Remove 2019 Remove EDA Remove ML
article thumbnail

Predicting new and existing product sales in semiconductors using Amazon Forecast

AWS Machine Learning Blog

& AWS Machine Learning Solutions Lab (MLSL) Machine learning (ML) is being used across a wide range of industries to extract actionable insights from data to streamline processes and improve revenue generation. We evaluated the WAPE for all BLs in the auto end market for 2019, 2020, and 2021.

article thumbnail

Unveiling Market Dynamics: Winners of the Google Trends Analysis and Predictive Modeling

Ocean Protocol

The challenge required a detailed analysis of Google Trends data, integration of additional data sources, and the application of advanced ML methods to predict market behaviors. Participants demonstrated outstanding abilities in utilizing ML and data analysis to probe and predict movements within the cryptocurrency market.

EDA 59
professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Heartbeat Newsletter: Volume 32

Heartbeat

ML models use loss functions to help choose the model that is creating the best model fit for a given set of data (actual values are the most like the estimated values). We will carry out some EDA on our dataset, and then we will log the visualizations onto the Comet experimentation website or platform.

article thumbnail

Linear Regression for tech start-up company Cars4U in Python

Mlearning.ai

In 2018–2019, while new car sales were recorded at 3.6 Exploratory Data Analysis (EDA) Univariate EDA Price: The price of a used car is the target variable and has a highly skewed distribution, with a median value of around 53.5 million units, around 4 million second-hand cars were bought and sold.

Python 52
article thumbnail

Meet the winners of the Unsupervised Wisdom Challenge!

DrivenData Labs

Summary of approach : Using a downsampling method with ChatGPT and ML techniques, we obtained a full NEISS dataset across all accidents and age groups from 2013-2022 with six new variables: fall/not fall, prior activity, cause, body position, home location, and facility. Check out zysymu's full write-up and solution in the competition repo.