The most used methods are Regression, Clustering, Visualization, Decision Trees/Rules, and Random Forests; Deep Learning is used by only 20% of respondents; we also analyze which methods are most "industrial" and most "academic". Introduction to Data Science tools Top Data Science Tools Features of Data Science Tools Data Science Tools For Data...

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Introduction to Seaborn in python Different categories of plot in Seaborn Features of Seaborn Seaborn - Environment Setup Seaborn - Importing Datasets and Libraries Seaborn -...

The ability to manage large amounts of potential predictor variables, fine-tuning the model to choose the best predictor variables from the available options. It's faster than other automatic model-selection methods. What is Stepwise Regression? Types of Stepwise Regression Example Stepwise regression analysi How Stepwise Regression...

Skewness is a measure of symmetry, or more precisely, the lack of symmetry. A distribution, or data set, is symmetric if it looks the same to the left and right of the center point. Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution. Introduction to Skewness vs Kurtosis Comparison...

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Introduction What is Data Science? What is...

A confusion matrix is a table that is often used to describe the performance of a classification model (or "classifier") on a set of test data for which the true values are known. Introduction to Confusion Matrix Confusion Matrix of True Positive Confusion Matrix of False Negative Confusion Matrix of False Positive Confusion Matrix of...

Polynomial Regression is a form of Linear regression known as a special case of Multiple linear regression which estimates the relationship as an nth degree polynomial. Polynomial Regression is sensitive to outliers so the presence of one or two outliers can also badly affect the performance. Introduction to polynomial Regression Polynomial...

A web crawler, or spider, is a type of bot that is typically operated by search engines like Google and Bing. Their purpose is to index the content of websites all across the Internet so that those websites can appear in search engine results. Introduction to Web Crawler A Web Crawler overview Features of Web Crawler Web Crawler...

The Pandas module mainly works with the tabular data, whereas the NumPy module works with the numerical data. The Pandas provides some sets of powerful tools like DataFrame and Series that mainly used for analyzing the data, whereas in NumPy module offers a powerful object called Array. Introduction to Pandas Key Features of Pandas Prologue to...

Data wrangling is the process of cleaning and unifying messy and complex data sets for easy access and analysis. Introduction of Data wrangling Importance of Data Wrangling Benefits of Data Wrangling Data Wrangling Tools Data Wrangling Examples Data Wrangling vs. ETL Data Wrangling Skills Required Steps of Data Wrangling...