Data science and Data preparation with KNIME

Switch between the classic and modern KNIME interfaces in version five. Use the information menu to switch to classic, or select the modern UI to revert.

Learn how to read multiple CSV files in bulk into KNIME by using the CSV reader with folder input, filtering by extension and name, and concatenating results for data preparation.

Learn to read multiple csv files in bulk in KNIME using loops, with list files and folders, a path variable, and a csv reader.

Learn to read multiple Excel files in KNIME by folder, selecting by name or by position, and using nested loops to read all sheets from all files.

Learn to use KNIME's lag node for time series analysis, importing stock price history from csv, and creating rolling lags with copy and multiple lag options.

Explore loops in KNIME, including counting loop, loop start, and loop end, to process a dataset and append a constant value column with the current iteration.

Explore loops in KNIME analytics platform, copy and paste workflow parts, and configure a generic loop with ports, using current iteration and a variable condition as the end criteria.

Explore splitting a dataset into multiple csv outputs by type using a KNIME group loop, dynamic file paths with create files and folder, and a csv writer.

Explore how KNIME uses a recursive loop to replace abbreviations with full names via a lookup table, handling Excel data with column splitter and string manipulation.

Learn to web scrape with KNIME by issuing a get request, transforming JSON to a table, and using json path with ungroup to extract IDs, titles, and bodies.

Extract daily euro exchange rates from the ECB using KNIME, converting XML to JSON or parsing XML with XPath, then ungroup to obtain currency and rate data.

Learn how to extend Knime with Python scripting, install Python extensions, run a Python script node, convert pandas data frames to Knime tables, and output data via Knime io.

Learn to use the Python script node in KNIME to import data, convert input to pandas, perform transformations like interpolation and column filtering, and return results for the workflow.

Learn hyperparameter tuning in KNIME by building a Titanic data preparation workflow, applying one-hot encoding, column filtering, and stratified data splitting while handling missing values and preventing data leakage.

Master hyperparameter optimization in KNIME by tuning a random forest model's number of trees and depth with an optimization loop, improving accuracy on training and test data.

Explore feature selection in knime with a binary heart disease dataset, using a loop, data normalization, and logistic regression to identify best features and evaluate with roc curve.

Explore multiple approaches to data science and data preparation with KNIME, learn concepts and ways to find solutions, and embrace experimentation to discover what works best for you.

Bulk read 12 CSV files into KNIME using the new CSV reader files and folders mode, or loop with list files and folders to concatenate into a single table.

Learn to read multiple Excel files with multiple sheets into KNIME using loops, list files and folders, and sheet name columns.

Explore time series analysis in KNIME using the lag column node to create lagged features, such as prior days' adjusted close and volume, enabling stock data prep.

Explore four loop techniques in KNIME, including counting loops, loop start and end, row filter, and using a constant value column to duplicate a data entry across multiple iterations.

Demonstrates KNIME generic loop versus counting loop to replicate the Bulbasaur entry 0–99 times, using current iteration for a replicate column and a variable end condition.

Stay updated on KNIME: depending on version, the two input and output ports for the recursive loop may be unavailable; drag the node, edit recursion ports, and add ports.

Master recursive loops in KNIME to replace abbreviations with real names using a lookup table, column and row splitters, and string manipulation within data preparation workflows.

Learn web scraping with KNIME by retrieving JSON data via a GET request and structuring it with JSON path and ungroup for scalable extraction.

Extract financial data with KNIME by web scraping ECB exchange rates, converting XML to JSON, and unpivoting currencies to create structured exchange rate tables.

Learn to script in KNIME with python, r, and java nodes, using python source and snippet nodes, configure environments in preferences, and run code within a no-code workflow.

Learn to use Python scripting in Knime to read data, fill missing values with linear interpolation, and perform in-place transformations on pandas dataframes, returning results through output table one.

Explore KNIME loops for hyperparameter optimization and grid search, and prepare Titanic data with one-to-many encoding, column filtering, and stratified train-test splits.

Optimize a KNIME random forest by tuning levels and trees with a parameter optimization loop, apply the best parameters to test data, and evaluate accuracy with the scorer.

Discover feature selection in KNIME using the feature selection loop to identify key variables. Normalize data, run a logistic regression model, and evaluate with ROC curves and accuracy.

Move from feature selection to evaluation by exporting a labeled dataset, normalizing data, training a logistic regression model, and evaluating accuracy and ROC on train and test sets.

Learn how to handle missing dates in KNIME data preparation by creating a date range per project, joining with costs, and using previous values to fill gaps.

Explore how to split a long text into lines every 100 characters in Knime using cell splitter, ungroup, string manipulation, moving aggregation, and group by with concatenate.

Create a KNIME workflow to count, for each customer, how many days between arrival and departure are weekdays or weekends. Use date range creation, weekday extraction, rule engine, and grouping.

Learn to check if a flow variable exists in KNIME using a Python node and try-catch logic to branch results.

Explore the available information challenge in knime by splitting pipes and commas, ungrouping lists, cleaning brackets with regex, and using one-hot encoding and group by to mark data presence.

Master approximate date matching in Knime by using a dictionary and an inner dictionary, creating a workflow with group by, group loop, and row filter to map dates to records.

Learn to remove text from one table using data from another in KNIME, leveraging strings to documents, dictionary replacer, and dictionary tagger to handle multiword targets.

Learn to tag data with keywords in Knime by cross joining descriptions with keyword lists, applying wildcard matches, aggregating keywords, and merging results back to the original data.

Develop a data-cleaning workflow that maps misspelled names to correct ones using Knime's similarity search with Levenstein distance and a join to produce clean, mapped data.

Learn to handle date conversion issues in KNIME with string manipulation and regex, transforming varied date formats into proper dates and enabling subsequent date calculations.

Master data preparation in KNIME by cleaning molecule data, applying string manipulation and regex, splitting and pivoting to count atoms like C, H, and N.

Discover how to move and copy files in knime using the new transfer files node (knime 9, 4.3+), filtering to copy only sales excel files into a new folder.

Clean a country dataset by unpivoting, using the cell splitter to remove parentheses, and re-pivoting to restore country, population, GDP, and inflation columns with KNIME nodes.

Learn how to dynamically merge two KNIME tables with a chunk loop, extract table dimensions, and use a column appender to map characters to their series.

Discover how to extract countries and their cities from a multi-structured JSON in KNIME by building a robust dataprep workflow using JSON reader, JSON path, ungroup, column splitter, and append.

Match mismatched or misspelled addresses in KNIME by preparing data, using the similarity search node with Levenshtein distance to find the nearest address matches.

Learn sentiment analysis in KNIME using a Twitter dataset. Convert sentiment to string for stratified sampling and transform tweets into documents for NLP.

Install knime text processing extensions and enable the text processing nodes. Clean data by removing numbers and punctuation, converting to lowercase, and applying porter stemming to create a pre-processed document.

Create a bag of words from pre-processed text, compute relative term frequency, build a document vector, include sentiment labels, shuffle data, and filter columns for KNIME NLP.

learn how to use the new chatgpt data app in knime to access GPT via OpenAI, configure credentials, and route chat outputs to a data port using a top-k filter.

Explore generative AI in KNIME with the built-in Chi assistant and KNIME hub; sign up free, retrieve web data, and build simple workflows using column and row filters.