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Step 4: Data Sampler
Stage 4 Modelling- Modelling in the AI project cycle is the process of selecting the appropriate machine
learning algorithm, training it with the data, and using the trained model to make predictions or
classifications. It involves understanding the data, choosing the right type of model, and preparing it
for real-world use.
For this stage, we will train data in Orange Data Mining as the given step:
Step 5: Train Model
Stage 5 Evaluation- After building and training your model, you need to assess its performance on new or
unseen data to ensure it is reliable and accurate. Evaluation helps in deciding whether to deploy the
model, adjust it, or try a different approach.
For this stage, we will evaluate the dataset in Orange Data Mining by following the given steps:
Step 6: Evaluate Model
Step 7: Predictions
Stage 6 Deployment- It involves implementing the trained model into a real-world environment. It includes
integrating the AI system with existing infrastructure, ensuring scalability, and monitoring performance.
Continuous evaluation and retraining are necessary to maintain accuracy. Deployment also involves
handling security, compliance, and user feedback for improvements.
Orange Data Mining Widgets
Let us discuss about some commonly used widgets of Orange Data Mining.
Data Widgets
Data widgets are essential components that help you interact with, manipulate,
and visualise your data. These widgets allow you to load datasets, explore data,
preprocess it, and apply machine learning algorithms. Some of these Data Widgets
are:
• File Widget: It is used to load and import data into the platform.
• CSV File Import Widget: It is used to import datasets stored in Comma-
Separated Values (CSV) files. CSV is a common data format used to store tabular
data, making it an essential widget for users who work with this file type.
• Dataset Widget: It is used to manage and view datasets within Orange. It
provides a way to inspect the loaded data, check for inconsistencies, and perform basic data manipulations.
• Data Table Widget: It allows users to interact with their data in a structured and accessible way, displaying
datasets in tabular form.
• Data Info Widget: It gives an overview of the dataset’s properties, helping users understand the data before
applying machine learning algorithms or further data processing.
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