Class 9 AI Chapter - AI Project Cycle Topic - AI Project Cycle

 

Class 9 Artificial Intelligence Code 417 Solutions
Session 2025 - 26

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AI Project Cycle

The AI Project Cycle is a step-by-step process followed to develop an Artificial Intelligence (AI) project. It helps in solving real-world problems using data and intelligent algorithms. The cycle ensures a structured approach from understanding the problem to deploying the AI model.

AI Project Cycle

✅ Problem Scoping in AI: 

Problem Scoping is the first and foundational step in the AI Project Cycle. It involves understanding the problem, identifying what needs to be solved, who it affects, and how AI can help.

Here are the main methods used:

1. Understanding the Problem

• Clearly define the real-world problem.
• Ask: What is the issue? Why does it need to be solved?
• Look for the root cause, not just symptoms.

2. Identifying Stakeholders

• Who is directly or indirectly affected by the problem or the solution?
• Stakeholders include:
  
  • End-users (students, patients, drivers)
  • Organizations (schools, hospitals)
  • Developers
  • Government or NGOs

3. Defining Objectives and Goals

• What do you want the AI system to achieve?
• Be SMART: Specific, Measurable, Achievable, Relevant, Time-bound.

4. Creating a Problem Statement

• Convert the real-world issue into a precise AI-solvable problem.
• Example: “Predict student exam performance using attendance and study hours.”

5. Using the 4W Problem Canvas

Break the problem into 4 key questions:

• What is the problem?
• Why is it important?
• Who are the stakeholders?
• Where does it occur?

6. Identifying Constraints and Ethical Considerations

• Data availability, budget, time, privacy, bias, and fairness must be considered.
• Make sure the AI model will be ethical, non-discriminatory, and safe.

🧪 Case Study: Improving Student Performance with AI

Let’s go through all the above steps using a realistic example.

🟦 Real-World Problem:

Students in rural schools are performing poorly in exams, especially in mathematics.

✅ Step-by-Step Problem Scoping:

1. Understanding the Problem

• Many students in rural schools fail in math due to lack of access to good teachers or digital learning tools.
• This leads to dropouts and poor career opportunities.

2. Identifying Stakeholders

• Primary: Students, Teachers
• Secondary: Parents, School Administrators, Education Department, NGOs

3. Defining Objectives

• Create an AI model that:
  
  • Predicts which students are at risk of failing.
  • Suggests personalized learning resources.
    
    
  SMART Goal: "Increase pass percentage by 15% in 6 months using AI-based prediction and feedback."

4. Creating a Problem Statement

• “Can we use data on attendance, homework submission, past marks, and online activity to predict students at risk of failing and recommend learning resources accordingly?”

5. 4W Problem Canvas

W	Answer
What	Students are failing math exams in rural schools.
Why	Due to lack of resources, guidance, and personalized support.
Who	Students, teachers, parents, government education departments.
Where	Rural schools in regions with poor digital infrastructure.

6. Constraints & Ethics

Constraints:

• Poor internet in rural areas.
• Limited digital data.

Ethical Concerns:


• Avoid bias against poor or disabled students.
• Maintain privacy of student data.

🔍 Final Output of Problem Scoping:

A well-scoped AI project idea:

“An AI system that predicts students likely to fail based on behavioral and academic data, and recommends custom learning resources to improve their performance.”

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📥 Data Acquisition

Data Acquisition is the second stage in the AI Project Cycle, following Problem Scoping. In this stage, we collect data that will be used to train, test, and evaluate the AI model.

Without good data, the AI system cannot learn or make accurate predictions. Think of data as the fuel that powers your AI engine.

✅ Purpose of Data Acquisition

• Gather relevant and high-quality data.
• Ensure the data reflects the real-world problem.
• Prepare the dataset for further analysis in the next stage (Data Exploration).

🔍 Key Activities in Data Acquisition

1. Identify Data Requirements
   

   • What type of data is needed? (text, numbers, images, audio, etc.)
   • What features/attributes are important?
   • Example: For predicting student performance → marks, attendance, homework submission.

2. Find Reliable Data Sources
   

   • Look for trustworthy, accurate, and legal data sources.

3. Collect the Data
   

   • From various sources (explained below).

4. Store the Data
   

   • Save it securely for future use (e.g., in databases, spreadsheets, cloud storage).

🔗 Reliable Sources for Data Acquisition

Data can be obtained from primary or secondary sources. Below are some reliable sources commonly used:

1. 📊 Open Data Repositories

• Government portals, academic institutions, NGOs.
• Examples:
  
  • data.gov.in – Indian Government Open Data
  • data.gov – US Government Open Data
  • Kaggle Datasets
  • UCI Machine Learning Repository

2. 🏢 Company Databases / Internal Records

• For organizations: use internal systems (CRM, ERP, LMS).
• Example: A school may collect student attendance, grades, test records.

3. 🌐 Web Scraping

• Extracting data from websites using tools like BeautifulSoup, Scrapy, or APIs.
• Example: Scraping real estate listings for a housing price prediction model.
• ⚠️ Note: Always check if web scraping is allowed (legal/ethical).

4. 📱 Sensors and IoT Devices

• For AI projects involving real-time monitoring (e.g., traffic, temperature, health).
• Example: Smartwatches collect heart rate data for health prediction.

5. 📄 APIs (Application Programming Interfaces)

• Access data from platforms like:
  
  • Google Maps API (location data)
  • Twitter API (social media data)
  • Weather API (climate data)

6. 📝 Surveys and Forms (Primary Data Collection)

• When existing data isn’t available.
• Use tools like Google Forms, Microsoft Forms to gather responses directly.

⚠️ Things to Ensure in Data Acquisition

Factor	Why It Matters
Quality	Accurate and clean data gives better results.
Relevance	Data must match the problem's context.
Volume	AI models often require large datasets.
Privacy	Always comply with laws like GDPR. Don't collect personal data without consent.
Bias-Free	Ensure the data represents all groups fairly.

📌 Example from Case Study: Student Performance

Problem: Predict which students are likely to fail math.

Data Needed:

• Student ID
• Attendance record
• Homework completion
• Previous exam marks
• Teacher feedback

Sources:

• School database (internal record)
• Excel sheets from teachers
• Google Forms filled by students/parents

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📘 Data Exploration 

Data Exploration is the third stage of the AI Project Cycle, right after Data Acquisition.

It is the process of examining, analyzing, and understanding the data you have collected, before building an AI model. The main goal is to make sense of the data so you can decide how to use it effectively.

🧠 Why is Data Exploration Important?

• To find errors or missing values.
• To understand the structure and meaning of the data.
• To identify any patterns or relationships between features.
• To decide which features (columns) are useful for building the AI model.

🔍 Key Activities in Data Exploration:

Activity	What it Means
Data Cleaning	Fixing errors, removing duplicates, filling missing values
Summary Statistics	Checking mean, median, max, min, etc.
Correlation Analysis	Checking which factors are related
Outlier Detection	Finding unusual values that might affect results
Data Visualization	Creating graphs and charts to see patterns clearly

🎨 Data Visualization

Data Visualization is the process of converting data into visual formats like graphs, charts, or maps to make it easier to understand.

🧠 Why use Data Visualization?

• It’s easier to see patterns and trends visually than in raw numbers.
• Helps in quick decision-making.
• Makes communication of results simple and effective.

📊 Common Data Visualization Types:

Visualization Type	Use
Bar Chart	Compare values across categories
Pie Chart	Show parts of a whole
Line Graph	Show trends over time
Histogram	Show frequency distribution
Scatter Plot	Show relationships between two variables
Heatmap	Show patterns in a table of values

🛠️ Popular Data Visualization Tools:

Tool	Description
Microsoft Excel	Easy and beginner-friendly for charts and graphs
Tableau	Powerful, drag-and-drop tool for dashboards
Power BI	Microsoft tool for business analytics and reports
Google Data Studio	Web-based tool for creating live dashboards
Python Libraries (like matplotlib, seaborn)	Used by programmers for custom charts
R Programming (ggplot2)	Great for statistical data visualization

🧪 Example from AI Case Study:

Problem: Students are failing in math.

In Data Exploration, we:

• Find that students with less than 60% attendance often fail.
• Use a bar chart to compare failure rates by attendance.
• Use a scatter plot to show the link between homework submission and marks.

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