A brief introduction to constructing a custom machine learning pipeline What is Machine Learning Pipeline? ML pipeline is a technique to construct end-to-end workflow such as feature cleaning, encoding, extraction, selection, etc. and helps to increase the reusability of your code by decreasing all ml steps into one model. Furthermore, we can avoid some mistakes…

This competition was about identifying bird species by sound to protect endangered Hawaiian birds. What was the objective? Hawaii has lost some bird species, and the consequences could harm entire food chains. For this reason, a Kaggle competition was hosted. …

Deepnote is a unique Jupiter-like web-based notebook environment that supports multi-user development. What is Deepnote? Deepnote is a unique Jupiter-like web-based notebook that helps us with data science projects. It allows multi-user development for data science teams and can publish your work as a link. You can get more information from this link. Why do I use Deepnote? Working With the Same Python Environment

Part 2, Xgboost, CatBoost, and Lightgbm with Optuna… — Modeling is one of the most important parts of predictions. You should find the best machine learning model for better results. In part 1, we worked on EDA and feature engineering. You can see this article here. In this part of the article, we compared three ml models that are…

Random, Machine Learning, Automated ML Baseline Models and Benchmarking For ML… We can train a machine learning model with any prepared data but how can we be sure about the machine learning model learned from train data? The objective of this article is to explain baseline models in data science. …

This is part 1 of the TPS-Mar21 competition that I am in LB %14. Hi dear readers 👋, I am telling my work about the TPS-Mar21 competition in this article. Tabular Playground Series is a Kaggle competition series that only include tabular data. …

Most of the python libraries are already written for data science but newbies working in data science and machine learning were not familiar with these libraries. In this article, I explain 5 different python libraries that make their job easier. Some of the libraries that I am going to share…

Best Data Science Tools, Methods, and Techniques such as Cloud Computing Product, Automated ML Tools, Courses, IDEs, Data Product, Data Analysis Tools, Business Intelligence Tools, Hardware, Deployments, Machine Learning(Computer Vision, Natural language processing) Models, Visualization Libraries, Programming Language and Media Sources For Artificial Intelligence. Happy New Year Dear Readers 🎉…

This article is about important talents, tools, features of the country, and features of the company for high income in data science. — Data science is a topic that is getting more and more popular day by day. With this increasing popularity, the difference between the income of data scientists is getting bigger. So what are the sources of this difference? Now, we are going to look closer at these sources.