KNIME

## KNIME Analytics Platform

KNIME Analytics Platform is the open source software for creating data science applications and services. Intuitive, open, and continuously integrating new developments, KNIME makes understanding data and designing data science workflows and reusable components accessible to everyone.

### Build end to end data science workflows

__Create visual workflows__ with an intuitive, drag and drop style graphical interface, without the need for coding.

__Blend tools from different domains__ with KNIME native nodes in a single workflow, including scripting in R & Python, machine learning, or connectors to Apache Spark.

__Choose from over 2000 modules ("nodes")__ to build your workflow. Model each step of your analysis, control the flow of data, and ensure your work is always current.

__Get up and running quickly.__ Select one of the hundreds of publicly available example workflows, or use the integrated workflow coach to guide you through building your workflow.

### Blend data from any source

__Open and combine__ simple text formats (CSV, PDF, XLS, JSON, XML, etc), unstructured data types (images, documents, networks, molecules, etc), or time series data.

__Connect__ to a host of databases and data warehouses to integrate data from Oracle, Microsoft SQL, Apache Hive, and more. Load Avro, Parquet, or ORC files from HDFS, S3, or Azure.

__Access and retrieve__ data from sources such as Twitter, AWS S3, Google Sheets, and Azure.

### Shape your data

__Derive statistics__, including mean, quantiles, and standard deviation, or apply statistical tests to validate a hypothesis. Integrate dimension reduction, correlation analysis, and more into your workflows.

__Aggregate, sort, filter, and join data__ either on your local machine, in-database, or in distributed big data environments.

__Clean data through normalization__, data type conversion, and missing value handling. Detect out of range values with outlier and anomaly detection algorithms.

__Extract and select features__ (or construct new ones) to prepare your dataset for machine learning. Manipulate text, apply formulas on numerical data, and apply rules to filter out or mark samples.

### Leverage Machine Learning and AI

__Build machine learning models__ for classification, regression, dimension reduction, or clustering, using advanced algorithms including deep learning, tree-based methods, and logistic regression.

__Optimize model performance__ with hyperparameter optimization, boosting, bagging, stacking, or building complex ensembles.

__Validate models__ by applying performance metrics including Accuracy, R², AUC, and ROC. Perform cross validation to guarantee model stability.