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What Makes a Successful Machine Learning Engineer? My Story
You’ve been doing machine learning for quite a while, and you’re starting to notice that your workload throughout a project is not constant. There are highs, with tasks that require your full dedication, and lows, where you mostly supervise processes that you’ve previously launched. You can do a (…)
10 Tips to Cuztomize Text Color, Font, Size in ggplot2 with element_text()
ggplot2’s theme system give us a great control over how the “non-data” elements of a plot should look like. The theme system helps elevate the plot you make by making finer changes and make it easy to look better. ggplot2’s theme system comes with multiple element_ functions, element_text() (…)
Improve ML Model Performance by Combining Categorical Features
A simple trick to Improve Model Performance. Photo by Joey Kyber from Pexels When you training a machine learning model, you can have some features in your dataset that represent categorical values. Categorical features are types of data that may be divided into groups. There are three common (…)
How to Send and Receive Automated Emails Using Python
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How to Use Progress Bars in Python?
ArticleVideo Book This article was published as a part of the Data Science Blogathon Hours of hard work would go into vain if our program … The post How to Use Progress Bars in Python? appeared first on Analytics Vidhya .
Machine Learning Life-cycle Explained!
ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction This blog mainly tells the story of the Machine Learning … The post Machine Learning Life-cycle Explained! appeared first on Analytics Vidhya .
Domino – financial forecasting in the age of global pandemic
Accurate forecasting is key for any successful business. It allows one to set realistic financial goals for the next quarters, evaluate impact of business decisions, and prepare adequate resources for what is coming. Yet, many companies struggle with efficient and accurate revenue forecasting. For (…)
Explainer Dashboard, czyli narzędzie do odpowiedzi jak działa model uczenia maszynowego!
– Tato…. jak działa lodówka? – zapytała Jagódka stojąc z siostrą przed otwartą chłodziarką i szukając owoców. – A co już wiesz o lodówce? – zapytałem Jagódkę. – No, wiem, że chłodzi nasze jedzonko i picie. I jeszcze, że niska temperatura chroni nasze jedzenie, żeby się nie zepsuło. – I … Artykuł (…)
Intermediate SQL for Data Science – Analytical Functions Deep Dive
Intermediate SQL for Data Science Running data queries in the database can offer significant speed improvements over doing so in R or Python. There’s no need to drag the entire dataset to memory and run the calculations once the loading completes. The runtime differences can be drastic, depending (…)
Combine the best of MS Excel and Python
You don’t need (and shouldn’t) entirely run away from Excel yet Have you heard that Python is the new Excel? I’ve already read this a couple of times, to be honest. So, why do I see a lot of people still using Excel? First of all, these two tools are intended for different purposes and, second of (…)
Working with Stock Market Time Series Data using Facebook Prophet
ArticleVideo Book This article was published as a part of the Data Science Blogathon Time series data consists of a set of observations in which … The post Working with Stock Market Time Series Data using Facebook Prophet appeared first on Analytics Vidhya .
Project & Task Management With R Shiny
ArticleVideo Book This article was published as a part of the Data Science Blogathon Project management leads to project completion which leads to faster implementation … The post Project & Task Management With R Shiny appeared first on Analytics Vidhya .
Dealing With Missing Values in Python – A Complete Guide
ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Data Cleaning is the process of finding and correcting … The post Dealing With Missing Values in Python – A Complete Guide appeared first on Analytics Vidhya .
Filtering Data in R 10 Tips -tidyverse package
filtering data in r, In this tutorial describes how to filter or extract data frame rows based on certain criteria. In this tutorial, you will learn the filter R functions from the tidyverse package. The main idea is to showcase different ways of filtering from the data set. Filtering data is one (…)
Beautiful Ridge maps with Python
How to quickly draw cool looking elevation maps with Python This visualization is the result of the sweet combination of elevation maps and ridge plots. It uses multiple horizontal lines to represent the elevation of the terrain, like tracing the peak of various latitudes, one above the other. The (…)
API: Flask & FastAPI & HTTP & Postman
From an AI algorithm to a fully functional API How to simply and efficiently expose your AI algorithm as an API Picture by Brett Sayles from Pexels As a data scientist, when working on a complex project along with other developers, you, very often, need to package your AI algorithm into what we (…)
Testing Birthday Paradox in Faker Library (Python)
A famous statistical phenomenon proven programmatically From Unsplash Tomorrow is the 18th of May, which is my birthday 🥰🎈. This inspired me to write an article about a phenomenon of probability theory called the birthday paradox . The essence of this problem is about the probability that in a (…)
Implement ORM Data Models with SQLAlchemy | by Todd Birchard
Handle your application’s data layer with SQLAlchemy’s powerful ORM. Define data models, add/remove records, and execute queries purely in Python. Utilizing ORMs as a data layer is a concept as old as object-oriented programming itself; by abstracting SQL concepts, developers avoid dreaded “context (…)
A Reference Notebook for 30+ Statistical Charts in Seaborn | by Anello | Apr, 2021
Photo by Pia from Pexels A Reference Notebook for (+30) Statistical Charts in Seaborn | LinkedIn The purpose of this tutorial is that we can build graphs to assist in the application of the data science process. We can employ visualizations during exploratory analysis, before or after processing (…)
Python and Parquet performance optimization using Pandas, PySpark, PyArrow, Dask, fastparquet and AWS S3
In Pandas, PyArrow, fastparquet, AWS Data Wrangler, PySpark and Dask This post outlines how to use all common Python libraries to read and write Parquet format while taking advantage of columnar storage , columnar compression and data partitioning . Used together, these three optimizations can (…)
3 Data Processing Pipelines You Can Build With Python Generators | by Patrick Kalkman | May, 2021
Łukasz Prokulski Also publish to my profile There are currently no responses for this story. Be the first to respond. Using generators to create data processing pipelines in Python Photo by maxxyustas , rights obtained via Envato Elements . Ever since I saw David Beazley’s presentation about (…)
Calculating Building Density in R with OSM data
Introduction OpenStreetMaps is a great source of spatial data. Most common programming languages have packages for downloading data from OSM. In this tutorial I will show how to download building data using R’s osmdata package, perform density analysis and plot it using ggplot, and interactively (…)
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