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BENEFITS OF DOCUMENT

High level overview of the data science process with a checklist of items at each step

DOCUMENT DESCRIPTION

Learn about the most common machine learning algorithms in regression, classification, and clustering problems which include linear regression, logistic regression, naive Bayes, support vector machine, and k-means. Then an introduction to the Python code for a classic classification problem, Titanic survival, and an overview through preprocessing, exploratory data analysis, visualization, model architecture, and evaluating performance on test data.

RMDS Lab is a pioneer in developing data science and AI ecosystems, and in offering extensive assistance for data science with its unique RM4Es framework, ResearchMap, and ecosystem approach. We exist to empower our clients worldwide to achieve successful, data driven results. With resources and tools in our robust scientific community, we have the capability to advance your project impact through data impact consultation and AI-powered services that evaluates your needs, deploys the necessary solutions, and maximize the effectiveness.

RMDS expands your company's reach to find new technologies and resources with the goal of reshaping how commercial clients, researchers and data scientists with industry experience work together to develop high impact data science solutions.

We have a long-standing Ecosystem of data scientists, engineers and subject matter experts, eager to be your trusted partner, enhance your research, increase the success rate of your work, and leverage the power of analytics. Each of our consultant teams have experience delivering and commercializing E2E data science solutions and software.

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Source: Turn a Business Problem into a Data Science Solution PDF document

Turn a Business Problem into a Data Science Solution

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File Type: PDF (pdf)

File Size: 6.3 MB

Number of Pages: 15

Related Topic(s): Data Analysis Machine Learning Data Science

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Initial upload date (first version): Apr 24, 2021
Most recent version published: May 5, 2021

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