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AmExpert ML Challenge Data
Finance & Banking Analytics
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About
This product encapsulates a challenge presented during a prestigious machine learning hackathon hosted by a globally integrated payments company. It offers participants and users an authentic glimpse into the types of analytical challenges encountered daily within the industry. The data is designed to facilitate the showcasing of analytical abilities and creative problem-solving skills, focusing on themes relevant to finance, banking, and customer interaction.
Columns
The dataset includes essential identifier and holding information, detailed as follows:
- Customer_ID: A unique identifier assigned to each customer. There are 20,327 unique values in the sample, all of which are valid.
- Product_Holding_B2: Represents a unique product holding identifier. In the sample provided, this field contains only one unique value ('P00').
Distribution
The data structure is well-defined and suitable for use in analytical models. The sample file contains 2 columns and features 20,327 unique records, as every Customer_ID is unique and validated. There are no missing or mismatched values in the sample provided. The expected update frequency for this specific dataset is listed as 'Never'.
Usage
This data is ideally suited for:
- Developing and testing machine learning models.
- Practicing data analysis techniques related to financial services, investing, and e-commerce.
- Building recommender systems, drawing on the context of customer relationships and product holdings.
- Evaluating candidates for potential analytical and machine learning roles.
Coverage
The underlying challenge is associated with a company recognized for its exceptional heritage and cutting-edge information management capabilities, particularly its unique Closed-Loop network and expertise in customer loyalty. While the company operates globally, the competition rules associated with this dataset specified eligibility and prize distribution restricted to Indian citizens who are residents, suggesting a potential focus relevant to that operational area or market.
License
CC0: Public Domain
Who Can Use It
The data is openly available and relevant for:
- Data Scientists seeking real-world machine learning challenges.
- Analysts and Statisticians looking to hone their skills.
- Students interested in gaining exposure to industry-relevant financial and e-commerce data problems.
- Data enthusiasts across various backgrounds.
Dataset Name Suggestions
- AmExpert ML Challenge Data
- Financial Product Holding Analysis
- Customer Analytical Hackathon Set
- Banking & E-Commerce Services Data
Attributes
Original Data Source: AmExpert ML Challenge Data
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