回傳 DataFrame 的 (列數, 欄數) tuple
     提示：df.shape
-    """
+        """
     # TODO: 你的程式碼
-    pass
+    return df.shape
 
 
 def green_dtypes(df):
@@ -38,7 +38,7 @@ def green_dtypes(df):
     提示：df.dtypes
     """
     # TODO: 你的程式碼
-    pass
+    return df.dtypes
 
 
 # ============================================================
@@ -52,7 +52,9 @@ def yellow_clean_columns(df):
     提示：df.columns.str.strip().str.lower()
     """
     # TODO: 你的程式碼
-    pass
+    result = df.copy()
+    result.columns = result.columns.str.strip().str.lower()
+    return result
 
 
 def yellow_clean_amount(df):
@@ -63,7 +65,14 @@ def yellow_clean_amount(df):
     提示：.str.replace() + .astype(float)
     """
     # TODO: 你的程式碼
-    pass
+    result = df.copy()
+    result['amount'] = (
+        result['amount']
+        .str.replace('$', '', regex=False)
+        .str.replace(',', '', regex=False)
+        .astype(float)
+    )
+    return result
 
 
 def yellow_drop_duplicates(df):
@@ -72,7 +81,7 @@ def yellow_drop_duplicates(df):
     提示：df.drop_duplicates()
     """
     # TODO: 你的程式碼
-    pass
+    return df.drop_duplicates()
 
 
 # ============================================================
@@ -93,4 +102,21 @@ def red_clean_orders(path):
     提示：pd.to_datetime(errors='coerce')
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv(path)
+    
+    df.columns = df.columns.str.strip().str.lower()
+    
+    df['amount'] = (
+        df['amount']
+        .str.replace('$', '', regex=False)
+        .str.replace(',', '', regex=False)
+        .astype(float)
+    )
+    
+    df['order_date'] = pd.to_datetime(df['order_date'], errors='coerce')
+    
+    df = df.dropna(subset=['amount', 'order_date'])
+    
+    df = df.drop_duplicates()
+    
+    return df
diff --git a/homework/m3_pandas_advanced.py b/homework/m3_pandas_advanced.py
index 68410e8..81233ba 100644
--- a/homework/m3_pandas_advanced.py
+++ b/homework/m3_pandas_advanced.py
@@ -24,19 +24,25 @@ def green_load_and_merge():
     提示：pd.merge(how='left')
     """
     # TODO: 你的程式碼
-    pass
+    orders = pd.read_csv('datasets/ecommerce/orders_clean.csv')
+    customers = pd.read_csv('datasets/ecommerce/customers.csv')
+    products = pd.read_csv('datasets/ecommerce/products.csv')
+        
+    df = pd.merge(orders, customers, on='customer_id', how='left')
+    df = pd.merge(df, products, on='product_id', how='left')
+    return df
 
 
 def green_row_count(df):
     """回傳 DataFrame 的列數 (int)"""
     # TODO: 你的程式碼
-    pass
+    return len(df)
 
 
 def green_column_list(df):
     """回傳 DataFrame 的所有欄位名稱 (list)"""
     # TODO: 你的程式碼
-    pass
+    return df.columns.tolist()
 
 
 # ============================================================
@@ -50,7 +56,7 @@ def yellow_top_category(df):
     提示：groupby('category')['amount'].sum()
     """
     # TODO: 你的程式碼
-    pass
+    return df.groupby('category')['amount'].sum().idxmax()
 
 
 def yellow_gold_vip_stats(df):
@@ -60,7 +66,8 @@ def yellow_gold_vip_stats(df):
     提示：df[df['vip_level'] == 'Gold']
     """
     # TODO: 你的程式碼
-    pass
+    gold = df[df['vip_level'] == 'Gold']
+    return (int(len(gold)), float(gold['amount'].sum()))
 
 
 def yellow_region_avg_amount(df):
@@ -70,7 +77,7 @@ def yellow_region_avg_amount(df):
     提示：groupby('region')['amount'].mean()
     """
     # TODO: 你的程式碼
-    pass
+    return df.groupby('region')['amount'].mean()
 
 
 # ============================================================
@@ -94,4 +101,22 @@ def red_rfm_top5(df):
     提示：groupby('customer_id').agg(...)
     """
     # TODO: 你的程式碼
-    pass
+    df['order_date'] = pd.to_datetime(df['order_date'])
+        
+    rfm = (
+        df.groupby('customer_id')
+        .agg(
+            R=('order_date', 'max'),
+            F=('order_date', 'count'),
+            M=('amount', 'sum'),
+            customer_name=('customer_name', 'first')
+        )
+        .reset_index()
+    )
+
+    return (
+        rfm[['customer_id', 'customer_name', 'R', 'F', 'M']]
+        .sort_values('M', ascending=False)
+        .head(5)
+        .reset_index(drop=True)
+    )
\ No newline at end of file
diff --git a/homework/m4_timeseries.py b/homework/m4_timeseries.py
index 047af6a..7fe52fc 100644
--- a/homework/m4_timeseries.py
+++ b/homework/m4_timeseries.py
@@ -27,7 +27,8 @@ def green_avg_by_month():
     提示：df['order_date'].dt.month
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    return df.groupby(df['order_date'].dt.month)['amount'].mean()
 
 
 def green_top3_dates():
@@ -37,7 +38,9 @@ def green_top3_dates():
     提示：value_counts().head(3)
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    return df['order_date'].dt.date.value_counts().head(3)
+
 
 
 def green_date_range():
@@ -46,7 +49,8 @@ def green_date_range():
     格式為 pandas Timestamp
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    return (df['order_date'].min(), df['order_date'].max())
 
 
 # ============================================================
@@ -60,7 +64,8 @@ def yellow_monthly_revenue():
     提示：set_index('order_date').resample('ME')['amount'].sum()
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    return df.set_index('order_date').resample('ME')['amount'].sum()
 
 
 def yellow_rolling_avg(monthly_revenue):
@@ -71,7 +76,7 @@ def yellow_rolling_avg(monthly_revenue):
     提示：.rolling(window=3).mean()
     """
     # TODO: 你的程式碼
-    pass
+    return monthly_revenue.rolling(window=3).mean()
 
 
 def yellow_category_median(df):
@@ -81,7 +86,11 @@ def yellow_category_median(df):
     提示：groupby + median + sort_values
     """
     # TODO: 你的程式碼
-    pass
+    return (
+        df.groupby('category')['amount']
+        .median()
+        .sort_values(ascending=False)
+    )
 
 
 # ============================================================
@@ -101,4 +110,19 @@ def red_monthly_report():
     提示：resample + agg + pct_change
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+        
+    monthly = (
+        df.set_index('order_date')
+        .resample('ME')
+        .agg(
+            order_count=('amount', 'count'),
+            revenue=('amount', 'sum'),
+            active_customers=('customer_id', 'nunique')
+        )
+    )
+        
+    monthly['avg_order_value'] = monthly['revenue'] / monthly['order_count']
+    monthly['revenue_growth'] = monthly['revenue'].pct_change() * 100
+        
+    return monthly
diff --git a/homework/m5_visualization.py b/homework/m5_visualization.py
index 7e7335d..af41f87 100644
--- a/homework/m5_visualization.py
+++ b/homework/m5_visualization.py
@@ -29,7 +29,15 @@ def green_bar_category():
     提示：sns.countplot 或 value_counts().plot.bar()
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    fig, ax = plt.subplots(figsize=(8, 5))
+    sns.countplot(data=df, x='category',
+                  order=df['category'].value_counts().index, ax=ax)
+    ax.set_title('Orders by Category')
+    ax.set_xlabel('Category')
+    ax.set_ylabel('Order Count')
+    plt.tight_layout()
+    return fig
 
 
 def green_hist_amount():
@@ -39,7 +47,14 @@ def green_hist_amount():
     提示：sns.histplot(bins=20) 或 plt.hist()
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    fig, ax = plt.subplots(figsize=(8, 5))
+    sns.histplot(data=df, x='amount', bins=20, ax=ax)
+    ax.set_title('Order Amount Distribution')
+    ax.set_xlabel('Amount')
+    ax.set_ylabel('Frequency')
+    plt.tight_layout()
+    return fig
 
 
 def green_set_labels():
@@ -51,7 +66,14 @@ def green_set_labels():
     回傳 matplotlib Figure 物件
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    fig, ax = plt.subplots(figsize=(8, 5))
+    df['category'].value_counts().plot.bar(ax=ax)
+    ax.set_title('Orders by Category')
+    ax.set_xlabel('Category')
+    ax.set_ylabel('Order Count')
+    plt.tight_layout()
+    return fig
 
 
 # ============================================================
@@ -68,7 +90,22 @@ def yellow_line_region_trend():
     提示：分別 groupby 再 plot，或用 sns.lineplot(hue='region')
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    df['month'] = df['order_date'].dt.to_period('M')
+    filtered = df[df['region'].isin(['North', 'South'])]
+    monthly = (
+        filtered.groupby(['month', 'region'])['amount']
+        .sum()
+        .reset_index()
+    )
+    monthly['month'] = monthly['month'].dt.to_timestamp()
+    fig, ax = plt.subplots(figsize=(10, 5))
+    sns.lineplot(data=monthly, x='month', y='amount', hue='region', ax=ax)
+    ax.set_title('Monthly Revenue: North vs South')
+    ax.set_xlabel('Month')
+    ax.set_ylabel('Revenue')
+    plt.tight_layout()
+    return fig
 
 
 def yellow_box_vip():
@@ -78,7 +115,14 @@ def yellow_box_vip():
     提示：sns.boxplot(x='vip_level', y='amount', data=df)
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    fig, ax = plt.subplots(figsize=(8, 5))
+    sns.boxplot(data=df, x='vip_level', y='amount', ax=ax)
+    ax.set_title('Order Amount by VIP Level')
+    ax.set_xlabel('VIP Level')
+    ax.set_ylabel('Amount')
+    plt.tight_layout()
+    return fig
 
 
 def yellow_scatter_price_amount():
@@ -88,7 +132,14 @@ def yellow_scatter_price_amount():
     提示：plt.scatter() 或 sns.scatterplot()
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    fig, ax = plt.subplots(figsize=(8, 5))
+    sns.scatterplot(data=df, x='unit_price', y='amount', alpha=0.5, ax=ax)
+    ax.set_title('Unit Price vs Order Amount')
+    ax.set_xlabel('Unit Price')
+    ax.set_ylabel('Amount')
+    plt.tight_layout()
+    return fig
 
 
 # ============================================================
@@ -107,4 +158,45 @@ def red_category_dashboard(category="Electronics"):
     提示：fig, axes = plt.subplots(2, 2, figsize=(14, 10))
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    cat_df = df[df['category'] == category].copy()
+    cat_df['month'] = cat_df['order_date'].dt.to_period('M')
+
+    fig, axes = plt.subplots(2, 2, figsize=(14, 10))
+    fig.suptitle(f'{category} Dashboard', fontsize=16, fontweight='bold')
+
+    # 左上：月營收趨勢
+    monthly_rev = cat_df.groupby('month')['amount'].sum()
+    monthly_rev.index = monthly_rev.index.to_timestamp()
+    axes[0, 0].plot(monthly_rev.index, monthly_rev.values, marker='o')
+    axes[0, 0].set_title('Monthly Revenue Trend')
+    axes[0, 0].set_xlabel('Month')
+    axes[0, 0].set_ylabel('Revenue')
+
+    # 右上：各地區營收
+    region_rev = cat_df.groupby('region')['amount'].sum().sort_values(ascending=False)
+    axes[0, 1].bar(region_rev.index, region_rev.values)
+    axes[0, 1].set_title('Revenue by Region')
+    axes[0, 1].set_xlabel('Region')
+    axes[0, 1].set_ylabel('Revenue')
+
+    # 左下：Top 5 商品水平長條圖
+    top5 = (
+        cat_df.groupby('product_name')['amount']
+        .sum()
+        .sort_values(ascending=False)
+        .head(5)
+    )
+    axes[1, 0].barh(top5.index[::-1], top5.values[::-1])
+    axes[1, 0].set_title('Top 5 Products by Revenue')
+    axes[1, 0].set_xlabel('Revenue')
+    axes[1, 0].set_ylabel('Product')
+
+    # 右下：訂單金額分佈
+    axes[1, 1].hist(cat_df['amount'], bins=20, edgecolor='white')
+    axes[1, 1].set_title('Order Amount Distribution')
+    axes[1, 1].set_xlabel('Amount')
+    axes[1, 1].set_ylabel('Frequency')
+
+    plt.tight_layout()
+    return fig
\ No newline at end of file
diff --git a/homework/m6_plotly_capstone.py b/homework/m6_plotly_capstone.py
index 0e2c32a..f2b25e8 100644
--- a/homework/m6_plotly_capstone.py
+++ b/homework/m6_plotly_capstone.py
@@ -13,6 +13,9 @@
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 
+def _load_enriched():
+    return pd.read_csv("datasets/ecommerce/orders_enriched.csv",
+                       parse_dates=["order_date"])
 
 # ============================================================
 # 🟢 送分題（每題 10 分，共 30 分）
@@ -26,8 +29,20 @@ def green_plotly_bar():
     提示：px.bar()
     """
     # TODO: 你的程式碼
-    pass
-
+    df = _load_enriched()
+    category_rev = (
+        df.groupby('category')['amount']
+        .sum()
+        .reset_index()
+        .sort_values('amount', ascending=False)
+    )
+    fig = px.bar(
+        category_rev,
+        x='category', y='amount',
+        title='Total Revenue by Category',
+        labels={'amount': 'Revenue', 'category': 'Category'}
+    )
+    return fig
 
 def green_plotly_line():
     """
@@ -37,7 +52,17 @@ def green_plotly_line():
     提示：先 groupby 月份算總營收，再 px.line()
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_enriched()
+    df['month'] = df['order_date'].dt.to_period('M').dt.to_timestamp()
+    monthly = df.groupby('month')['amount'].sum().reset_index()
+    fig = px.line(
+        monthly,
+        x='month', y='amount',
+        title='Monthly Revenue Trend',
+        labels={'month': 'Month', 'amount': 'Revenue'},
+        markers=True
+    )
+    return fig
 
 
 def green_plotly_pie():
@@ -48,7 +73,15 @@ def green_plotly_pie():
     提示：px.pie()
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_enriched()
+    vip_counts = df['vip_level'].value_counts().reset_index()
+    vip_counts.columns = ['vip_level', 'count']
+    fig = px.pie(
+        vip_counts,
+        names='vip_level', values='count',
+        title='Order Share by VIP Level'
+    )
+    return fig
 
 
 # ============================================================
@@ -63,7 +96,27 @@ def yellow_clean_and_merge(raw_path, customers_path, products_path):
     回傳：合併後的 DataFrame
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv(raw_path)
+    customers = pd.read_csv(customers_path)
+    products = pd.read_csv(products_path)
+
+    df.columns = df.columns.str.strip().str.lower()
+
+    df['amount'] = (
+        df['amount']
+        .str.replace('$', '', regex=False)
+        .str.replace(',', '', regex=False)
+        .astype(float)
+    )
+
+    df['order_date'] = pd.to_datetime(df['order_date'], errors='coerce')
+    df = df.dropna(subset=['amount', 'order_date'])
+    df = df.drop_duplicates()
+
+    df = pd.merge(df, customers, on='customer_id', how='left')
+    df = pd.merge(df, products, on='product_id', how='left')
+
+    return df
 
 
 def yellow_kpi_summary(df):
@@ -77,7 +130,12 @@ def yellow_kpi_summary(df):
     }
     """
     # TODO: 你的程式碼
-    pass
+    return {
+        "total_revenue":    float(df['amount'].sum()),
+        "order_count":      int(len(df)),
+        "active_customers": int(df['customer_id'].nunique()),
+        "avg_order_value":  float(df['amount'].mean()),
+    }
 
 
 def yellow_plotly_scatter(df):
@@ -91,7 +149,16 @@ def yellow_plotly_scatter(df):
     提示：px.scatter(hover_data=['product_name'])
     """
     # TODO: 你的程式碼
-    pass
+    fig = px.scatter(
+        df,
+        x='unit_price', y='amount',
+        color='category',
+        hover_data=['product_name'],
+        title='Unit Price vs Order Amount',
+        labels={'unit_price': 'Unit Price', 'amount': 'Order Amount'},
+        opacity=0.6
+    )
+    return fig
 
 
 # ============================================================
@@ -105,14 +172,91 @@ def red_dashboard():
     流程：
     1. 清理 orders_raw.csv + 合併三張表
     2. 建立 2×2 subplot dashboard（用 plotly make_subplots）：
-       - 左上：月營收趨勢 (line)
-       - 右上：Top 10 商品營收 (bar)
-       - 左下：各地區營收 (bar)
-       - 右下：類別營收佔比 (pie/donut)
+    - 左上：月營收趨勢 (line)
+    - 右上：Top 10 商品營收 (bar)
+    - 左下：各地區營收 (bar)
+    - 右下：類別營收佔比 (pie/donut)
     3. 設定整體標題
 
     回傳 plotly Figure 物件
     提示：from plotly.subplots import make_subplots
     """
     # TODO: 你的程式碼
-    pass
+    df = yellow_clean_and_merge(
+        "datasets/ecommerce/orders_raw.csv",
+        "datasets/ecommerce/customers.csv",
+        "datasets/ecommerce/products.csv"
+    )
+
+    # 預計算各子圖資料
+    df['month'] = df['order_date'].dt.to_period('M').dt.to_timestamp()
+    monthly = df.groupby('month')['amount'].sum().reset_index()
+
+    top10 = (
+        df.groupby('product_name')['amount']
+        .sum()
+        .sort_values(ascending=False)
+        .head(10)
+        .reset_index()
+    )
+
+    region_rev = (
+        df.groupby('region')['amount']
+        .sum()
+        .sort_values(ascending=False)
+        .reset_index()
+    )
+
+    cat_rev = df.groupby('category')['amount'].sum().reset_index()
+
+    # 建立 2×2 subplots
+    fig = make_subplots(
+        rows=2, cols=2,
+        subplot_titles=(
+            'Monthly Revenue Trend',
+            'Top 10 Products by Revenue',
+            'Revenue by Region',
+            'Revenue Share by Category'
+        ),
+        specs=[
+            [{"type": "xy"},     {"type": "xy"}],
+            [{"type": "xy"},     {"type": "domain"}]
+        ]
+    )
+
+    # 左上：折線圖
+    fig.add_trace(
+        go.Scatter(x=monthly['month'], y=monthly['amount'],
+                   mode='lines+markers', name='Monthly Revenue'),
+        row=1, col=1
+    )
+
+    # 右上：水平長條圖 Top 10
+    fig.add_trace(
+        go.Bar(x=top10['amount'], y=top10['product_name'],
+               orientation='h', name='Product Revenue'),
+        row=1, col=2
+    )
+
+    # 左下：地區營收長條圖
+    fig.add_trace(
+        go.Bar(x=region_rev['region'], y=region_rev['amount'],
+               name='Region Revenue'),
+        row=2, col=1
+    )
+
+    # 右下：類別佔比甜甜圈圖
+    fig.add_trace(
+        go.Pie(labels=cat_rev['category'], values=cat_rev['amount'],
+               hole=0.4, name='Category Share'),
+        row=2, col=2
+    )
+
+    fig.update_layout(
+        title_text='E-Commerce Interactive Dashboard',
+        title_font_size=20,
+        height=800,
+        showlegend=False
+    )
+
+    return fig