AI Project Cycle

 AI Project Cycle

The AI Project Cycle is a step-by-step way to create an AI project. It helps in solving a problem using AI.

There are 5 main stages:

---

1. Problem Scoping (Understanding the Problem)

First, we find what problem we want to solve.

Then, we ask who is facing the problem, and how solving it will help.

Example: Students are wasting food in the school canteen. Can we reduce it?

---

2. Data Collection

We collect information (data) related to the problem.

This data helps the AI to learn.

Example: How much food is wasted daily, what food items are thrown the most, etc.

---

3. Data Exploration

Now, we study and understand the data.

We find patterns, remove wrong data, and organize it.

Example: We see that rice is wasted the most on Fridays.

---

4. Modelling

In this step, we train the AI model using the data.

The computer learns from the data and makes predictions or decisions.

Example: AI learns which food items are wasted more and on which days.

---

5. Evaluation

We test if the AI is working correctly.

If not, we go back, fix the problem, and try again.

Example: AI gives suggestions to reduce food waste. We check if it really helps.

---