diff --git a/homework/m1_numpy.py b/homework/m1_numpy.py
index 5ff41ab..79c966b 100644
--- a/homework/m1_numpy.py
+++ b/homework/m1_numpy.py
@@ -18,20 +18,20 @@
 
 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 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]
 
 
 # ============================================================
@@ -41,8 +41,7 @@ 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((prices > 1000).sum())
 
 
 def yellow_top3_stock_indices(stocks):
@@ -50,8 +49,7 @@ def yellow_top3_stock_indices(stocks):
     回傳庫存最多的前 3 個商品的索引位置 (ndarray, 由大到小排)
     提示：np.argsort
     """
-    # TODO: 你的程式碼
-    pass
+    return np.argsort(stocks)[-3:][::-1]
 
 
 def yellow_restock_cost(prices, stocks):
@@ -59,8 +57,7 @@ def yellow_restock_cost(prices, stocks):
     單價 < 500 的商品，每種各進貨 50 個，回傳總花費 (float/int)
     提示：布林遮罩 + .sum()
     """
-    # TODO: 你的程式碼
-    pass
+    return (prices[prices < 500] * 50).sum()
 
 
 # ============================================================
@@ -76,5 +73,8 @@ def red_double11_prices(prices, stocks):
     回傳每個商品的雙 11 售價 (ndarray)
     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)
+    )
\ No newline at end of file
diff --git a/homework/m2_pandas_cleaning.py b/homework/m2_pandas_cleaning.py
index fa36bff..dc1864c 100644
--- a/homework/m2_pandas_cleaning.py
+++ b/homework/m2_pandas_cleaning.py
@@ -19,8 +19,7 @@ def green_read_csv():
     讀取 orders_raw.csv，回傳原始 DataFrame（不做任何清理）
     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):
@@ -28,8 +27,7 @@ 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):
@@ -37,8 +35,7 @@ def green_dtypes(df):
     回傳 DataFrame 的欄位型別 (Series)
     提示：df.dtypes
     """
-    # TODO: 你的程式碼
-    pass
+    return df.dtypes
 
 
 # ============================================================
@@ -51,8 +48,9 @@ def yellow_clean_columns(df):
     回傳清理後的 DataFrame（不要修改原始 df）
     提示：df.columns.str.strip().str.lower()
     """
-    # TODO: 你的程式碼
-    pass
+    new_df = df.copy()
+    new_df.columns = new_df.columns.str.strip().str.lower()
+    return new_df
 
 
 def yellow_clean_amount(df):
@@ -62,8 +60,15 @@ def yellow_clean_amount(df):
     回傳清理後的 DataFrame（不要修改原始 df）
     提示：.str.replace() + .astype(float)
     """
-    # TODO: 你的程式碼
-    pass
+    new_df = df.copy()
+    new_df["amount"] = (
+        new_df["amount"]
+        .astype(str)
+        .str.replace("$", "", regex=False)
+        .str.replace(",", "", regex=False)
+        .astype(float)
+    )
+    return new_df
 
 
 def yellow_drop_duplicates(df):
@@ -71,8 +76,7 @@ def yellow_drop_duplicates(df):
     移除完全重複的列，回傳去重後的 DataFrame
     提示：df.drop_duplicates()
     """
-    # TODO: 你的程式碼
-    pass
+    return df.drop_duplicates().copy()
 
 
 # ============================================================
@@ -92,5 +96,27 @@ def red_clean_orders(path):
     回傳：清理後的 DataFrame
     提示：pd.to_datetime(errors='coerce')
     """
-    # TODO: 你的程式碼
-    pass
+    df = pd.read_csv(path)
+
+    # 欄位名稱清理
+    df.columns = df.columns.str.strip().str.lower()
+
+    # amount 清理
+    df["amount"] = (
+        df["amount"]
+        .astype(str)
+        .str.replace("$", "", regex=False)
+        .str.replace(",", "", regex=False)
+        .astype(float)
+    )
+
+    # 日期轉換
+    df["order_date"] = pd.to_datetime(df["order_date"], errors="coerce")
+
+    # 刪除關鍵欄位缺值
+    df = df.dropna(subset=["amount", "order_date"])
+
+    # 去重
+    df = df.drop_duplicates()
+
+    return df.copy()
\ No newline at end of file
diff --git a/homework/m3_pandas_advanced.py b/homework/m3_pandas_advanced.py
index 68410e8..046e8ea 100644
--- a/homework/m3_pandas_advanced.py
+++ b/homework/m3_pandas_advanced.py
@@ -23,20 +23,23 @@ def green_load_and_merge():
     - 再 LEFT JOIN products.csv ON product_id
     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 int(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.columns)
 
 
 # ============================================================
@@ -49,8 +52,8 @@ def yellow_top_category(df):
     回傳該類別名稱 (str)
     提示：groupby('category')['amount'].sum()
     """
-    # TODO: 你的程式碼
-    pass
+    revenue_by_cat = df.groupby("category")["amount"].sum()
+    return revenue_by_cat.idxmax()
 
 
 def yellow_gold_vip_stats(df):
@@ -59,8 +62,10 @@ def yellow_gold_vip_stats(df):
     回傳 tuple: (訂單數 int, 總金額 float)
     提示：df[df['vip_level'] == 'Gold']
     """
-    # TODO: 你的程式碼
-    pass
+    sub = df[df["vip_level"] == "Gold"]
+    order_count = int(len(sub))
+    total_amount = float(sub["amount"].sum())
+    return order_count, total_amount
 
 
 def yellow_region_avg_amount(df):
@@ -69,8 +74,7 @@ def yellow_region_avg_amount(df):
     回傳 Series（index=region, values=平均金額）
     提示：groupby('region')['amount'].mean()
     """
-    # TODO: 你的程式碼
-    pass
+    return df.groupby("region")["amount"].mean()
 
 
 # ============================================================
@@ -93,5 +97,30 @@ def red_rfm_top5(df):
 
     提示：groupby('customer_id').agg(...)
     """
-    # TODO: 你的程式碼
-    pass
+    data = df.copy()
+
+    # 確保日期型別可比較
+    data["order_date"] = pd.to_datetime(data["order_date"], errors="coerce")
+
+    agg = (
+        data.groupby("customer_id")
+        .agg(
+            R=("order_date", "max"),
+            F=("order_date", "count"),
+            M=("amount", "sum"),
+        )
+        .reset_index()
+    )
+
+    # 取每位客戶的名稱（假設同一 customer_id 對應唯一名稱）
+    names = (
+        data[["customer_id", "customer_name"]]
+        .drop_duplicates(subset=["customer_id"])
+    )
+
+    result = pd.merge(agg, names, on="customer_id", how="left")
+
+    result = result[["customer_id", "customer_name", "R", "F", "M"]]
+    result = result.sort_values("M", ascending=False).head(5)
+
+    return result.reset_index(drop=True)
\ No newline at end of file
diff --git a/homework/m4_timeseries.py b/homework/m4_timeseries.py
index 047af6a..2e87405 100644
--- a/homework/m4_timeseries.py
+++ b/homework/m4_timeseries.py
@@ -26,8 +26,8 @@ def green_avg_by_month():
     回傳 Series（index=月份 1~12, values=平均金額）
     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():
@@ -36,8 +36,8 @@ def green_top3_dates():
     回傳 Series（index=日期, values=訂單數, 由多到少排序）
     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():
@@ -45,8 +45,8 @@ def green_date_range():
     回傳資料的日期範圍 tuple: (最早日期, 最晚日期)
     格式為 pandas Timestamp
     """
-    # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    return df["order_date"].min(), df["order_date"].max()
 
 
 # ============================================================
@@ -59,8 +59,8 @@ def yellow_monthly_revenue():
     回傳 Series（index=月底日期 period, values=總營收）
     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):
@@ -70,8 +70,7 @@ def yellow_rolling_avg(monthly_revenue):
     回傳 Series（同樣 index，values=移動平均，前 2 筆可為 NaN）
     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):
@@ -80,8 +79,7 @@ def yellow_category_median(df):
     回傳 Series（index=category, values=中位數）
     提示：groupby + median + sort_values
     """
-    # TODO: 你的程式碼
-    pass
+    return df.groupby("category")["amount"].median().sort_values(ascending=False)
 
 
 # ============================================================
@@ -100,5 +98,19 @@ def red_monthly_report():
     index 為月份 (period 或 datetime)
     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()
+
+    return monthly
\ No newline at end of file
diff --git a/homework/m5_visualization.py b/homework/m5_visualization.py
index 7e7335d..7e995a3 100644
--- a/homework/m5_visualization.py
+++ b/homework/m5_visualization.py
@@ -28,8 +28,14 @@ def green_bar_category():
     回傳 matplotlib Figure 物件
     提示：sns.countplot 或 value_counts().plot.bar()
     """
-    # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    fig, ax = plt.subplots()
+    sns.countplot(x="category", data=df, ax=ax)
+    ax.set_title("Order Count by Category")
+    ax.set_xlabel("Category")
+    ax.set_ylabel("Count")
+    fig.tight_layout()
+    return fig
 
 
 def green_hist_amount():
@@ -38,8 +44,14 @@ def green_hist_amount():
     回傳 matplotlib Figure 物件
     提示：sns.histplot(bins=20) 或 plt.hist()
     """
-    # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    fig, ax = plt.subplots()
+    sns.histplot(df["amount"], bins=20, ax=ax)
+    ax.set_title("Distribution of Order Amount")
+    ax.set_xlabel("Amount")
+    ax.set_ylabel("Frequency")
+    fig.tight_layout()
+    return fig
 
 
 def green_set_labels():
@@ -50,8 +62,13 @@ def green_set_labels():
     - Y 軸標籤 (ylabel)
     回傳 matplotlib Figure 物件
     """
-    # TODO: 你的程式碼
-    pass
+    fig, ax = plt.subplots()
+    ax.bar(["A", "B", "C"], [10, 20, 15])
+    ax.set_title("Simple Bar Chart")
+    ax.set_xlabel("Category")
+    ax.set_ylabel("Value")
+    fig.tight_layout()
+    return fig
 
 
 # ============================================================
@@ -67,8 +84,25 @@ def yellow_line_region_trend():
     回傳 matplotlib Figure 物件
     提示：分別 groupby 再 plot，或用 sns.lineplot(hue='region')
     """
-    # TODO: 你的程式碼
-    pass
+    df = _load_data()
+
+    sub = df[df["region"].isin(["North", "South"])].copy()
+    sub["month"] = sub["order_date"].dt.to_period("M").dt.to_timestamp()
+
+    monthly = (
+        sub.groupby(["month", "region"])["amount"]
+        .sum()
+        .reset_index()
+    )
+
+    fig, ax = plt.subplots()
+    sns.lineplot(data=monthly, x="month", y="amount", hue="region", ax=ax)
+    ax.set_title("Monthly Revenue Trend (North vs South)")
+    ax.set_xlabel("Month")
+    ax.set_ylabel("Revenue")
+    ax.legend(title="Region")
+    fig.tight_layout()
+    return fig
 
 
 def yellow_box_vip():
@@ -77,8 +111,14 @@ def yellow_box_vip():
     回傳 matplotlib Figure 物件
     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()
+    sns.boxplot(x="vip_level", y="amount", data=df, ax=ax)
+    ax.set_title("Amount Distribution by VIP Level")
+    ax.set_xlabel("VIP Level")
+    ax.set_ylabel("Amount")
+    fig.tight_layout()
+    return fig
 
 
 def yellow_scatter_price_amount():
@@ -87,8 +127,14 @@ def yellow_scatter_price_amount():
     回傳 matplotlib Figure 物件
     提示：plt.scatter() 或 sns.scatterplot()
     """
-    # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    fig, ax = plt.subplots()
+    sns.scatterplot(x="unit_price", y="amount", data=df, ax=ax)
+    ax.set_title("Unit Price vs Order Amount")
+    ax.set_xlabel("Unit Price")
+    ax.set_ylabel("Amount")
+    fig.tight_layout()
+    return fig
 
 
 # ============================================================
@@ -106,5 +152,36 @@ def red_category_dashboard(category="Electronics"):
     回傳 matplotlib Figure 物件
     提示：fig, axes = plt.subplots(2, 2, figsize=(14, 10))
     """
-    # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    sub = df[df["category"] == category].copy()
+
+    fig, axes = plt.subplots(2, 2, figsize=(14, 10))
+
+    # 1. 月營收趨勢
+    sub["month"] = sub["order_date"].dt.to_period("M").dt.to_timestamp()
+    monthly = sub.groupby("month")["amount"].sum().reset_index()
+    sns.lineplot(data=monthly, x="month", y="amount", ax=axes[0, 0])
+    axes[0, 0].set_title(f"{category} Monthly Revenue")
+
+    # 2. 各地區營收
+    region_rev = sub.groupby("region")["amount"].sum().reset_index()
+    sns.barplot(data=region_rev, x="region", y="amount", ax=axes[0, 1])
+    axes[0, 1].set_title(f"{category} Revenue by Region")
+
+    # 3. Top 5 商品
+    top_products = (
+        sub.groupby("product_name")["amount"]
+        .sum()
+        .sort_values(ascending=False)
+        .head(5)
+        .reset_index()
+    )
+    sns.barplot(data=top_products, y="product_name", x="amount", ax=axes[1, 0])
+    axes[1, 0].set_title(f"Top 5 Products in {category}")
+
+    # 4. 金額分佈
+    sns.histplot(sub["amount"], bins=20, ax=axes[1, 1])
+    axes[1, 1].set_title(f"{category} Amount Distribution")
+
+    fig.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..3d48e6a 100644
--- a/homework/m6_plotly_capstone.py
+++ b/homework/m6_plotly_capstone.py
@@ -25,8 +25,11 @@ def green_plotly_bar():
     回傳 plotly Figure 物件
     提示：px.bar()
     """
-    # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv",
+                     parse_dates=["order_date"])
+    agg = df.groupby("category")["amount"].sum().reset_index()
+    fig = px.bar(agg, x="category", y="amount", title="Revenue by Category")
+    return fig
 
 
 def green_plotly_line():
@@ -36,8 +39,12 @@ def green_plotly_line():
     回傳 plotly Figure 物件
     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").dt.to_timestamp()
+    agg = df.groupby("month")["amount"].sum().reset_index()
+    fig = px.line(agg, x="month", y="amount", title="Monthly Revenue Trend")
+    return fig
 
 
 def green_plotly_pie():
@@ -47,8 +54,12 @@ def green_plotly_pie():
     回傳 plotly Figure 物件
     提示：px.pie()
     """
-    # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv")
+    agg = df["vip_level"].value_counts().reset_index()
+    agg.columns = ["vip_level", "count"]
+    fig = px.pie(agg, names="vip_level", values="count",
+                 title="Order Share by VIP Level")
+    return fig
 
 
 # ============================================================
@@ -62,8 +73,33 @@ 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
+    df = pd.read_csv(raw_path)
+
+    # 欄位名稱清理
+    df.columns = df.columns.str.strip().str.lower()
+
+    # amount 清理
+    df["amount"] = (
+        df["amount"]
+        .astype(str)
+        .str.replace("$", "", regex=False)
+        .str.replace(",", "", regex=False)
+        .astype(float)
+    )
+
+    # 日期轉換
+    df["order_date"] = pd.to_datetime(df["order_date"], errors="coerce")
+
+    # 移除缺值與重複
+    df = df.dropna(subset=["amount", "order_date"]).drop_duplicates()
+
+    customers = pd.read_csv(customers_path)
+    products = pd.read_csv(products_path)
+
+    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):
@@ -76,8 +112,17 @@ def yellow_kpi_summary(df):
         "avg_order_value": float,     # 平均客單價
     }
     """
-    # TODO: 你的程式碼
-    pass
+    total_revenue = float(df["amount"].sum())
+    order_count = int(len(df))
+    active_customers = int(df["customer_id"].nunique())
+    avg_order_value = float(total_revenue / order_count) if order_count > 0 else 0.0
+
+    return {
+        "total_revenue": total_revenue,
+        "order_count": order_count,
+        "active_customers": active_customers,
+        "avg_order_value": avg_order_value,
+    }
 
 
 def yellow_plotly_scatter(df):
@@ -90,8 +135,15 @@ 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"
+    )
+    return fig
 
 
 # ============================================================
@@ -114,5 +166,61 @@ def red_dashboard():
     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"
+    )
+
+    fig = make_subplots(
+        rows=2, cols=2,
+        subplot_titles=(
+            "Monthly Revenue",
+            "Top 10 Products",
+            "Revenue by Region",
+            "Revenue Share by Category"
+        ),
+        specs=[
+            [{"type": "xy"}, {"type": "xy"}],
+            [{"type": "xy"}, {"type": "domain"}],
+        ]
+    )
+
+    # 月營收
+    df["month"] = df["order_date"].dt.to_period("M").dt.to_timestamp()
+    monthly = df.groupby("month")["amount"].sum().reset_index()
+    fig.add_trace(
+        go.Scatter(x=monthly["month"], y=monthly["amount"], mode="lines", name="Revenue"),
+        row=1, col=1
+    )
+
+    # Top 10 商品
+    top_products = (
+        df.groupby("product_name")["amount"]
+        .sum()
+        .sort_values(ascending=False)
+        .head(10)
+        .reset_index()
+    )
+    fig.add_trace(
+        go.Bar(x=top_products["product_name"], y=top_products["amount"], name="Top Products"),
+        row=1, col=2
+    )
+
+    # 地區營收
+    region = df.groupby("region")["amount"].sum().reset_index()
+    fig.add_trace(
+        go.Bar(x=region["region"], y=region["amount"], name="Region Revenue"),
+        row=2, col=1
+    )
+
+    # 類別佔比
+    category = df.groupby("category")["amount"].sum().reset_index()
+    fig.add_trace(
+        go.Pie(labels=category["category"], values=category["amount"], hole=0.4),
+        row=2, col=2
+    )
+
+    fig.update_layout(title="E-commerce Dashboard")
+
+    return fig
\ No newline at end of file