This project focuses on analyzing sales data from an online cosmetics store, using Python's pandas library for Exploratory Data Analysis (EDA), data cleaning, and analysis. The project applies the RFM (Recency, Frequency, Monetary) model to classify customers into different segments. Visualization is achieved using the pyecharts library.

The dataset used in this project comes from a Chinese online store, offering valuable insights into customer purchasing behaviors in the cosmetics industry.

The dataset contains the following key fields:

• 订单编码 (Order ID)
• 订单日期 (Order Date)
• 客户编码 (Customer ID)
• 所在区域 (Area)
• 所在省份 (Province)
• 所在城市 (City)
• 商品编号 (commodyID)
• 订购数量 (saleAmount)
• 金额 (Amount)
• 商品名称 (Name)
• 商品小类 (Type1)
• 商品大类 (Type2)
• 销售单价 (commodyPrice)

• Python
• pandas for data analysis
• numpy for numerical operations
• pyecharts for data visualization
• matplotlib for additional visualizations (if needed)

1. Data Loading and Cleaning: Load the sales data, handle missing values, and remove duplicates.
2. EDA (Exploratory Data Analysis): Analyze customer behaviors, such as monthly sales trends and product popularity.
3. Visualization: Use pyecharts to create interactive charts showing sales trends, customer spending habits, etc.
4. RFM Model:
   • Recency: Days since the last purchase.
   • Frequency: Number of transactions within a specified period.
   • Monetary: Total amount spent by the customer.
5. Customer Segmentation: Based on RFM scores, customers are classified into groups such as "Important Value Customer" and "General Growth Customer".

1. Clone the repository:

   git clone <repository-url>
   cd <project-directory>

2. Install required libraries:

   pip install -r requirements.txt

   Or install manually:

   pip install pandas numpy pyecharts

3. Open and run the Jupyter Notebook:

   jupyter notebook

   Execute the steps in data analysis.ipynb to perform the analysis and customer segmentation.

Using the RFM model, customers are segmented into various groups based on their purchasing behavior. This helps the business in identifying valuable customers and tailoring marketing strategies accordingly.

The dataset used in this analysis is sourced from a Chinese online store, focusing on the cosmetics industry.