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Predicting the Protein Structure Resolution Using Decision Tree

Mlearning.ai

Exploratory Data Analysis(EDA)on Biological Data: A Hands-On Guide Unraveling the Structural Data of Proteins, Part II — Exploratory Data Analysis Photo from Pexels In a previous post, I covered the background of this protein structure resolution data set, including an explanation of key data terminology and details on how to acquire the data.

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Decoding METAR Data: Insights from the Ocean Protocol Data Challenge

Ocean Protocol

METAR, Miami International Airport (KMIA) on March 9, 2024, at 15:00 UTC In the recently concluded data challenge hosted on Desights.ai , participants used exploratory data analysis (EDA) and advanced artificial intelligence (AI) techniques to enhance aviation weather forecasting accuracy.

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Consolidated Kaggle datasets for learning data science

Mlearning.ai

Explore the data (EDA) and spot patterns and missing values. Split the dataset and apply simple machine learning models (like logistic regression or decision trees) to predict survival rates. Visualize word frequencies, distributions, and other cool stuff in the text (EDA). Evaluate your model and tweak it.

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Enhancing Customer Churn Prediction with Continuous Experiment Tracking

Heartbeat

In this article, we take a deep dive into a machine learning project aimed at predicting customer churn and explore how Comet ML, a powerful machine learning experiment tracking platform, plays a key role in increasing project success. ?I Our project uses Comet ML to: 1. The entire code can be found on both GitHub and Kaggle.

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Feature Engineering in Machine Learning

Pickl AI

The growing application of Machine Learning also draws interest towards its subsets that add power to ML models. Key takeaways Feature engineering transforms raw data for ML, enhancing model performance and significance. EDA, imputation, encoding, scaling, extraction, outlier handling, and cross-validation ensure robust models.

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Large Language Models: A Complete Guide

Heartbeat

It is also essential to evaluate the quality of the dataset by conducting exploratory data analysis (EDA), which involves analyzing the dataset’s distribution, frequency, and diversity of text. The ML process is cyclical — find a workflow that matches. Check out our expert solutions for overcoming common ML team problems.