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<h1 class="breadcrumbs-title mb-17 sm-mb-10" Enigma</h1>
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<p class="desc2 mb-20"><strong>Enigma</strong> is a full-fledged classical Machine Learning model building contest where competent teams will be challenged to solve problems that require prior knowledge in various data science techniques, including data
engineering, data analytics, and classical machine learning models.<br><br>
<strong>Team Size:</strong> At least one (1) and at most three (3) members.<br><br>
<strong>Prerequisites:</strong><br> • Intermediate Python<br> • Data Science and Data Analysis<br> • Machine Learning<br><br>
<strong>Requirements:</strong><br> • Your own laptop setup (chargers, mouse, keyboards etc.)<br> • Make sure that your IDE for analysis and modelling is installed and up to date.<br><br>
<strong>What will you get to see at the competition?</strong><br> • Three problem statements along with relevant datasets with different levels of complexities:<br> <strong>Level 1: </strong>Basic Data Pre-processing and Modelling
using standard algorithms.
<br> <strong>Level 2: </strong> Data Gathering, Cleaning, Analysing, Visualizing and Modelling using moderately complex classical ML models.<br> <strong>Level 3: </strong> Highly complex, large dataset(s) which is/are
computationally time consuming, and require deeper understanding in the fields of Data Engineering, Data Science and Machine Learning to be solved.<br> • Our team will be around to guide you if at all you are stuck or require help.
<br> • Expect surprises!
<br><br>
<strong>Assessment Criteria:</strong><br> Each team's data handling, visualization, manipulation along with their ability to understand and analyse the data correctly to apply the most suitable algorithms on the given data is put
to test.<br> • Visualizations and Illustrations: “One picture can speak a thousand words”<br> • Insights you have drawn from your analysis: "Insights" are clues or patterns in information to help you understand the data better
and make informed choices.<br> • Conclusive, informed, data-driven decision making at scale.
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