AI Project Cycle Practical, The AI project cycle is a step-by-step process that works, from identifying a problem to deploying and evaluating the solution. There are 6 different types of steps in the AI project cycle; the AI project cycle software is given below is based on all 6 of these steps, and this software uses a rule-based program to perform the prediction. The six steps of the AI project cycle are problem scoping, data acquisition, data exploration, modelling, evaluation and deployment.
AI Project Cycle Practical
1. Problem Scoping
What problem do you want to solve?
2. Data Acquisition
You need to acquire data, which will become the base of your project.
3. Data Exploration
Understand the data using graphs, databases, flowcharts, maps, formula etc.
4. Modelling
Decide the type of model you would build to achieve the goal, we have used rule based AI.
5. Evaluation
Check the accuracy of prediction. The most efficient model is now the base of your AI project.
6. Deployment
You now need to test your model on newly fetched data.
Final Output
Disclaimer: We have tried to provide the practical base model, which will help the students to understand the concept. If you find any errors or the software is not working properly, then you can contact me at anuraganand2017@gmail.com. The above model present on our websites is for educational purposes, not our copyrights. The above content, information or code are used here for reference purposes only.