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• Python Integration: While it is a no-code tool, ODM also allows users to write Python scripts for advanced
customization. Users can easily integrate their work with Python for further analysis or to extend functionality.
This feature makes it flexible and powerful for data scientists who may need to implement more specific or
advanced models.
• Open-Source: ODM is free and open-source, which makes it accessible to anyone interested in data analysis or
machine learning. This also allows for extensive customization and contribution from the community.
Applying the AI Project Cycle in Orange Data Mining
The AI Project Cycle is a structured approach to designing, building, and evaluating machine learning models. It
typically consists of several stages as shown below:
Data
Acquisition Step 2: Clean Modelling Step 6: Evaluate
Step 1: Upload Missing Data Step 5: Train Model
Dataset Step 3: Select Model Step 7: Predictions
Columns
Step 4: Data Deployment
Sampler
Problem Data
Scoping Evaluation
Exploration
Below is how you can apply each stage of the AI Project Cycle using Orange Data Mining:
Stage 1 Problem Scoping- It includes setting clear objectives, ensuring feasibility, and identifying relevant data
sources. This step helps in aligning AI solutions with business goals and constraints. Proper problem
scoping ensures an effective and efficient AI model development process.
Stage 2 Data Acquisition- It is a critical step in the AI (Artificial Intelligence) Cycle. It involves gathering and
collecting relevant data required to train AI models and perform analyses. Data is the foundation upon
which machine learning algorithms and AI systems are built, so acquiring high-quality, accurate, and
relevant data is essential.
For this stage, we will upload the dataset in Orange Data Mining as step 1.
Step 1: Upload Dataset
Stage 3 Data Exploration- Data Exploration in AI project cycle involves examining and understanding the data
before using it to train machine learning models or develop AI systems. Proper data exploration helps
ensure that the data is clean, relevant, and ready for analysis. During this phase, that data scientists and
AI practitioners uncover patterns, relationships, inconsistencies, and other characteristics in the data
that will guide further data preparation, feature engineering, and model building.
For this stage, we will explore the dataset in Orange Data Mining by following the given steps:
Step 2: Clean Missing Data
Step 3: Select Columns
Statisical Data (Practical) 267

