實例 1：智能銷售預測

Context: You have an Excel table containing sales figures for each month over the past three years, and you want to predict sales for the next six months.

方法 (Excel 內建)：

1. Select the two columns containing the dates and sales amounts.
2. Go to the "Data" tab and click "Prediction Worksheet."
3. Excel automatically analyzes the data for seasonality and generates a new worksheet and chart containing the forecast values and confidence intervals. You can adjust the forecast end date, confidence intervals, and other parameters.

Method (Azure ML Gain Set):

1. 在 Azure ML Studio 中訓練一個時間序列預測模型（例如 ARIMA 或指數平滑法）。
2. Deploy the model as a web service.
3. 在 Excel 中安裝 Azure Machine Learning 增益集。
4. Connect to your web service using the Enhance Collection, input historical sales data, and directly obtain the prediction results in Excel.

實例 2：網站流量異常偵測

Context: You have an Excel spreadsheet that records the number of website visits per hour and would like to automatically flag any unusual spikes or dips in traffic.

The method (combined with Python):

1. Use pandas Read Excel data into a Python DataFrame.
2. Leverage scikit-learn Anomaly detection algorithms in the center, such as Isolation Forest or One-Class SVM, analyze time series data.
3. The algorithm calculates an anomaly score or label for each point in time based on the number of visits.
4. Write back the abnormal time points and their data to a new column in Excel, or highlight these anomalies using conditional formatting.

程式碼片段 (概念性)：

實例 3：客戶評論情感分析

Context: 您收集了大量客戶在線上留下的產品評論，儲存在 Excel 中，希望快速了解評論的整體情感傾向。

方法 (API 調用 – 以 Azure Cognitive Services 為例)：

1. Create a "Language Service" resource on Azure to obtain an API key and endpoint.
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   • 遍歷 Excel 中包含評論的儲存格。
   • 對於每條評論，建構一個 HTTP POST 請求到 Azure 語言服務的情感分析端點，請求本文包含評論文字。
   • 在請求標頭中包含您的 API 金鑰。
   • Parse the JSON response returned by the API to extract sentiment scores (e.g., positive, negative, and neutral trust scores).
   • 將提取到的情感分數或主要情感標籤寫入旁邊的儲存格。

Tips: 處理大量評論時，注意 API 的速率限制和成本。

實例 4：基於購買行為的客戶細分

Context: You have customer purchase records (purchase frequency, total purchase amount, most recent purchase date, etc.) and would like to group customers into different categories (such as high-value customers, potential customers, and customers at risk of churn).

方法 (結合 Python 和 K-Means)：

1. Use pandas Read customer purchase data from Excel
2. 進行必要的資料預處理，例如計算 RFM 指標（Recency, Frequency, Monetary）。
3. Standardize RFM metrics or other relevant features (because K-Means is scale-sensitive).
4. Use scikit-learn 的 KMeans The algorithm performs clustering. You need to decide how many clusters to create (the value of k), which can be determined using techniques such as the elbow method.
5. 將每個客戶所屬的群組標籤寫回 Excel。
6. 分析每個群組的特徵（例如，計算各群組的平均 RFM 值），為每個群組命名並制定相應的行銷策略。