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Articles by Gourav Bais
5 articles
Top 7 Machine Learning Hackathons that You can Consider
Machine learning hackathons provide an excellent opportunity for data scientists and ML engineers to showcase their skills, learn new techniques, and compete for exciting prizes. These events focus on solving real-world problems using cutting-edge algorithms and data processing techniques. This article covers the top 7 machine learning hackathons and platforms that organize competitive ML events worldwide ? 1. Kaggle Kaggle is the world's largest platform for the machine learning community, with over 10 million registered users ranging from beginners to experts. Acquired by Google, it provides comprehensive training and hands-on challenges in data science. ...
Read MoreHow to Perform Grubbs Test in Python
The Grubbs test is a statistical hypothesis testing method to detect outliers in a dataset. Outliers are observations that disturb the data distribution and can cause models to overfit. This article explains what the Grubbs test is and demonstrates how to implement it in Python using both built-in libraries and manual formula implementation. What are Outliers? Outliers are data points that are numerically distant from other observations in the dataset. For normally distributed data, approximately 68% of records should fall within one standard deviation, 95% within two standard deviations, and 99.7% within three standard deviations of the mean. ...
Read MoreHow to Perform an F-Test in Python
Statisticians use F-test to check whether the two datasets have the same variance or not. F-test is named after Sir Ronald Fisher. To use the F-Test, we make two hypotheses: a null hypothesis and one alternate hypothesis. Then we select any of these two hypotheses based on the F-Test results. Variance is a data distribution metric that measures data deviation from the mean. Higher values show more dispersion than smaller values. In this article, you will learn how to perform an F-Test in Python programming language with its use cases. F-Test Process The process to perform ...
Read MoreHow to Perform a Chi-Square Goodness of Fit Test in Python
Data Scientists often use statistical methods for hypothesis testing to gain insights from datasets. The Chi-Square Goodness of Fit Test is a statistical method that validates whether observed categorical data follows an expected distribution. It helps determine if sample data significantly differs from what we would expect under a specific hypothesis. Understanding Chi-Square Goodness of Fit Test The Chi-Square Goodness of Fit test compares observed frequencies with expected frequencies to determine if there is a significant difference. This test makes several important assumptions ? Variables are independent Only one categorical feature is present Each category must ...
Read MoreHow to Perform a Brown ñ Forsythe Test in Python
The Brown-Forsythe test is a statistical test used to determine whether the variances of two or more groups are equal. While Levene's test uses the absolute deviations from the mean, the Brown-Forsythe test uses the deviations from the median, making it more robust to outliers and non-normal data. Hypothesis The Brown-Forsythe test evaluates the following hypotheses − H0 (Null Hypothesis): The variances of all groups are equal H1 (Alternative Hypothesis): At least one group has a different variance How the Test Works The test calculates each group's median and determines the absolute deviations ...
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