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Challenge

AI-Powered Foundry Customer Prediction

Develop an AI model to predict the likelihood of a company becoming a customer for Intel's foundry services. Utilize publicly available data such as company financials, technological needs, and market trends to build a predictive model. The goal is to identify potential customers and inform Intel's sales and marketing strategies. This challenge focuses on applying machine learning techniques to real-world business problems in the semiconductor industry.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build an AI model to predict potential customers for Intel's foundry services using publicly available data.

Delivery guide

How work is evaluated

Evaluation

Evaluation will be based on model accuracy, precision, recall, and F1-score, as well as a clear presentation of the model and findings.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Learning goals

What you should walk away with

  • Develop and train a machine learning model to predict customer likelihood.

  • Evaluate the model's performance using appropriate metrics.

  • Present findings and insights in a clear and concise manner.

  • Gather and preprocess relevant data from public sources.

Resources and assets

Reference links and supporting material

Dataset notes

Sample data for 1 tasks

Challenge setup

Evaluation will be based on model accuracy, precision, recall, and F1-score, as well as a clear presentation of the model and findings.

Challenge input

CSV file with company features.

Agent execution

No agent tool is listed for this challenge.

Evaluated output

CSV file with predicted probabilities of becoming a customer.

Evaluation checks
  • No evaluation checks are configured.
Configured evaluation evidence
  • Accuracy target: 0.7
Configured runtime
  • Python execution harness

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

No tool records are attached.

Evaluation contract

  • No evaluation checks are configured.

Metrics

  • Accuracy · Target 0.7
  • Precision · Target 0.7
  • Recall · Target 0.7
  • F1-score · Target 0.7

Workflow tasks

  1. Task 1

    Input: CSV file with company features.

    Output: CSV file with predicted probabilities of becoming a customer.

    Execution harness: Python

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about AI-Powered Foundry Customer Prediction