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1. Top results related to data preparation & processing

   • Data Science Data Preparation and Analyze Data

     Before analyzing data, a Data Scientist must extract the data, and make it clean and valuable.

     Extract and Read Data With Pandas

     Before data can be analyzed, it must be imported/extracted.

     In the example below, we show you how to import data using Pandas in Python.

     We use the read_csv() function to import a CSV file with the health data:

     Example

     import pandas as pdhealth_data = pd.read_csv("data.csv", header=0, sep=",")print(health_data)

     Example Explained

     • Import the Pandas library

     Tip: If you have a large CSV file, you can...

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     1/5

   • preparation data set for basket analysis in R

     Top Answer

     Answered Apr 28, 2017 · 0 votes

     Lets call your data set data. Then

     library(data.table)-data <- data.table(basket_id=c(2345, 2345, 7876, 7876, 7876, 7876, 3498 ), item_name=c("coke 0.5" , "salad 300g" , "water" , "diet coke" , "CANCEL diet coke", "sushi" , "coffee" ), item_id=c(98, 103, 88, 95, 95, 143, 23))dt <- as.data.table(data)temp.basket <- dt[item_name%like%"^CANCEL",.(basket_id)]temp.item <- dt[item_name%like%"^CANCEL",.(item_id)]dt2 <- dt[!(basket_id%in%temp.basket &item_id%in%temp.item)]

     The symbol ^ ensures that the item_name starts with the word CANCEL

     Check all answers on Stack Overflow

     2/5

   • data preparation for random forest and predictive modeling

     Top Answer

     Answered Aug 21, 2015 · 3 votes

     • It helps to train with balanced classes (but don't cross validate with them) RF is surprisingly efficient with data, so you won't need all 350k negative samples to train, likely. Choose an equal number of positive examples by sampling with replacement from that pool. Don't forget to leave some positive examples out for validation though.
     • If you are in scikit-learn, use pandas' df.get_dummies() to generate the binary encoding. R does the binary encoding for you for variables that are factors. Behind the scenes it makes a bit vector.
     • I always start with RF because there are so few knobs, it's a good benchmark. After I've straightened out my feature transforms and gotten AUC up, I try the other methods.
     • a) no b) no c) yes d) Yes it needs to be fixed somehow. If you can get away with removing data where any predictor has missing values, great. However if that's not possible, median is a common choice. Let's say a tree is being built, and variable X4 is chosen to split on. RF needs to choose a point on a line and send all the data to either the left or right. What should it do for data where X4 has no value ? Here is the strategy the 'randomForest' package takes in R:

     For numeric variables, NAs are replaced with column medians. For factor variables, NAs are replaced with the most frequent levels (breaking ties at random). If object contains no NAs, it is returned unaltered.

     Check all answers on Stack Overflow

     3/5

   • Data processing - BigQuery vs Data Proc+BigQuery

     Top Answer

     Answered Jan 15, 2020 · 2 votes

     I went successfully with option 1: Big Query is very capable to run the very complex transformation with SQL, on top of that you can also run them incrementally with time range decorators. Note that it takes a lot of time and resources to take data back and forth to BigQuery. When running BigQuery SQL data never leaves BigQuery in the first place and you already have all raw logs there. So as long your problem can be solved by a series of SQL I believe this is the best way to go.

     We moved out Vertica reporting cluster, rewriting successfully ETL last year, with option 1.

     Around a year ago, I've written POC comparing DataFlow and series of BigQuery SQL jobs orchestrated by potens.io workflow allowing SQL parallelization at scale.

     I took a good month to write DataFlow in Java with 200+ data points and complex transformation with terrible debugging capability at a time. And a week to do the same using a series of SQL with potens.io utilizing Cloud Function for Windowed Tables and parallelization with clustering transient tables. I know there's been bunch improvement in CloudDataFlow since then, but at a time the DataFlow did fine only at a million scale and never-completed at billions record input (main reason shuffle cardinality went little under billions of records, with each records having 200+ columns). And the SQL approach produced all required aggregation under 2 hours for a dozen billion. Debugging and easiest of troubleshooting with potens.io helped a lot too.

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     4/5

   • Processing data from React.js form with django

     Top Answer

     Answered Sep 28, 2021 · 1 votes

     With react, we mostly fetch data and show it as HTML, and that data is fetched by a web-based API, which just looks like a URL. so if you want to send data to Django must create an API with Django-rest-framework and it's very easy. You will need to send data to that API and it will add that to your database.

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     5/5

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2. www.techtarget.com › definition › data-preparationWhat is Data Preparation? An In-Depth Guide to Data Prep

   www.techtarget.com › definition › data-preparation

   Data preparation is the process of gathering, combining, structuring and organizing data so it can be used in business intelligence ( BI ), analytics and data visualization applications.

3. aws.amazon.com › what-is › data-preparationWhat is Data Preparation? - Data Preparation Explained - AWS

   aws.amazon.com › what-is › data-preparation

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   Data preparation is the process of preparing raw data so that it is suitable for further processing and analysis. Key steps include collecting, cleaning, and labeling raw data into a form suitable for machine learning (ML) algorithms and then exploring and visualizing the data.

4. www.geeksforgeeks.org › what-is-data-preparationWhat is Data Preparation? - GeeksforGeeks

   www.geeksforgeeks.org › what-is-data-preparation

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   Feb 28, 2024 · Data preparation is the process of making raw data ready for after processing and analysis. The key methods are to collect, clean, and label raw data in a format suitable for machine learning (ML) algorithms, followed by data exploration and visualization.

5. Data Preparation & Processing near Columbus, OH

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6. towardsdatascience.com › the-basics-of-data-prep-7How to Prepare your Data. Structuring, cleaning, and ...

   towardsdatascience.com › the-basics-of-data-prep-7

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   May 9, 2020 · How to Prepare your Data. Structuring, cleaning, and enriching raw data. Diego Lopez Yse. ·. Follow. Published in. Towards Data Science. ·. 16 min read. ·. May 9, 2020. Photo by Anchor Lee on Unsplash. It is rare that you get data in exactly the right form you need it.

7. www.sitepoint.com › data-preprocessingData Preprocessing: Exploring the Keys to Data Preparation

   www.sitepoint.com › data-preprocessing

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   Aug 21, 2023 · Learn what data preprocessing is, why it's important, and techniques for cleaning, transforming, integrating and reducing your data.

8. dataintegrationinfo.com › data-preparation-processData Preparation Process Explained: Steps, Benefits, & Tools

   dataintegrationinfo.com › data-preparation-process

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   Apr 30, 2020 · Data Preparation is a scientific process to extract, cleanse, validate, transform and enriche data. Learn the complete data preparation process in steps.

9. www.alteryx.com › glossary › data-preparationWhat Is Data Preparation? Guide & Templates - Alteryx

   www.alteryx.com › glossary › data-preparation

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   Data preparation, also sometimes called “pre-processing,” is the act of cleaning and consolidating raw data prior to using it for business analysis and machine learning. It might not be the most celebrated of tasks, but careful data preparation is a key component of successful data analytics.