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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. One important stage of any data analysis/science project is EDA. Figure 2: A quick look at the data. 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. For example, in the 2019 WAPE value, we trained our model using sales data between 2011–2018 and predicted sales values for the next 12 months (2019 sale).

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Heartbeat Newsletter: Volume 32

Heartbeat

RoBERTa: A Modified BERT Model for NLP — by Khushboo Kumari An open-source machine learning model called BERT was developed by Google in 2018 for NLP, but this model had some limitations, and due to this, a modified BERT model called RoBERTa (Robustly Optimized BERT Pre-Training Approach) was developed by the team at Facebook in the year 2019.

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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 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.

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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.