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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


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