About the challenge

 In the competition, participants are challenged to develop a lead-scoring algorithm that can accurately predict the likelihood of a customer making a purchase on an e-commerce platform based on accuracy and recall value.

Recall value is an essential metric in lead scoring, as it measures the algorithm's ability to correctly identify all positive cases (i.e., customers who are likely to make a purchase) out of all actual positive cases in the dataset. In other words, it measures how well the algorithm is able to capture all potential customers who are likely to make a purchase.

Getting started 

 Here are few Suggestions for the participants so to smoothen their journey through Make-o-thon 

1. Understand the problem: The problem is to develop an algorithm that can accurately predict the likelihood of a customer making a purchase on an e-commerce platform based on both accuracy and recall value.

2. Gather and preprocess the data: Growth Jockey will provide you with a dataset containing information about various customers. The dataset may contain various features such as sale date , subscribed to newsletter etc.  Preprocessing the data involves cleaning the data, removing any irrelevant features, and normalizing the data to make it more suitable for analysis.

3. Develop and test your model: Once you have preprocessed the data, you can start developing your lead-scoring algorithm. You can use various machine learning techniques such as logistic regression, decision trees, or random forests to develop your model. Once you have developed your model, you should test it using cross-validation techniques to evaluate its accuracy and recall value.

4. Submit your entry: After testing your model, you can submit your entry on the platform. The entry will be a .pynb file which further will be tested on different dataset values with the same parameters. They will evaluate your entry based on both accuracy and recall value, and the entry with the best overall performance will be declared the winner

• Recall Value
  
• Precision