People order breakfast at Bill Smith's Cafe, after Texas Governor Greg Abbott issued a rollback of coronavirus disease (COVID-19) restrictions in McKinney, Texas, March 10, 2021.

Shelby Tauber | Reuters

Since the World Health Organization declared the coronavirus a pandemic one year ago Thursday, new Yelp data showed nearly a half million businesses opened in America during that time, an optimistic sign of the state of the U.S. economic recovery.

Between March 11, 2020 and March 1, 2021, Yelp has seen more than 487,500 new businesses listing on its platform in the United States. That's down just 14% compared with the year-ago period. More than 15% of the new entities were restaurant and food businesses.

The novel coronavirus, first discovered in China, is believed to have surfaced in Wuhan in late 2019, before spreading rapidly around the world, infecting 118 million people and causing 2.6 million deaths, according to data from Johns Hopkins University.

Virus mitigation efforts in nations all over the world, including the U.S., have ranged from full lockdowns to partial closures to reduced capacity of nonessential businesses and services. Masks and social distancing have been a hallmark of the pandemic. The economic damage from the crisis was swift.

However, according to data compiled by Yelp, which has released local economic impact reports all throughout pandemic, more than 260,800 businesses that had closed due to Covid restrictions, reopened from March 11, 2020 until March 1. About 85,000 of them were restaurant and food businesses.

Justin Norman, vice president of data science at Yelp, sees optimism in the numbers.

"As more and more Americans continue to get vaccinated, case counts continue to lower, and Congress' Covid relief bill that offers additional aid is distributed, we anticipate businesses that were once struggling over the last year will bounce back," Norman told CNBC. "We see this evidenced through the 260,000 businesses that have been able to reopen after temporarily closing."

Of the almost half million new businesses that have opened, about 59% were within the "professional, local, home and auto" category on Yelp.

"The number of new business openings particularly the high number of new home, local, professional and auto services businesses also shows great potential for those industries in the future," Norman said.

Yelp said that certain trends borne out of the pandemic may be here to stay. As consumers spend more time at home, Yelp noted an uptick in interest in home improvement. The company saw that average review mentions for home office renovation increased by 75% year over year and bathroom renovations rose by 80%.

"I anticipate that we'll still see people invest in higher-quality home offices or improving their homes," Norman said. "With warmer summer months coming and the number of vaccines being administered continuing to increase, people who aren't planning to return to the office this year may focus on more home improvement projects."

Yelp's new business data also shows the restrictions brought on by the pandemic accelerated the need for businesses to adapt by using technology and changing the ways they interact with their customers.

Of the new business openings, the number of food trucks climbed 12% and food delivery businesses were up 128%."The increase in food delivery services would have easily been predicted, although we may not have predicted they would stay on the rise a year later," Norman said.

He also said he was surprised by how local businesses have incorporated the tools technology offers. "It's been incredibly impressive and encouraging to see how much local businesses, both in large cities and smaller towns, have embraced technology to serve customers during this challenging time."

Yelp also saw changes in the ways companies specifically interacted with its app. In 2020, 1.5 million businesses updated their hours through Yelp, 500,000 indicated that they were offering virtual services, and more than 450,000 businesses crafted a custom message at the top of their page, to speak directly to customers.

In addition to the positive data about food delivery and restaurants, Norman was surprised to see some trends through the year that indicated a change in how consumers engaged with everyday life. Yelp saw that consumer interest in psychics increased 74% year over year and astrologers rose by 63%. Yelp measures consumer interest in page views, posts, or reviews.

"It was also surprising to find that consumer interest in notaries were up 52% on Yelp, as many federal and state rules allowed remote notarization," Norman said. "While Yelp data can't provide an in-depth look into what people were notarizing over the last year, Yelp data does show a trend of couples holding smaller, more intimate weddings, instead of more traditional large wedding celebrations, as well as the housing market seeing an astounding demand coupled with low interest rates and housing prices in certain markets."

A year on, it's clear that the businesses that have survived have had to find new ways to operate, and that many of the changes will be permanent."We've seen more and more businesses embrace app-enabled delivery, software tools like reservations and waitlist and consumer-oriented communications tools like the Covid health and safety measures. The digital local business is here to stay," Norman said.

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Yelp data shows almost half a million new businesses opened during the pandemic - CNBC

Published on February 22, 2021

(Miami, FL - February 22, 2021)-The City of Miami has been named as a participant in Data Science for All / Empowerment (DS4A / Empowerment), a new effort designed to upskill and prepare job-seekers from underserved communities for data science careers. The initiative is being backed by SoftBank Group International (SoftBank) as part of its AI Academy, and was developed by Correlation One. It aims to train at least 10,000 people from underrepresented communities including Greater Miami over the next three years, providing new pathways to economic opportunity in the worlds fastest-growing industries.

We need talent with a deep understanding of data science to build the companies of the future, said Marcelo Claure, CEO of SoftBank Group International. Were proud to support this effort, continue to upskill our portfolio companies and train more than 10,000 people from underrepresented communities with critical technical skills.

SoftBanks AI Academy supports programs that supplement theoretical training of traditional technical education courses with practical lessons, including AI and data skills that can be immediately applied to common business needs.

DS4A / Empowerment will provide training for SoftBank Group International portfolio company employees, including portfolio companies of the Opportunity Fund and Latin America Fund, as well as external candidates from the U.S. and Latin America. The program is specifically designed to address talent and equity gaps in a field that has historically been inaccessible for many workers, leading to significant underrepresentation of women and non-white individuals. Participants work on real-world case studies that are expected to have measurable impact on the operational performance of participating companies.

IDB Lab will join SoftBank by providing over ten full-ride Fellowships to underrepresented candidates in Latin America while the Miami-Dade Beacon Council will provide four full-ride fellowships for underrepresented candidates based in Miami.

In addition, The City of Miami will join as an impact partner by providing twenty fellowships to Miami talent and five fellowships to public sector workers.

"As Miami grows as a tech hub, it is important that we empower local entrepreneurs and the public sector to leverage the power of AI. We are proud to support the building of a diverse data-fluent community in Miami through our partnership with Correlation One and SoftBank, said Francis Suarez, the Mayor of Miami.

Participants in the program will receive 13 weeks of data and analytics training (plus optional Python training) while working on case studies and projects, including projects submitted by SoftBank portfolio companies. The initiative will also connect participants with mentors who will provide professional development and career coaching. At the end of the program, external participants will be connected with employment opportunities at SoftBank and leading enterprises across business, financial services, technology, healthcare, consulting, and consumer sectors.

Miamis success hinges on dramatically expanding opportunity across our community and building a workforce with the skills for the jobs of tomorrow, said Matt Haggman, Executive Vice President of The Beacon Council. This program is an important step towards creating the innovative and equitable future we can - and must - achieve.

Training our residents to take on the jobs of the future is critical to ensuring that economic growth is shared across all communities, and to building our local talent so that more leading companies in fields like tech and data science can put down roots in Miami-Dade, said Daniella Levine-Cava, Miami-Dade County's Mayor. Im thrilled that this program is unlocking opportunities in a field that has historically been inaccessible for so many, and creating new, inclusive pathways to prosperity in one of the worlds fastest-growing industries.

The COVID-19 pandemic has both accelerated demand for data science talent and exacerbated the access gaps that kept so many aspiring workers locked out of opportunity, said Rasheed Sabar and Sham Mustafa, Co-CEOs and Co-Founders of Correlation One. We are grateful to work with innovative employers like SoftBank that are stepping up to play a more direct role in helping the workforce prepare themselves for jobs of the future.

Program and Registration DetailsDS4A Empowerment is an online program delivered in English over a 13-week period. Classes will convene on Saturdays from 10:00am to 8:00pm ET, beginning on April 17, 2021.Registration for the program ends on March 7, 2021. Candidates who should consider applying include employees of SoftBank affiliated portfolio companies in the region as well as software engineers, technical product managers, technical marketers, and anyone with a STEM background who is interested in learning data analysis. To apply and find out more information about the program, interested candidates can visit the official DS4A Empowerment website:https://c1-web.correlation-one.com/ds4a-empowermentFor program related inquiries, please contactds4aempowerment@correlation-one.com.

About SoftBankThe SoftBank Group invests in breakthrough technology to improve the quality of life for people around the world. The SoftBank Group is comprised of SoftBank Group Corp. (TOKYO: 9984), an investment holding company that includes telecommunications, internet services, AI, smart robotics, IoT and clean energy technology providers; the SoftBank Vision Funds, which are investing up to $100 billion to help extraordinary entrepreneurs transform industries and shape new ones; and the SoftBank Latin America Fund, the largest venture fund in the region. To learn more, please visithttps://global.softbankAbout Correlation OneCorrelation One is on a mission to build the most equitable vocational school of the future. We believe that data literacy is the most important skill for the future of work. We make data fluency a competitive edge for firms through global data science competitions, rigorous data skills assessments, and enterprise-focused data science training.

Correlation One's solutions are used by some of the most elite employers all around the world in finance, technology, healthcare, insurance, consulting and governmental agencies. Since launching in 2015, Correlation One has built an expert community of 250,000+ data scientists and 600+ partnerships with leading universities and data science organizations in the US, UK, Canada, China, and Latin America.

https://www.correlation-one.com/about

Media Contacts:

For SoftBank:Laura Gaviria HalabyLaura.gaviria@softbank.com

City for Miami Media:Stephanie Severinosseverino@miamigov.com

For Beacon Council:Maria Budetmbudet@beaconcouncil.com

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Participating in SoftBank/ Correlation One Initiative - Miami - City of Miami

February 22, 2021

A new multidisciplinary collaboration between the University of Rochesters departments of biology, biomedical engineering, and optics and the Goergen Institute for Data Science will establish an innovative microscopy resource on campus, allowing for cutting-edge scientific research in biological imaging.

Michael Welte, professor and chair of the Department of Biology, is the lead principal investigator of the project, which was awarded a $1.2 million grant from the Arnold and Mabel Beckman Foundation.

The grant supports an endeavor at the intersection of optics, data science, and biomedical research, and the University of Rochester is very strong in these areas, Welte says. The University has a highly collaborative culture, and the close proximity of our college and medical center makes Rochester ideally suited to lead advances in biological imaging.

The project will include developing and building a novel light-sheet microscope that employs freeform optical designs devised at Rochester. The microscope, which will be housed in a shared imaging facility in Goergen Hall and is expected to be online in 2022, enables three-dimensional imaging of complex cellular structures in living samples. Researchers and engineers will continually improve the microscope, and it will eventually become a resource for the entire campus research community.

The optical engineers working on this project will take light-sheet technology into new domains, says Scott Carney, professor of optics and director of Rochesters Institute of Optics, who is a co-principal investigator on the project. They will transform a precise, high-end microscope into a workhorse for biologists working at the cutting edge of their disciplines to make discoveries about the very fabric of life at the cellular and subcellular level.

The microscope will produce large amounts of data that will require new methods to better collect, analyze, and store the images.

These efforts will focus on developing algorithms for computational optical imaging and automated biological image analysis, as well as on big data management, says Mujdat Cetin, a professor of electrical and computer engineering and the Robin and Tim Wentworth Director of the Goergen Institute for Data Science. Cetin is also a co-principal investigator on the project.

While many other research microscopes illuminate objects pixel by pixel, light-sheet technology illuminates an entire plane at once. The result is faster imaging with less damage to samples, enabling researchers to study biological processes in ways previously out of reach.

In addition to funding the construction of the microscope and development of the data science component, the grant from the Arnold and Mabel Beckman Foundation supports three biological research projects:

Not only am I excited about each of the individual projectsfrom intimate looks at bacteria to finding new ways to analyze imagesI am absolutely thrilled about the prospect of building something even bigger and better via the close collaboration of disciplines Rochester excels at individually: optics, data science, and biomedical research, Welte says. I believe this joint endeavor is only the first in a long line that will establish Rochester as a leader in biological imaging.

Tags: Anne S. Meyer, Arts and Sciences, Dan Bergstralh, Department of Biology, Goergen Institute for Data Science, grant, Hajim School of Engineering and Applied Sciences, James McGrath, Michael Welte, Mujdat Cetin, Richard Waugh, Scott Carney

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Rochester to advance research in biological imaging through new grant - University of Rochester

UNIVERSITY PARK, Pa. Data science can be a useful tool and powerful ally in enhancing diversity. A group of data scientists are holding Harnessing the Data Revolution to Enhance Diversity, a symposium aimed at discussing the issues, identifying opportunities and initiating the next steps toward improving equity and diversity in academia at the undergraduate and faculty levels.

The online event, organized by the Institute for Computational and Data Sciences and co-sponsored by the Office of the Vice Provost for Educational Equity and the Center for Social Data Analytics, will be held from 1 to 3:30 p.m. on March 16 and 17, and is scheduled to include 10 30-minute talks and a roundtable discussion. Organizers added that the event is designed to help form new collaborations and identify cutting-edge approaches that can enhance diversity at Penn State and in higher education across the country.

This symposium will bring together researchers from across the computational and social sciences to explore how we can build more diverse communities of researchers that are sensitive to how computational and data science can shape how diverse populations are impacted by change, said Jenni Evans, professor ofmeteorology and atmospheric scienceand ICDS director.

Speakers from across the U.S. will discuss issues ranging from quantifying and contextualizing diversity-related issues to examining approaches that have and havent worked in academia.

Ed O'Brien, associate professor of chemistry and ICDS co-hire, said data science offers several tools to promote diversity, equity and inclusion.

This symposium is bringing together diverse academic communities to explore how data science can be utilized to enhance diversity, equity and inclusion, said OBrien. Leveraging advances in big data and artificial intelligence holds the promise of complementing and accelerating a range of initiatives in this area.

Some of the topics the speakers and participants will address include how to identify a diversity-related goal, how to quantify and contextualize the challenge of increasing diversity, and analyzing approaches that have and have not worked.

Find out more and register for the symposium at https://icds.psu.edu/diversity.

Last Updated February 17, 2021

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Symposium aimed at leveraging the power of data science for promoting diversity - Penn State News

Scikit-learn is a powerful machine learning library that provides a wide variety of modules for data access, data preparation and statistical model building. It has a good selection of clean toy data sets that are great for people just getting started with data analysis and machine learning. Even better, easy access to these data sets removes the hassle of searching for and downloading files from an external data source. The library also enables data processing tasks such as imputation, data standardization and data normalization. These tasks can often lead to significant improvements in model performance.

Scikit-learn also provides a variety of packages for building linear models, tree-based models, clustering models and much more. It featuresan easy-to-use interface for each model object type, which facilitates fast prototyping and experimentation with models. Beginners in machine learning will also find the library useful since each model object is equipped with default parameters that provide baseline performance. Overall, Scikit-learn provides many easy-to-use modules and methods for accessing and processing data and building machine learning models in Python. This tutorial will serve as an introduction to some of its functions.

Scikit-learn is a powerful machine learning library that provides a wide variety of modules for data access, data preparation and statistical model building.Scikit-learn also provides a variety of packages for building linear models, tree-based models, clustering models and much more. It featuresan easy-to-use interface for each model object type, which facilitates fast prototyping and experimentation with models.

Scikit-learn provides a wide variety of toy data sets, whichare simple, clean, sometimes fictitiousdata sets that can be used for exploratory data analysis and building simple prediction models. The ones available in Scikit-learn can be applied to supervised learning tasks such as regression and classification.

For example, it has a set called iris data, which contains information corresponding to different types of iris plants. Users can employ this data for building, training and testing classification models that can classifytypes of iris plants based on their characteristics.

Scikit-learn also has a Boston housing data set, which contains information on housing prices in Boston. This data is useful for regression tasks like predicting the dollar value of a house. Finally, the handwritten digits data set is an image data set that is great for building image classification models. All of these data sets are easy to load using a few simple lines of Python code.

To start, lets walk through loading the iris data. We first need to import the pandas and numpy packages:

Next, we relax the display limits on the columns and rows:

We then load the iris data from Scikit-learn and store it in a pandas data frame:

Finally, we print the first five rows of data using the head() method:

We can repeat this process for the Boston housing data set. To do so, lets wrap our existing code in a function that takes a Scikit-learn data set as input:

We can call this function with the iris data and get the same output as before:

Now that we see that our function works, lets import the Boston housing data and call our function with the data:

Finally, lets load the handwritten digits data set, which contains images of handwritten digits from zero through nine. Since this is an image data set, its neithernecessary nor useful to store it in a data frame. Instead, we can display the first five digits in the data using the visualization library matplotlib:

And if we call our function with load_digits(), we get the following displayed images:

I cant overstate the ease with which a beginner in the field can access these toy data sets. These sets allow beginners to quickly get their feet wet with different types of data and use cases such as regression, classification and image recognition.

Scikit-learn also provides a variety of methods for data processing tasks. First, lets take a look at data imputation, which is the process of replacing missing data and is important because oftentimes real data contains either inaccurate or missing elements. This can result in misleading results and poor model performance.

Being able to accurately impute missing values is a skill that both data scientists and industry domain experts should have in their toolbox. To demonstrate how to perform data imputation using Scikit-learn, well work with the University of California, Irvines data set on housing electric power consumption, which is available here. Since the data set is quite large, well take a random sample of 40,000 records for simplicity and store the down-sampled data in a separate csv file called hpc.csv:

As we can see, the third row (second index) contains missing values specified by ? and NaN. The first thing we can do is replace the ? values with NaN values. Lets demonstrate this with Global_active_power:

We can repeat this process for the rest of the columns:

Now, to impute the missing values, we import the SimpleImputer method from Scikit-learn. We will define an imputer object that simply imputes the mean for missing values:

And we can fit our imputer to our columns with missing values:

Store the result in a data frame:

Add back the additional date and time columns:

And print the first five rows of our new data frame:

As we can see, the missing values have been replaced.

Although Scikit-learns SimpleImputer isnt the most sophisticated imputation method, it removes much of the hassle around building a custom imputer. This simplicity is useful for beginners who are dealing with missing data for the first time. Further, it serves as a good demonstration of how imputation works. By introducing the process, it can motivate more sophisticated extensions of this type of imputation such as using a statistical model to replace missing values.

Data standardization and normalization are also easy with Scikit-learn. Both of these are useful in machine learning methods that involve calculating a distance metric like K-nearest neighbors and support vector machines. Theyre also useful in cases where we can assume the data are normally distributed and for interpreting coefficients in linear models to be of variable importance.

Standardization is the process of subtracting values in numerical columns by the mean and scaling to unit variance (through dividing by the standard deviation). Standardization is necessary in cases where a wide range of numerical values may artificially dominate prediction outcomes.

Lets consider standardizing the Global_intensity in the power consumption data set. This column has values ranging from 0.2 to 36. First, lets import the StandardScalar() method from Scikit-learn:

Data normalization scales a numerical column such that its values are between 0 and 1. Normalizing data using Scikit-learn follows similar logic to standardization. Lets apply the normalizer method to the Sub_metering_2 column:

Now we see that the min and max are 1.0 and 0.

In general, you should standardize data if you can safely assume its normally distributed. Conversely, if you can safely assume that your data isnt normally distributed, then normalization is a good method for scaling it. Given that these transformations can be applied to numerical data with just a few lines of code, the StandardScaler() and Normalizer() methods are great options for beginners dealing with data fields that have widely varying values or data that isnt normally distributed.

Scikit-learn also has methods for building a wide array of statistical models, including linear regression, logistic regression and random forests. Linear regression is used for regression tasks. Specifically, it works for the prediction of continuous output like housing price, for example. Logistic regression is used for classification tasks in which the model predicts binary output or multiclass like predicting iris plant type based on characteristics. Random forests can be used for both regression and classification. Well walk through how to implement each of these models using the Scikit-learn machine learning library in Python.

Linear regression is a statistical modeling approach in which a linear function represents the relationship between input variables and a scalar response variable. To demonstrate its implementation in Python, lets consider the Boston housing data set. We can build a linear regression model that uses age as an input for predicting the housing value. To start, lets define our input and output variables:

Next, lets split our data for training and testing:

Now lets import the linear regression module from Scikit-learn:

Finally, lets train, test and evaluate the performance of our model using R^2 and RMSE:

Since we use one variable to predict a response, this is a simple linear regression. But we can also use more than one variable in a multiple linear regression. Lets build a linear regression model with age (AGE), average number of rooms (RM), and pupil-to-teacher ratio (PTRATION). All we need to do is redefine X (input) as follows:

This gives the following improvement in performance:

Linear regression is a great method to use if youre confident that there is a linear relationship between input and output. Its also useful as a benchmark against more sophisticated methods like random forests and support vector machines.

Logistic regression is a simple classification model that predicts binary or even multiclass output. The logic for training and testing is similar to linear regression.

Lets consider the iris data for our Python implementation of a logistic regression model. Well use sepal length (cm), sepal width (cm), petal length (cm) and petal width (cm) to predict the type of iris plant:

We can evaluate and visualize the model performance using a confusion matrix:

We see that the model correctly captures all of the true positives across the three iris plant classes. Similar to linear regression, logistic regression depends on a linear sum of inputs used to predict each class. As such, logistic regression models are referred to as generalized linear models. Given that logistic regression models a linear relationship between input and output, theyre best employed when you know that there is a linear relationship between input and class membership.

Random forests, also called random decision trees, is a statistical model for both classification and regression tasks. Random forests are basically a set of questions and answers about the data organized in a tree-like structure.

These questions split the data into subgroups so that the data in each successive subgroup are most similar to each other. For example, say wed like to predict whether or not a borrower will default on a loan. A question that we can ask using historical lending data is whether or not the customers credit score is below 700. The data that falls into the yes bucket will have more customers who default than the data that falls into the no bucket.

Within the yes bucket, we can further ask if the borrowers income is below $30,000. Presumably, the yes bucket here will have an even greater percentage of customers who default. Decision trees continue asking statistical questions about the data until achieving maximal separation between the data corresponding to those who default and those who dont.

Random forests extend decision trees by constructing a multitude of them. In each of these trees, we ask statistical questions on random chunks and different features of the data. For example, one tree may ask about age and credit score on a fraction of the train data. Another may ask about income and gender on a separate fraction of the training data, and so forth. Random forest then performs consensus voting across these decision trees and uses the majority vote for the final prediction.

Implementing a random forests model for both regression and classification is straightforward and very similar to the steps we went through for linear regression and logistic regression. Lets consider the regression task of predicting housing prices using the Boston housing data. All we need to do is import the random forest regressor module, initiate the regressor object, fit, test and evaluate our model:

We see a slight improvement in performance compared to linear regression.

The random forest object takes several parameters that can be modified to improve performance. The three Ill point out here are n_estimators, max_depth and random_state. You can check out the documentation for a full description of all random forest parameters.

The parameter n_estimators is simply the number of decision trees that the random forest is made up of. Max_depth measures the longest path from the first question to a question at the base of the tree. Random_state is how the algorithm randomly chooses chunks of the data for question-asking.

Since we didnt specify any values for these parameters, the random forest module automatically selects a default value for each parameter. The default value for n_estimators is 10, which corresponds to 10 decision trees. The default value for max_depth is None, which means there is no cut-off for the length of the path from the first question to the last question at the base of the decision tree. This can be roughly understood as the limit on the number of questions we ask about the data. The default value for random_state is None. This means, upon each model run, different chunks of data will be randomly selected and used to construct the decision trees in the random forests. This will result in slight variations in output and performance.

Despite using default values, we achieve pretty good performance. This accuracy demonstrates the power of random forests and the ease with which the data science beginner can implement an accurate random forest model.

Lets see how to specify n_estimators, max_depth and random_state. Well choose 100 estimators, a max depth of 10 and a random state of 42:

We see that we get a slight improvement in both MSE and R^2. Further, specifying random_state makes our results reproducible since it ensures the same random chunks of data are used to construct the decision trees.

Applying random forest models to classification tasks is very straightforward. Lets do this for the iris classification task:

And the corresponding confusion matrix is just as accurate:

Random forests are a great choice for building a statistical model since they can be applied to a wide range of prediction use cases. This includes classification, regression and even unsupervised clustering tasks. Its a fantastic tool that every data scientist should have in their back pocket. In the context of Scikit-learn, theyre extremely easy to implement and modify for improvements in performance. This enables fast prototyping and experimentation of models, which leads to accurate results faster.

Finally, all the code in this post is available on GitHub.

Overall, Scikit-learn provides many easy-to-use tools for accessing benchmark data, performing data processing, and training, testing and evaluating machine learning models. All of these tasks require relatively few lines of code, making the barrier to entry for beginners in data science and machine learning research quite low. Users can quickly access toy data sets and familiarize themselves with different machine learning use cases (classification, regression, clustering) without the hassle of finding a data source, downloading and then cleaning the data. Upon becoming familiar with different use cases, the user can then easily port over what theyve learned to more real-life applications.

Further, new data scientists unfamiliar with data imputation can quickly pick up how to use the SimpleImputer package in Scikit-learn and implement some standard methods for replacing missing or bad values in data. This can serve as the foundation for learning more advanced methods of data imputation, such as using a statistical model for predicting missing values. Additionally, the standard scaler and normalizer methods make data preparation for advanced models like neural networks and support vector machines very straightforward. This is often necessary in order to achieve satisfactory performance with more complicated models like support vector machines and neural networks.

Finally, Scikit-learn makesbuilding a wide variety of machine learning models very easy. Although Ive only covered three in this post, the logic for building other widely used models such as support vector machines and K-nearest neighbors is very similar. It is also very suitable for beginners who have limited knowledge of how these algorithms work under the hood, given that each model object comes with default parameters that give baseline performance. Whether the task is model benching marking with toy data, preparing/cleaning data, or evaluating model performance Scikit-learn is a fantastic tool for building machine learning models for a wide variety of use cases.

Jump Into Machine LearningThe Top 10 Machine Learning Algorithms Every Beginner Should Know

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A Comprehensive Guide to Scikit-Learn - Built In