Remove Cross Validation Remove Events Remove Exploratory Data Analysis
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Meet the winners of the Kelp Wanted challenge

DrivenData Labs

We take a gap year to participate in AI competitions and projects, and organize and attend events. At the time of selecting competitions, this was the most attractive in terms of sustainability, image segmentation being a new type of challenge for this team, and having a topic that would be easy to explain and visualize at events.

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New Data Challenge: Aviation Weather Forecasting Using METAR Data

Ocean Protocol

This is a unique opportunity for data people to dive into real-world data and uncover insights that could shape the future of aviation safety, understanding, airline efficiency, and pilots driving planes. When implementing these models, you’ll typically start by preprocessing your time series data (e.g.,

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Data Science Project?—?Build a Decision Tree Model with Healthcare Data

Mlearning.ai

According to the CDC more than 1 million individuals visit emergency departments for adverse drug events each year in the United States. ADRs can range from mild symptoms, such as nausea or dizziness, to more serious or life-threatening events, such as anaphylaxis(severe allergic reaction) or organ damage.

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AI in Time Series Forecasting

Pickl AI

Making Data Stationary: Many forecasting models assume stationarity. If the data is non-stationary, apply transformations like differencing or logarithmic scaling to stabilize its statistical properties. Exploratory Data Analysis (EDA): Conduct EDA to identify trends, seasonal patterns, and correlations within the dataset.

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Showcasing the Power of AI in Investment Management: a Real Estate Case Study

DataRobot Blog

As discussed in the previous article , these challenges may include: Automating the data preprocessing workflow of complex and fragmented data. Monitoring models in production and continuously learning in an automated way, so being prepared for real estate market shifts or unexpected events. Rapid Modeling with DataRobot AutoML.

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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

Clustering: An unsupervised Machine Learning technique that groups similar data points based on their inherent similarities. Cross-Validation: A model evaluation technique that assesses how well a model will generalise to an independent dataset.

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Large Language Models: A Complete Guide

Heartbeat

It is therefore important to carefully plan and execute data preparation tasks to ensure the best possible performance of the machine learning model. 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.