diff --git a/homework/m1_numpy.py b/homework/m1_numpy.py
index 5ff41ab..b7b603b 100644
--- a/homework/m1_numpy.py
+++ b/homework/m1_numpy.py
@@ -19,19 +19,22 @@
 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(np.mean(arr))
 
 
 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 +45,8 @@ 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
+    mask = prices > 1000
+    return mask.sum()
 
 
 def yellow_top3_stock_indices(stocks):
@@ -51,7 +55,9 @@ def yellow_top3_stock_indices(stocks):
     提示：np.argsort
     """
     # TODO: 你的程式碼
-    pass
+    stocks_3 = np.argsort(stocks)
+    return (stocks_3[::-1][:3])
+
 
 
 def yellow_restock_cost(prices, stocks):
@@ -60,7 +66,8 @@ def yellow_restock_cost(prices, stocks):
     提示：布林遮罩 + .sum()
     """
     # TODO: 你的程式碼
-    pass
+    prices_500 = prices[prices < 500]*50
+    return (prices_500.sum())
 
 
 # ============================================================
@@ -77,4 +84,16 @@ def red_double11_prices(prices, stocks):
     提示：np.where 可以巢狀使用
     """
     # TODO: 你的程式碼
-    pass
+    prices = np.array(prices)
+    stocks = np.array(stocks)
+    
+    new_prices = np.where(
+        stocks >= 100,                       
+        prices * 0.7,                        
+        np.where(
+            stocks >= 20,                    
+            prices * 0.9,                    
+            prices                           
+    )
+)
+    return new_prices
\ No newline at end of file
diff --git a/homework/m2_pandas_cleaning.py b/homework/m2_pandas_cleaning.py
index fa36bff..8e8a5df 100644
--- a/homework/m2_pandas_cleaning.py
+++ b/homework/m2_pandas_cleaning.py
@@ -20,7 +20,7 @@ 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):
@@ -29,7 +29,7 @@ def green_shape(df):
     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
+    df_new = df.copy()
+    df_new.columns = df_new.columns.str.strip().str.lower()
+    return df_new
 
 
 def yellow_clean_amount(df):
@@ -63,7 +65,15 @@ def yellow_clean_amount(df):
     提示：.str.replace() + .astype(float)
     """
     # TODO: 你的程式碼
-    pass
+    df_amount = df.copy()
+    df_amount['amount'] = (
+        df_amount['amount']
+        .astype(str)
+        .str.replace('$', '', regex=False)
+        .str.replace(',', '', regex=False)
+        .astype(float)
+    )
+    return df_amount
 
 
 def yellow_drop_duplicates(df):
@@ -72,7 +82,7 @@ def yellow_drop_duplicates(df):
     提示：df.drop_duplicates()
     """
     # TODO: 你的程式碼
-    pass
+    return df.drop_duplicates()
 
 
 # ============================================================
@@ -93,4 +103,10 @@ 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'].astype(str).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=['order_date','amount']) # 只要這兩個欄位中任一個有缺失值，該列就會被剔除。
+    df = df.drop_duplicates()
+    return df
\ No newline at end of file
diff --git a/homework/m3_pandas_advanced.py b/homework/m3_pandas_advanced.py
index 68410e8..c0d829f 100644
--- a/homework/m3_pandas_advanced.py
+++ b/homework/m3_pandas_advanced.py
@@ -24,19 +24,27 @@ 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=(
+        orders
+        .merge(customers,on='customer_id',how='left')
+        .merge(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 list(df)
 
 
 # ============================================================
@@ -50,7 +58,8 @@ def yellow_top_category(df):
     提示：groupby('category')['amount'].sum()
     """
     # TODO: 你的程式碼
-    pass
+    category_rev = df.groupby('category')['amount'].sum().sort_values(ascending=False)
+    return category_rev.idxmax()
 
 
 def yellow_gold_vip_stats(df):
@@ -60,7 +69,11 @@ def yellow_gold_vip_stats(df):
     提示：df[df['vip_level'] == 'Gold']
     """
     # TODO: 你的程式碼
-    pass
+    gold_df = df[df['vip_level'] == 'Gold']
+    order_count = int(len(gold_df))
+    total_amount = float(gold_df['amount'].sum())
+
+    return (order_count, total_amount)
 
 
 def yellow_region_avg_amount(df):
@@ -70,7 +83,13 @@ def yellow_region_avg_amount(df):
     提示：groupby('region')['amount'].mean()
     """
     # TODO: 你的程式碼
-    pass
+    region_mean = (
+    df.groupby('region')['amount']
+      .mean()
+      .round(2)
+      .sort_values(ascending=False)
+    )
+    return region_mean
 
 
 # ============================================================
@@ -94,4 +113,11 @@ def red_rfm_top5(df):
     提示：groupby('customer_id').agg(...)
     """
     # TODO: 你的程式碼
-    pass
+    rfm = df.groupby('customer_id').agg(
+        R=('order_date', 'max'),
+        F=('order_id', 'count'),
+        M=('amount', 'sum'),
+        customer_name=("customer_name", "first")
+    ).reset_index()
+    result = rfm.sort_values('M',ascending=False).head()
+    return result[["customer_id", "customer_name", "R", "F", "M"]]
\ No newline at end of file
diff --git a/homework/m4_timeseries.py b/homework/m4_timeseries.py
index 047af6a..d037867 100644
--- a/homework/m4_timeseries.py
+++ b/homework/m4_timeseries.py
@@ -27,7 +27,9 @@ def green_avg_by_month():
     提示：df['order_date'].dt.month
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()  # 必須先讀取資料，否則會報錯
+    ts = df.groupby(df['order_date'].dt.month)
+    return ts['amount'].mean()
 
 
 def green_top3_dates():
@@ -37,7 +39,8 @@ def green_top3_dates():
     提示：value_counts().head(3)
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    return df['order_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 +76,8 @@ def yellow_rolling_avg(monthly_revenue):
     提示：.rolling(window=3).mean()
     """
     # TODO: 你的程式碼
-    pass
+    monthly_rev = yellow_monthly_revenue()
+    return monthly_rev.rolling(window=3).mean()
 
 
 def yellow_category_median(df):
@@ -81,7 +87,13 @@ def yellow_category_median(df):
     提示：groupby + median + sort_values
     """
     # TODO: 你的程式碼
-    pass
+    result = (
+        df.groupby('category')['amount']
+          .median()
+          .sort_values(ascending=False)
+    )
+    
+    return result
 
 
 # ============================================================
@@ -101,4 +113,13 @@ def red_monthly_report():
     提示：resample + agg + pct_change
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    report = df.set_index('order_date').resample('ME').agg({
+        'order_id':'count',
+        'amount':'sum',
+        'customer_id':'nunique'
+    })
+    report.columns=['order_count','revenue','active_customers']
+    report['avg_order_value'] = report['revenue'] / report['order_count']
+    report['revenue_growth'] = report['revenue'].pct_change() * 100
+    return report
\ No newline at end of file
diff --git a/homework/m5_visualization.py b/homework/m5_visualization.py
index 7e7335d..80ca92f 100644
--- a/homework/m5_visualization.py
+++ b/homework/m5_visualization.py
@@ -29,7 +29,12 @@ 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, 4))
+    # 計算訂單數最準確的方式是 countplot
+    sns.countplot(data=df, x='category', palette='viridis', ax=ax)
+    ax.set_title("Order Count by Category")
+    return fig
 
 
 def green_hist_amount():
@@ -39,7 +44,15 @@ def green_hist_amount():
     提示：sns.histplot(bins=20) 或 plt.hist()
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    # 建立畫布與座標軸
+    fig, ax = plt.subplots(figsize=(10, 4))
+    
+    # 畫出直方圖，指定 bins=20
+    sns.histplot(data=df, x='amount', bins=20, ax=ax, color='skyblue', kde=True)
+    
+    ax.set_title("Distribution of Order Amounts")
+    return fig
 
 
 def green_set_labels():
@@ -51,7 +64,13 @@ def green_set_labels():
     回傳 matplotlib Figure 物件
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    plt.figure(figsize=(8, 4))
+    fig = sns.barplot(data=df, x='region', y='amount', palette='viridis', hue='region', legend=False)
+    plt.title('This is title', fontweight='bold')
+    plt.xlabel('This is xlabel')
+    plt.ylabel('This is ylabel')
+    return fig
 
 
 # ============================================================
@@ -68,7 +87,16 @@ 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').astype(str)
+    trend = df.groupby(['month', 'region'])['amount'].sum().reset_index()
+    
+    trend = trend[trend['region'].isin(['North', 'South'])]
+    
+    fig, ax = plt.subplots(figsize=(10, 5))
+    sns.lineplot(data=trend, x='month', y='amount', hue='region', marker='o', ax=ax)
+    plt.xticks(rotation=45)
+    return fig
 
 
 def yellow_box_vip():
@@ -78,7 +106,10 @@ 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', palette='Set2', ax=ax)
+    return fig
 
 
 def yellow_scatter_price_amount():
@@ -88,7 +119,13 @@ 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, 6))
+    
+    sns.scatterplot(data=df, x='unit_price', y='amount', alpha=0.6, ax=ax)
+    
+    ax.set_title("Unit Price vs. Total Amount")
+    return fig
 
 
 # ============================================================
@@ -107,4 +144,30 @@ 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()
+    
+    fig, axes = plt.subplots(2, 2, figsize=(14, 10))
+    fig.suptitle(f"Dashboard: {category}", fontsize=20, fontweight='bold')
+
+    # 1. 左上：月營收趨勢
+    cat_df['month'] = cat_df['order_date'].dt.to_period('M').astype(str)
+    mon_rev = cat_df.groupby('month')['amount'].sum()
+    mon_rev.plot(kind='line', marker='o', ax=axes[0, 0], color='blue')
+    axes[0, 0].set_title("Monthly Revenue Trend")
+
+    # 2. 右上：各地區營收
+    sns.barplot(data=cat_df, x='region', y='amount', estimator=sum, ax=axes[0, 1])
+    axes[0, 1].set_title("Revenue by Region")
+
+    # 3. 左下：Top 5 商品
+    top5 = cat_df.groupby('product_name')['amount'].sum().nlargest(5)
+    top5.plot(kind='barh', ax=axes[1, 0], color='green')
+    axes[1, 0].set_title("Top 5 Products")
+
+    # 4. 右下：金額分佈
+    sns.histplot(cat_df['amount'], bins=15, kde=True, ax=axes[1, 1], color='orange')
+    axes[1, 1].set_title("Order Amount Distribution")
+
+    plt.tight_layout(rect=[0, 0.03, 1, 0.95])
+    return fig
\ No newline at end of file
diff --git a/homework/m6_plotly_capstone.py b/homework/m6_plotly_capstone.py
index 0e2c32a..9222dc3 100644
--- a/homework/m6_plotly_capstone.py
+++ b/homework/m6_plotly_capstone.py
@@ -26,7 +26,10 @@ def green_plotly_bar():
     提示：px.bar()
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv")
+    rev_by_cat = df.groupby('category')['amount'].sum().reset_index()
+    fig = px.bar(rev_by_cat, x='category', y='amount', title="Revenue by Category")
+    return fig
 
 
 def green_plotly_line():
@@ -37,7 +40,11 @@ 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 = pd.read_csv("datasets/ecommerce/orders_enriched.csv", parse_dates=['order_date'])
+    df['month'] = df['order_date'].dt.to_period('M').astype(str)
+    monthly_rev = df.groupby('month')['amount'].sum().reset_index()
+    fig = px.line(monthly_rev, x='month', y='amount', title="Monthly Revenue Trend", markers=True)
+    return fig
 
 
 def green_plotly_pie():
@@ -48,7 +55,9 @@ def green_plotly_pie():
     提示：px.pie()
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv")
+    fig = px.pie(df, names='vip_level', values='amount', title="Revenue Share by VIP Level")
+    return fig
 
 
 # ============================================================
@@ -62,8 +71,43 @@ def yellow_clean_and_merge(raw_path, customers_path, products_path):
     2. 合併 customers.csv 和 products.csv
     回傳：合併後的 DataFrame
     """
-    # TODO: 你的程式碼
-    pass
+    # 讀取原始訂單資料
+    orders = pd.read_csv(raw_path)
+    
+    # 標準化欄位名稱（去除空格，轉小寫）
+    orders.columns = orders.columns.str.strip().str.lower()
+    
+    # 重命名欄位
+    orders = orders.rename(columns={
+        'order_id ': 'order_id',
+        'product_id': 'product_id',
+        ' qty': 'qty'
+    })
+    
+    # 清理金額：移除 $ 符號和逗號，轉為 float
+    orders['amount'] = orders['amount'].astype(str).str.replace('$', '').str.replace(',', '')
+    orders['amount'] = pd.to_numeric(orders['amount'], errors='coerce')
+    
+    # 解析日期
+    orders['order_date'] = pd.to_datetime(orders['order_date'], errors='coerce')
+    
+    # 移除缺值
+    orders = orders.dropna()
+    
+    # 去重
+    orders = orders.drop_duplicates()
+    
+    # 讀取客戶資料和商品資料
+    customers = pd.read_csv(customers_path)
+    products = pd.read_csv(products_path)
+    
+    # 合併客戶資料
+    orders = orders.merge(customers, on='customer_id', how='left')
+    
+    # 合併商品資料
+    orders = orders.merge(products, on='product_id', how='left')
+    
+    return orders
 
 
 def yellow_kpi_summary(df):
@@ -76,8 +120,12 @@ def yellow_kpi_summary(df):
         "avg_order_value": float,     # 平均客單價
     }
     """
-    # TODO: 你的程式碼
-    pass
+    return {
+        "total_revenue": float(df['amount'].sum()),
+        "order_count": len(df),
+        "active_customers": df['customer_id'].nunique(),
+        "avg_order_value": float(df['amount'].mean()),
+    }
 
 
 def yellow_plotly_scatter(df):
@@ -90,8 +138,15 @@ def yellow_plotly_scatter(df):
     回傳 plotly Figure 物件
     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 by Category"
+    )
+    return fig
 
 
 # ============================================================
@@ -114,5 +169,94 @@ def red_dashboard():
     回傳 plotly Figure 物件
     提示：from plotly.subplots import make_subplots
     """
-    # TODO: 你的程式碼
-    pass
+    # 清理和合併資料
+    df = yellow_clean_and_merge(
+        "datasets/ecommerce/orders_raw.csv",
+        "datasets/ecommerce/customers.csv",
+        "datasets/ecommerce/products.csv"
+    )
+    
+    # 建立 2×2 subplot
+    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=[
+            [{"secondary_y": False}, {"secondary_y": False}],
+            [{"secondary_y": False}, {"type": "pie"}]
+        ]
+    )
+    
+    # 1. 左上：月營收趨勢 (line)
+    df_copy = df.copy()
+    df_copy['month'] = df_copy['order_date'].dt.to_period('M').astype(str)
+    monthly_rev = df_copy.groupby('month')['amount'].sum().reset_index()
+    fig.add_trace(
+        go.Scatter(
+            x=monthly_rev['month'],
+            y=monthly_rev['amount'],
+            mode='lines+markers',
+            name='Monthly Revenue',
+            line=dict(color='#1f77b4')
+        ),
+        row=1, col=1
+    )
+    
+    # 2. 右上：Top 10 商品營收 (bar)
+    top10_products = df.groupby('product_name')['amount'].sum().nlargest(10).reset_index()
+    fig.add_trace(
+        go.Bar(
+            y=top10_products['product_name'],
+            x=top10_products['amount'],
+            orientation='h',
+            name='Product Revenue',
+            marker=dict(color='#ff7f0e')
+        ),
+        row=1, col=2
+    )
+    
+    # 3. 左下：各地區營收 (bar)
+    region_rev = df.groupby('region')['amount'].sum().reset_index()
+    fig.add_trace(
+        go.Bar(
+            x=region_rev['region'],
+            y=region_rev['amount'],
+            name='Region Revenue',
+            marker=dict(color='#2ca02c')
+        ),
+        row=2, col=1
+    )
+    
+    # 4. 右下：類別營收佔比 (pie)
+    category_rev = df.groupby('category')['amount'].sum().reset_index()
+    fig.add_trace(
+        go.Pie(
+            labels=category_rev['category'],
+            values=category_rev['amount'],
+            name='Category Share'
+        ),
+        row=2, col=2
+    )
+    
+    # 更新軸標籤
+    fig.update_xaxes(title_text="Month", row=1, col=1)
+    fig.update_yaxes(title_text="Revenue ($)", row=1, col=1)
+    
+    fig.update_xaxes(title_text="Revenue ($)", row=1, col=2)
+    fig.update_yaxes(title_text="Product", row=1, col=2)
+    
+    fig.update_xaxes(title_text="Region", row=2, col=1)
+    fig.update_yaxes(title_text="Revenue ($)", row=2, col=1)
+    
+    # 設定整體標題
+    fig.update_layout(
+        title_text="E-Commerce Dashboard",
+        height=900,
+        showlegend=True
+    )
+    
+    return fig
\ No newline at end of file