diff --git a/.gitignore b/.gitignore
index 911bb2d..2169d7a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,3 +2,4 @@ __pycache__/
 *.pyc
 .pytest_cache/
 .pytest_results.json
+venv
\ No newline at end of file
diff --git a/homework/m1_numpy.py b/homework/m1_numpy.py
index 5ff41ab..c48b408 100644
--- a/homework/m1_numpy.py
+++ b/homework/m1_numpy.py
@@ -15,23 +15,26 @@
 # ============================================================
 # 🟢 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]).mean()
+    print(arr)
+    return 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])
+    arr = arr*2
+    return arr
+
 
 
 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])
+    arr = arr[arr>25]
+    return arr
 
 
 # ============================================================
@@ -39,29 +42,32 @@ def green_filter():
 # 以下函式會接收從 products.csv 讀出的 prices, stocks 陣列
 # ============================================================
 
+# ---------- 載入 products.csv ----------
+DATA_PATH = "datasets/ecommerce/products.csv"
+prices = np.genfromtxt(DATA_PATH, delimiter=",", skip_header=1, usecols=3)
+stocks = np.genfromtxt(DATA_PATH, delimiter=",", skip_header=1, usecols=4)
+
 def yellow_expensive_count(prices):
     """回傳單價 > 1000 的商品數量 (int)"""
-    # TODO: 你的程式碼
-    pass
-
+    rrrr = len(prices[prices>1000])
+    return rrrr
 
 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
-
+    GGGG = np.argsort(stocks)[::-1][:3]
+    return GGGG
 
 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
-
+    rrrr = (prices[prices<500]*50).sum()
+    
+    return rrrr
 
 # ============================================================
 # 🔴 挑戰題（25 分）
@@ -76,5 +82,6 @@ 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
+    new_prices = np.where(stocks>=100 ,prices * 0.7,np.where((stocks>=20)&(stocks<100),prices*0.9,prices))
+    print(new_prices)
+    return new_prices
diff --git a/homework/m2_pandas_cleaning.py b/homework/m2_pandas_cleaning.py
index fa36bff..b0c8462 100644
--- a/homework/m2_pandas_cleaning.py
+++ b/homework/m2_pandas_cleaning.py
@@ -9,7 +9,6 @@
 """
 import pandas as pd
 
-
 # ============================================================
 # 🟢 送分題（每題 10 分，共 30 分）
 # ============================================================
@@ -17,10 +16,10 @@
 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()
+    提示：pd.read_csv(s)
     """
-    # TODO: 你的程式碼
-    pass
+    df = pd.read_csv('datasets/ecommerce/orders_raw.csv')
+    return df
 
 
 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):
@@ -37,9 +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
 
 # ============================================================
 # 🟡 核心題（每題 15 分，共 45 分）
@@ -51,9 +47,9 @@ 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):
     """
@@ -62,17 +58,19 @@ 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'].str.replace('$','',regex=False).str.replace(',','',regex=False).astype(float)
+    return new_df
 
 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
+    new_df = df.copy()
+    new_df = new_df.drop_duplicates(subset=None, keep='first', inplace=False)
+    return new_df
+
 
 
 # ============================================================
@@ -92,5 +90,11 @@ 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)
+    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(subset=None, keep='first', inplace=False)
+    return df
+     
diff --git a/homework/m3_pandas_advanced.py b/homework/m3_pandas_advanced.py
index 68410e8..f1e7246 100644
--- a/homework/m3_pandas_advanced.py
+++ b/homework/m3_pandas_advanced.py
@@ -23,34 +23,56 @@ 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
+  
+    path = "datasets/ecommerce/"
+    
+    df_orders = pd.read_csv(path + "orders_clean.csv")
+    df_customers = pd.read_csv(path + "customers.csv")
+    df_products = pd.read_csv(path + "products.csv")
 
+    m1 = pd.merge(df_orders,df_customers,on='customer_id',how = 'left')
+    m2 = pd.merge(m1,df_products,on='product_id',how = 'left')
+
+    return m2
+
+
+  
 
 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()
 
 # ============================================================
 # 🟡 核心題（每題 15 分，共 45 分）
 # ============================================================
 
+# def yellow_top_category(df):
+#     """
+#     哪個商品類別 (category) 的總營收最高？
+#     回傳該類別名稱 (str)
+#     提示：groupby('category')['amount'].sum()
+#     """
+#     # return df.groupby('category')['amount'].sum().idxmax()
+#     df = df.groupby('category')['amount'].sum().idxmax()
+#     print(df)
+#     return  df
+# print(green_load_and_merge())
+
+
 def yellow_top_category(df):
     """
     哪個商品類別 (category) 的總營收最高？
-    回傳該類別名稱 (str)
-    提示：groupby('category')['amount'].sum()
     """
-    # TODO: 你的程式碼
-    pass
+
+    category_totals = df.groupby('category')['amount'].sum().idxmax()
+
+    return category_totals
+
+
 
 
 def yellow_gold_vip_stats(df):
@@ -59,8 +81,11 @@ 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
+
+    gold_df = df[df['vip_level'] == 'Gold']
+    order_count = len(gold_df)
+    total_amount = gold_df['amount'].sum()
+    return (int(order_count), float(total_amount))
 
 
 def yellow_region_avg_amount(df):
@@ -69,8 +94,8 @@ 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 +118,15 @@ def red_rfm_top5(df):
 
     提示：groupby('customer_id').agg(...)
     """
-    # TODO: 你的程式碼
-    pass
+ 
+    rrr = df.groupby(['customer_id','customer_name']).agg(
+         {'order_date':'max','order_id':'count','amount':'sum'}
+     ).reset_index()
+    rrr.columns = ['customer_id','customer_name','R','F','M']
+    result = rrr.sort_values(by='M',ascending=False).head(5)
+    print(result)
+    return result
+    
+
+
+
diff --git a/homework/m4_timeseries.py b/homework/m4_timeseries.py
index 047af6a..f63682b 100644
--- a/homework/m4_timeseries.py
+++ b/homework/m4_timeseries.py
@@ -26,9 +26,12 @@ 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()
+    df['order_date'] = pd.to_datetime(df['order_date'])
+    df = df.groupby(df['order_date'].dt.month)['amount'].mean()
+    print(df)
+    return df  
 
 def green_top3_dates():
     """
@@ -36,18 +39,23 @@ 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()
+    df['order_date'] = pd.to_datetime(df['order_date'])
+    print(df)
+    return df['order_date'].value_counts().head(3)
 
 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()
+    df['order_date'] = pd.to_datetime(df['order_date'])
+    min_date = df['order_date'].min()
+    max_date = df['order_date'].max()
+    return (min_date,max_date)
 
+green_date_range()
 
 # ============================================================
 # 🟡 核心題（每題 15 分，共 45 分）
@@ -59,9 +67,11 @@ 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()
+    df['order_date'] = pd.to_datetime(df['order_date'])
+    df = df.set_index(['order_date']).resample('ME')['amount'].sum()
+    return df
+ 
 
 def yellow_rolling_avg(monthly_revenue):
     """
@@ -70,8 +80,9 @@ 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 +91,13 @@ 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
+    df = _load_data()
+    df = df.groupby('category')['amount'].median().sort_values(ascending=False)
+    print(df.head())
+    return df
+
+
+
 
 
 # ============================================================
@@ -100,5 +116,17 @@ 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()
+    df['order_date'] = pd.to_datetime(df['order_date'])
+    
+    monthly_report = df.set_index('order_date').resample('ME').agg(
+        order_count=('order_id', 'count'),
+        revenue=('amount', 'sum'),
+        active_customers=('customer_id', 'nunique')
+    )
+    
+    monthly_report['avg_order_value'] = monthly_report['revenue'] / monthly_report['order_count']
+    monthly_report['revenue_growth'] = monthly_report['revenue'].pct_change() * 100
+    
+    return monthly_report
+ 
\ No newline at end of file
diff --git a/homework/m5_visualization.py b/homework/m5_visualization.py
index 7e7335d..7bfbef1 100644
--- a/homework/m5_visualization.py
+++ b/homework/m5_visualization.py
@@ -28,9 +28,10 @@ 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)
+    return fig
 
 def green_hist_amount():
     """
@@ -38,8 +39,10 @@ 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)
+    return fig
 
 
 def green_set_labels():
@@ -50,8 +53,13 @@ def green_set_labels():
     - Y 軸標籤 (ylabel)
     回傳 matplotlib Figure 物件
     """
-    # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    fig, ax = plt.subplots()
+    df['category'].value_counts().plot.bar(ax=ax)
+    ax.set_title("Order Count by Category")
+    ax.set_xlabel("Category")
+    ax.set_ylabel("Count")
+    return fig
 
 
 # ============================================================
@@ -67,8 +75,19 @@ 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()
+    # 建立月份欄位 (如: 2025-01)
+    df['month'] = df['order_date'].dt.to_period('M').astype(str)
+    
+    # 過濾地區並計算月營收
+    df_filtered = df[df['region'].isin(['North', 'South'])]
+    monthly_revenue = df_filtered.groupby(['month', 'region'])['amount'].sum().reset_index()
+    
+    fig, ax = plt.subplots(figsize=(10, 6))
+    sns.lineplot(x='month', y='amount', hue='region', data=monthly_revenue, ax=ax, marker='o')
+    plt.xticks(rotation=45)
+    ax.legend(title='Region')
+    return fig
 
 
 def yellow_box_vip():
@@ -77,8 +96,10 @@ 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)
+    return fig
 
 
 def yellow_scatter_price_amount():
@@ -87,8 +108,10 @@ 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)
+    return fig
 
 
 # ============================================================
@@ -106,5 +129,29 @@ 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()
+    cat_df = df[df['category'] == category].copy()
+    cat_df['month'] = cat_df['order_date'].dt.to_period('M').astype(str)
+
+    fig, axes = plt.subplots(2, 2, figsize=(14, 10))
+    fig.suptitle(f"Dashboard for {category}", fontsize=16)
+
+    # 1. 左上：該類別月營收趨勢 (折線圖)
+    cat_df.groupby('month')['amount'].sum().plot(ax=axes[0, 0], marker='s')
+    axes[0, 0].set_title("Monthly Revenue Trend")
+
+    # 2. 右上：該類別各地區營收 (長條圖)
+    cat_df.groupby('region')['amount'].sum().plot(kind='bar', ax=axes[0, 1])
+    axes[0, 1].set_title("Revenue by Region")
+
+    # 3. 左下：該類別 Top 5 商品營收 (水平長條圖)
+    top5 = cat_df.groupby('product_name')['amount'].sum().sort_values(ascending=False).head(5)
+    top5.sort_values().plot(kind='barh', ax=axes[1, 0])
+    axes[1, 0].set_title("Top 5 Products by Revenue")
+
+    # 4. 右下：該類別訂單金額分佈 (直方圖)
+    sns.histplot(cat_df['amount'], bins=15, ax=axes[1, 1], kde=True)
+    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..40d4e82 100644
--- a/homework/m6_plotly_capstone.py
+++ b/homework/m6_plotly_capstone.py
@@ -25,8 +25,10 @@ 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")
+    revenue_by_cat = df.groupby('category')['amount'].sum().reset_index()
+    fig = px.bar(revenue_by_cat, x='category', y='amount', title="Category Revenue")
+    return fig
 
 
 def green_plotly_line():
@@ -36,8 +38,11 @@ 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').astype(str)
+    revenue_by_month = df.groupby('month')['amount'].sum().reset_index()
+    fig = px.line(revenue_by_month, x='month', y='amount', title="Monthly Revenue Trend")
+    return fig
 
 
 def green_plotly_pie():
@@ -47,8 +52,9 @@ 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")
+    fig = px.pie(df, names='vip_level', title="Orders by VIP Level")
+    return fig
 
 
 # ============================================================
@@ -62,8 +68,24 @@ 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
+  
+    orders = pd.read_csv(raw_path)
+
+    orders.columns = orders.columns.str.strip().str.lower().str.replace(' ', '_')
+    
+    if 'amount' in orders.columns and orders['amount'].dtype == 'object':
+        orders['amount'] = orders['amount'].str.replace('$', '', regex=False).str.replace(',', '', regex=False).astype(float)
+        
+    orders = orders.drop_duplicates().dropna()
+    orders['order_date'] = pd.to_datetime(orders['order_date'])
+    
+    customers = pd.read_csv(customers_path)
+    products = pd.read_csv(products_path)
+    
+    df = orders.merge(customers, on='customer_id', how='left')
+    df = df.merge(products, on='product_id', how='left')
+    
+    return df
 
 
 def yellow_kpi_summary(df):
@@ -76,8 +98,16 @@ def yellow_kpi_summary(df):
         "avg_order_value": float,     # 平均客單價
     }
     """
-    # TODO: 你的程式碼
-    pass
+    total_rev = float(df['amount'].sum())
+    order_cnt = int(len(df))
+    
+    kpi_dict = {
+        "total_revenue": total_rev,
+        "order_count": order_cnt,
+        "active_customers": int(df['customer_id'].nunique()),
+        "avg_order_value": total_rev / order_cnt if order_cnt > 0 else 0.0
+    }
+    return kpi_dict
 
 
 def yellow_plotly_scatter(df):
@@ -90,8 +120,15 @@ 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 Amount"
+    )
+    return fig
 
 
 # ============================================================
@@ -114,5 +151,40 @@ def red_dashboard():
     回傳 plotly Figure 物件
     提示：from plotly.subplots import make_subplots
     """
-    # TODO: 你的程式碼
-    pass
+    # 1. 執行 ETL 取得乾淨合併的資料
+    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').astype(str)
+
+    # 2. 建立 2x2 子圖架構
+    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": "scatter"}, {"type": "bar"}],
+               [{"type": "bar"}, {"type": "pie"}]]
+    )
+
+    # 左上：月營收趨勢 (line)
+    m_rev = df.groupby('month')['amount'].sum().reset_index()
+    fig.add_trace(go.Scatter(x=m_rev['month'], y=m_rev['amount'], mode='lines+markers', name="Trend"), row=1, col=1)
+
+    # 右上：Top 10 商品營收 (bar)
+    top10 = df.groupby('product_name')['amount'].sum().sort_values(ascending=False).head(10).reset_index()
+    fig.add_trace(go.Bar(x=top10['product_name'], y=top10['amount'], name="Top 10"), row=1, col=2)
+
+    # 左下：各地區營收 (bar)
+    reg_rev = df.groupby('region')['amount'].sum().reset_index()
+    fig.add_trace(go.Bar(x=reg_rev['region'], y=reg_rev['amount'], name="Region"), row=2, col=1)
+
+    # 右下：類別營收佔比 (pie/donut)
+    cat_rev = df.groupby('category')['amount'].sum().reset_index()
+    fig.add_trace(go.Pie(labels=cat_rev['category'], values=cat_rev['amount'], name="Category"), row=2, col=2)
+
+    # 3. 設定整體標題與排版
+    fig.update_layout(title_text="E-commerce Interactive Dashboard", height=800, showlegend=False)
+     
+    return fig
\ No newline at end of file