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Data Structure ?
*The above analysis is the result extracted and analyzed by the system, and the specific actual data shall prevail.
README.md
コンテキスト
このデータセットは、顧客セグメンテーションの概念（マーケットバスケット分析とも呼ばれる）を学習する目的でのみ作成されています。私は、教師なし機械学習手法（KMeansクラスタリングアルゴリズム）を最もシンプルな形で使ってこれを実証します。
内容
あなたはスーパーマーケットのモールを経営しており、会員カードを通じて、顧客ID、年齢、性別、年収、消費スコアなどの顧客に関する基本的なデータを持っています。
消費スコアは、顧客行動や購買データなどのあなたが定義したパラメータに基づいて顧客に割り当てるものです。
**問題の定式化**
あなたはモールを所有しており、誰が簡単に取り込めるか（ターゲット顧客）といった顧客の理解を深め、マーケティングチームにその感覚を伝え、それに応じた戦略を立てたいと考えています。
謝辞
Udemyの「Machine Learning A - Z」コースから。
私はデータサイエンスの分野に新参で、自分の知識を他の人と共有したいと思っています。
https://github.com/SteffiPeTaffy/machineLearningAZ/blob/master/Machine%20Learning%20A - Z%20Template%20Folder/Part%204%20-%20Clustering/Section%2025%20-%20Hierarchical%20Clustering/Mall_Customers.csv
啓発
このケーススタディの最後まで進めると、以下の質問に答えることができるようになります。
1 - Pythonで機械学習アルゴリズム（KMeansクラスタリング）を使って最も簡単な方法で顧客セグメンテーションを達成する方法は？
2 - どのような顧客がターゲット顧客で、マーケティング戦略を開始できるか（簡単に取り込める顧客）？
3 - マーケティング戦略が現実世界でどのように機能するか？
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- 1. If any data on this site involves infringement, please contact us promptly, and we will arrange for the data to be taken offline.
- 1. The data originates from internet data collection or provided by service providers, and this platform offers users the ability to view and browse datasets.
- 2. This platform serves only as a basic information display for datasets, including but not limited to image, text, video, and audio file types.
- 3. Basic dataset information comes from the original data source or the information provided by the data provider. If there are discrepancies in the dataset description, please refer to the original data source or service provider's address.
- 1. All datasets on this site are copyrighted by their original publishers or data providers.
- 1. If you need to repost data from this site, please retain the original data source URL and related copyright notices.
- 1. If any data on this site involves infringement, please contact us promptly, and we will arrange for the data to be taken offline.
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