Remove 2018 Remove EDA Remove Exploratory Data Analysis
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Building an End-to-End Machine Learning Project to Reduce Delays in Aggressive Cancer Care.

Towards AI

This article seeks to also explain fundamental topics in data science such as EDA automation, pipelines, ROC-AUC curve (how results will be evaluated), and Principal Component Analysis in a simple way. The dataset originated from Health Verity, one of the largest healthcare data ecosystems in the US. Figure 5: Code Magic!

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Predicting new and existing product sales in semiconductors using Amazon Forecast

AWS Machine Learning Blog

We observed during the exploratory data analysis (EDA) that as we move from micro-level sales (product level) to macro-level sales (BL level), missing values become less significant. We calculated the WAPE value of a model by splitting the data into test and validation sets.

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Linear Regression for tech start-up company Cars4U in Python

Mlearning.ai

In 2018–2019, while new car sales were recorded at 3.6 As a data scientist at Cars4U, I had to come up with a pricing model that can effectively predict the price of used cars and can help the business in devising profitable strategies using differential pricing. million units, around 4 million second-hand cars were bought and sold.

Python 52
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Multivariate Time Series Forecasting

Mlearning.ai

The Art of Forecasting in the Retail Industry Part I : Exploratory Data Analysis & Time Series Analysis In this article, I will conduct exploratory data analysis and time series analysis using a dataset consisting of product sales in different categories from a store in the US between 2015 and 2018.