diff --git a/homework/m1_numpy.py b/homework/m1_numpy.py
index 5ff41ab..dfb6196 100644
--- a/homework/m1_numpy.py
+++ b/homework/m1_numpy.py
@@ -19,19 +19,25 @@
 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]
 
 
 # ============================================================
@@ -42,7 +48,7 @@ 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 (prices > 1000).sum()
 
 
 def yellow_top3_stock_indices(stocks):
@@ -51,7 +57,7 @@ def yellow_top3_stock_indices(stocks):
     提示：np.argsort
     """
     # TODO: 你的程式碼
-    pass
+    return np.argsort(-stocks)[0:3]
 
 
 def yellow_restock_cost(prices, stocks):
@@ -60,7 +66,9 @@ def yellow_restock_cost(prices, stocks):
     提示：布林遮罩 + .sum()
     """
     # TODO: 你的程式碼
-    pass
+    product = prices[prices < 500]
+
+    return (product * 50).sum()
 
 
 # ============================================================
@@ -77,4 +85,8 @@ 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)
+        )
\ No newline at end of file
diff --git a/homework/m2_pandas_cleaning.py b/homework/m2_pandas_cleaning.py
index fa36bff..04e9ef8 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 +52,10 @@ def yellow_clean_columns(df):
     提示：df.columns.str.strip().str.lower()
     """
     # TODO: 你的程式碼
-    pass
+    clean = df.copy()
+    clean.columns = clean.columns.str.strip().str.lower()
+
+    return clean
 
 
 def yellow_clean_amount(df):
@@ -63,7 +66,10 @@ def yellow_clean_amount(df):
     提示：.str.replace() + .astype(float)
     """
     # TODO: 你的程式碼
-    pass
+    clean = df.copy()
+    clean["amount"] = clean["amount"].str.replace("$", "", regex=False).str.replace(",", "", regex=False).astype(float)
+
+    return clean
 
 
 def yellow_drop_duplicates(df):
@@ -72,7 +78,7 @@ def yellow_drop_duplicates(df):
     提示：df.drop_duplicates()
     """
     # TODO: 你的程式碼
-    pass
+    return df.drop_duplicates()
 
 
 # ============================================================
@@ -93,4 +99,11 @@ 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
\ No newline at end of file
diff --git a/homework/m3_pandas_advanced.py b/homework/m3_pandas_advanced.py
index 68410e8..afa4d8f 100644
--- a/homework/m3_pandas_advanced.py
+++ b/homework/m3_pandas_advanced.py
@@ -24,19 +24,26 @@ 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")
+    df = df.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.columns)
 
 
 # ============================================================
@@ -50,7 +57,9 @@ def yellow_top_category(df):
     提示：groupby('category')['amount'].sum()
     """
     # TODO: 你的程式碼
-    pass
+    revenue = df.groupby("category")["amount"].sum().idxmax()
+
+    return revenue
 
 
 def yellow_gold_vip_stats(df):
@@ -60,7 +69,10 @@ def yellow_gold_vip_stats(df):
     提示：df[df['vip_level'] == 'Gold']
     """
     # TODO: 你的程式碼
-    pass
+    orders = df[df["vip_level"] == "Gold"]["order_id"].count()
+    total = df[df["vip_level"] == "Gold"]["amount"].sum()
+
+    return (orders, total)
 
 
 def yellow_region_avg_amount(df):
@@ -70,7 +82,9 @@ def yellow_region_avg_amount(df):
     提示：groupby('region')['amount'].mean()
     """
     # TODO: 你的程式碼
-    pass
+    average = df.groupby("region")["amount"].mean()
+
+    return average
 
 
 # ============================================================
@@ -94,4 +108,13 @@ def red_rfm_top5(df):
     提示：groupby('customer_id').agg(...)
     """
     # TODO: 你的程式碼
-    pass
+    rfm = df.groupby("customer_id").agg(
+        customer_name=("customer_name", "first"),
+        R=("order_date", "max"),
+        F=("order_id", "count"),
+        M=("amount", "sum")
+        ).reset_index()
+    
+    top5 = rfm.sort_values("M", ascending=False).head(5)[["customer_id", "customer_name", "R", "F", "M"]]
+
+    return top5
\ No newline at end of file
diff --git a/homework/m4_timeseries.py b/homework/m4_timeseries.py
index 047af6a..496e209 100644
--- a/homework/m4_timeseries.py
+++ b/homework/m4_timeseries.py
@@ -27,7 +27,10 @@ def green_avg_by_month():
     提示：df['order_date'].dt.month
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    month = df["order_date"].dt.month
+
+    return df.groupby(month)["amount"].mean().round(1)
 
 
 def green_top3_dates():
@@ -37,7 +40,10 @@ def green_top3_dates():
     提示：value_counts().head(3)
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    top3 = df["order_date"].dt.date.value_counts().head(3)
+
+    return top3
 
 
 def green_date_range():
@@ -46,7 +52,11 @@ def green_date_range():
     格式為 pandas Timestamp
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    earliest = df["order_date"].min()
+    latest = df["order_date"].max()
+
+    return (earliest, latest)
 
 
 # ============================================================
@@ -60,7 +70,10 @@ def yellow_monthly_revenue():
     提示：set_index('order_date').resample('ME')['amount'].sum()
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    monthly = df.set_index("order_date").resample("ME")["amount"].sum()
+
+    return monthly
 
 
 def yellow_rolling_avg(monthly_revenue):
@@ -71,7 +84,7 @@ 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 +94,7 @@ 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 +114,15 @@ 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=("order_id", "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..f7fa554 100644
--- a/homework/m5_visualization.py
+++ b/homework/m5_visualization.py
@@ -29,7 +29,19 @@ 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))
+    
+    sns.countplot(data=df, x="category", ax=ax)
+
+    ax.set_title("Order Count by Category")
+    ax.set_xlabel("Category")
+    ax.set_ylabel("Order Count")
+
+    fig.tight_layout()
+    
+    return fig
 
 
 def green_hist_amount():
@@ -39,7 +51,19 @@ 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, 4))
+
+    sns.histplot(data=df, x="amount", bins=20, ax=ax)
+
+    ax.set_title("Distribution of Order Amount")
+    ax.set_xlabel("Amount")
+    ax.set_ylabel("Count")
+
+    fig.tight_layout()
+    
+    return fig
 
 
 def green_set_labels():
@@ -51,7 +75,23 @@ def green_set_labels():
     回傳 matplotlib Figure 物件
     """
     # TODO: 你的程式碼
-    pass
+    fig, ax = plt.subplots()
+    
+    brands = ["Samsung", "Apple", "Xiaomi", "OPPO", "vivo", "Others"]
+    market_share = [22, 20, 11, 10, 7, 29]
+
+    ax.bar(brands, market_share)
+    ax.set_title("Global Smartphones Brand Market Share")
+    ax.set_xlabel("Brand")
+    ax.set_ylabel("Market Share (%)")
+    ax.set_ylim(0, 35)
+    
+    for i, value in enumerate(market_share):
+        ax.text(i, value + 0.5, str(value) + "%", ha="center")
+
+    fig.tight_layout()
+
+    return fig
 
 
 # ============================================================
@@ -68,7 +108,26 @@ 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 = df[df["region"].isin(["North", "South"])].copy()
+    df["month"] = df["order_date"].dt.to_period("M").dt.to_timestamp()
+
+    monthly = df.groupby(["month", "region"])["amount"].sum().reset_index()
+
+    fig, ax = plt.subplots(figsize=(10, 5))
+
+    sns.lineplot(data=monthly, x="month", y="amount", hue="region", marker="o", ax=ax)
+
+    ax.set_title("Monthly Revenue Trend: North vs South")
+    ax.set_xlabel("Month")
+    ax.set_ylabel("Total Revenue")
+    ax.legend(title="Region")
+
+    fig.autofmt_xdate()
+    fig.tight_layout()
+
+    return fig
 
 
 def yellow_box_vip():
@@ -78,7 +137,19 @@ 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, 4))
+
+    sns.boxplot(data=df, x="vip_level", y="amount", ax=ax)
+
+    ax.set_title("Order Amount Distribution by VIP Level")
+    ax.set_xlabel("VIP Level")
+    ax.set_ylabel("Order Amount")
+
+    fig.tight_layout()
+
+    return fig
 
 
 def yellow_scatter_price_amount():
@@ -88,7 +159,19 @@ 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, 4))
+
+    sns.scatterplot(data=df, x="unit_price", y="amount", ax=ax)
+    
+    ax.set_title("Unit Price vs Order Amount")
+    ax.set_xlabel("Unit Price")
+    ax.set_ylabel("Order Amount")
+
+    fig.tight_layout()
+
+    return fig
 
 
 # ============================================================
@@ -107,4 +190,49 @@ 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()
+    
+    sub = df[df["category"] == category].copy()
+    sub["month"] = sub["order_date"].dt.to_period("M").dt.to_timestamp()
+
+    fig, ax = plt.subplots(2, 2, figsize=(14, 10))
+
+    # 1. 左上：月營收趨勢 (折線圖)
+    monthly = sub.groupby("month")["amount"].sum().reset_index()
+    
+    sns.lineplot(data=monthly, x="month", y="amount", ax=ax[0, 0])
+
+    ax[0, 0].set_title(f"{category} Monthly Revenue Trend")
+    ax[0, 0].set_xlabel("Month")
+    ax[0, 0].set_ylabel("Revenue")
+
+    # 2. 右上：各地區營收 (長條圖)
+    region_sales = sub.groupby("region")["amount"].sum().reset_index()
+    
+    sns.barplot(data=region_sales, x="region", y="amount", ax=ax[0, 1])
+
+    ax[0, 1].set_title(f"{category} Revenue by Region")
+    ax[0, 1].set_xlabel("Region")
+    ax[0, 1].set_ylabel("Revenue")
+
+    # 3. 左下：Top 5 商品營收 (水平長條圖)
+    top_products = sub.groupby("product_name")["amount"].sum().sort_values(ascending=False).head(5).reset_index()
+    
+    sns.barplot(data=top_products, x="amount", y="product_name", ax=ax[1, 0])
+
+    ax[1, 0].set_title(f"{category} Top 5 Products by Revenue")
+    ax[1, 0].set_xlabel("Revenue")
+    ax[1, 0].set_ylabel("Product Name")
+
+    # 4. 右下：訂單金額分佈 (直方圖)
+    sns.histplot(data=sub, x="amount", bins=20, ax=ax[1, 1])
+
+    ax[1, 1].set_title(f"{category} Order Amount Distribution")
+    ax[1, 1].set_xlabel("Order Amount")
+    ax[1, 1].set_ylabel("Count")
+
+    fig.suptitle(f"Category Dashboard: {category}", fontsize=16)
+    fig.autofmt_xdate()
+    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..c739686 100644
--- a/homework/m6_plotly_capstone.py
+++ b/homework/m6_plotly_capstone.py
@@ -26,7 +26,31 @@ def green_plotly_bar():
     提示：px.bar()
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv")
+    
+    category_revenue = df.groupby("category")["amount"].sum().reset_index().sort_values("amount", ascending=False)
+
+    fig = px.bar(
+        category_revenue,
+        x="category",
+        y="amount",
+        title="Revenue by Category",
+        labels={"category": "Category", "amount": "Total Revenue"},
+        text="amount"
+        )
+    
+    fig.update_traces(
+        texttemplate="%{text:.0f}",
+        textposition="outside"
+        )
+    fig.update_layout(
+        xaxis_title="Category",
+        yaxis_title="Total Revenue",
+        uniformtext_minsize=8,
+        uniformtext_mode="hide"
+        )
+    
+    return fig
 
 
 def green_plotly_line():
@@ -37,7 +61,27 @@ 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")
+    df["order_date"] = pd.to_datetime(df["order_date"])
+    df["month"] = df["order_date"].dt.to_period("M").dt.to_timestamp()
+
+    monthly = df.groupby("month")["amount"].sum().reset_index().sort_values("month")
+
+    fig = px.line(
+        monthly,
+        x="month",
+        y="amount",
+        markers=True,
+        title="Monthly Revenue Trend",
+        labels={"month": "Month", "amount": "Total Revenue"}
+        )
+    
+    fig.update_layout(
+        xaxis_title="Month",
+        yaxis_title="Total Revenue"
+        )
+    
+    return fig
 
 
 def green_plotly_pie():
@@ -48,7 +92,20 @@ def green_plotly_pie():
     提示：px.pie()
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv")
+
+    vip_counts = df["vip_level"].value_counts().reset_index()
+    vip_counts.columns = ["vip_level", "order_count"]
+
+    fig = px.pie(vip_counts, names="vip_level", values="order_count", title="Order Count Share by VIP Level")
+
+    fig.update_traces(
+        textinfo="label+percent",
+        hovertemplate="VIP Level: %{label}<br>Order Count: %{value}<br>Share: %{percent}<extra></extra>"
+        )
+    fig.update_layout(title_x=0.5)
+
+    return fig
 
 
 # ============================================================
@@ -63,7 +120,27 @@ 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
+    orders = pd.read_csv(raw_path)
+    customers = pd.read_csv(customers_path)
+    products = pd.read_csv(products_path)
+
+    # 欄位名稱
+    orders.columns = orders.columns.str.strip().str.lower()
+    
+    # 金額
+    orders["amount"] = (orders["amount"].astype(str).str.replace("$", "", regex=False).str.replace(",", "", regex=False).astype(float))
+    
+    # 日期
+    orders["order_date"] = pd.to_datetime(orders["order_date"], errors="coerce")
+    
+    # 缺值、去重
+    orders = orders.dropna(subset=["amount", "order_date"]).drop_duplicates()
+
+    # 合併
+    merge = orders.merge(customers, on="customer_id", how="left")
+    merge = merge.merge(products, on="product_id", how="left")
+    
+    return merge
 
 
 def yellow_kpi_summary(df):
@@ -77,7 +154,17 @@ def yellow_kpi_summary(df):
     }
     """
     # TODO: 你的程式碼
-    pass
+    total_revenue = df["amount"].sum()
+    order_count = len(df)
+    active_customers = df["customer_id"].nunique()
+    avg_order_value = total_revenue / order_count
+
+    return {
+        "total_revenue": float(total_revenue),
+        "order_count": int(order_count),
+        "active_customers": int(active_customers),
+        "avg_order_value": float(avg_order_value),
+        }
 
 
 def yellow_plotly_scatter(df):
@@ -91,7 +178,27 @@ 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 by Category",
+        labels={
+            "unit_price": "Unit Price",
+            "amount": "Order Amount",
+            "category": "Category",
+            "product_name": "Product Name"
+            }
+            )
+    
+    fig.update_layout(
+        xaxis_title="Unit Price",
+        yaxis_title="Order Amount"
+        )
+
+    return fig
 
 
 # ============================================================
@@ -115,4 +222,38 @@ def red_dashboard():
     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",
+        )
+    
+    df["month"] = df["order_date"].dt.to_period("M").dt.to_timestamp()
+
+    monthly = df.groupby("month")["amount"].sum().reset_index().sort_values("month")
+    top_products = df.groupby("product_name")["amount"].sum().sort_values(ascending=False).head(10).reset_index()
+    region_revenue = df.groupby("region")["amount"].sum().reset_index().sort_values("amount", ascending=False)
+    category_revenue = df.groupby("category")["amount"].sum().reset_index().sort_values("amount", ascending=False)
+
+    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"}]]
+        )
+    
+    fig.add_trace(go.Scatter(x=monthly["month"], y=monthly["amount"], mode="lines+markers", name="Monthly Revenue"), row=1, col=1)
+    fig.add_trace(go.Bar(x=top_products["product_name"], y=top_products["amount"], name="Top Products"), row=1, col=2)
+    fig.add_trace(go.Bar(x=region_revenue["region"], y=region_revenue["amount"], name="Region Revenue"), row=2, col=1)
+    fig.add_trace(go.Pie(labels=category_revenue["category"], values=category_revenue["amount"], hole=0.4, name="Category Revenue"), row=2, col=2)
+
+    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="Product Name", row=1, col=2)
+    fig.update_yaxes(title_text="Revenue", row=1, col=2)
+    fig.update_xaxes(title_text="Region", row=2, col=1)
+    fig.update_yaxes(title_text="Revenue", row=2, col=1)
+
+    fig.update_layout(title_text="Ecommerce Dashboard", title_x=0.5, height=800, showlegend=True)
+    
+    return fig
\ No newline at end of file