Project Cycle Glossary Class 9

Project Cycle
Problem Scoping
Data Acquisition
Data Exploration
4Ws of Problem Scoping
Statement Template
Project Stakeholder
Data Sources
Visualization Tools
Learning Based Approach
Rule Based Approach
Training Set
Testing Set

Project Cycle Overview

Ai Project cycle

What is Project Cycle ?

In earlier classes we have studied about Water Cycle, etc. In water Cycle we studied that how is the journey of water carried out from one step to other till the end.

Like that in Project Cycle we are going to deal with the steps involved in creating a project, starting from the given problem till the project is created and tested.

Project Cycle is a step by step process to solve the problems using proven scientific methods and drawing the inference about it.

Let us take some daily examples as project, requiring steps to solve the problem.

Making Tea

  1. Boil Water .
  2. Put Tea Powder
  3. Put Milk
This was a simple one which was only consisting exploring the items and modelling it into tea. Lets see one more example:

Creating a birthday card.

  1. Checking the factors like budget,etc Which will help us decide the next steps and understanding the Project.
  2. Acquiring data from different sources like online, with friends etc for Designs and ideas.
  3. Making a list of the gathered data.
  4. Creating or Modelling a card on the basis of the data collected.
  5. Showing it to Parents or cousins to Let them check it or evaluate it.

Components of Project Cycle

Components of project cycle are the steps which contributes in completing the Project.

Components of the AI Project Cycle :

Problem Scoping Understanding the problem
Data Acquisition Collecting accurate and reliable data
Data Exploration Arranging the data uniformly
Modelling Creating Models from the data
Evaluation Evaluating the project

Problem Scoping

Problem Scoping Project Cycle

Problem Scoping refers to understanding a problem finding out various factors which affect the problem, define the goal or aim of the project.

4Ws Of Problem Scoping

The 4W's of Problem Scoping are Who, What, Where and Why. These Ws helps in identifying and understanding the problem in a better and efficient manner.

1. Who - "Who" part helps us in comprehending and categorizing who all are affected directly and indirectly with the problem and who are called the Stake Holders

2. What - "What" part helps us in understanding and identifying the nature of the problem and under this block, you also gather evidence to prove that the problem you have selected actually exists.

3. Where - "Where" does the problem arises, situation and the location.

4. Why - "Why" is the given problem worth solving.

Problem Statement Template

The Problem Statement Template helps us to summarise all the key points into one single.

Template so that in future, whenever there is need to look back at the basis of the problem, we can take a look at the Problem Statement Template and understand the key elements of it.

Data Acquisition

Data Acquisition is he process of collecting accurate and reliable data to work with. Data Can be in the format of text, video, images, audio and so on and it can be collected from carious source like the interest, journals, newspapers and so on.

Data Sources

Data Acquisition Sources Project Cycle

Data Exploration

Data Exploration is the process of arranging the gathered data uniformly for a better understanding. Data can be arranged in the form of a table, plotting a chart or making database.

If we simplify this Data Exploration means that the data which we collected in Data Acquisition, in Data Exploration we need to arrange it for example if we have data of 50 students in a class, we have their Mobile Number, Date of Birth, Class, Etc .

In the process of data exploration we can make a chart for that data in which all the names will be at one place and all the mobile numbers at one etc.

Data Exploration or Visualization Tools

Data Exploration Tools Project Cycle

1. Google Charts

Google chart tools are powerful, simple to use, and free. Try out our rich gallery of interactive charts and data tools.

2. Tableau

Tableau is often regarded as the grand master of data visualization software and for good reason.

Tableau has a very large customer base of 57,000+ accounts across many industries due to its simplicity of use and ability to produce interactive visualizations far beyond those provided by general BI solutions.

3. FusionCharts

This is a very widely-used, JavaScript-based charting and visualization package that has established itself as one of the leaders in the paid-for market.

It can produce 90 different chart types and integrates with a large number of platforms and frameworks giving a great deal of flexibility.

4. Highcharts

A simple options-structure allows for deep customization, and styling can be done via JavaScript or CSS. Highcharts is also extendable and pluggable for experts seeking advanced animations and functionality.


Modelling is the process in which different models based on the visualized data can be created and even checked for the advantages and disadvantages of the model.

To Make a machine learning model there are 2 ways/Approaches Learning Based Approach and Rule Based Approach

Learning Based Approach

learning based approach Project Cycle

Learning Based Approach is based on Machine learning experiance with the data feeded.

Machine Learning

Machine learning is a subset of artificial Intelligence (AI) whcih provides machines the ability to learn automatically and improve from experience without being programmed for it.

Types of Machine Learning

Machine learning can be divided into 3 types, Supervised Learning, Unsupervised Learning, andSemisupervised or Reinforcement Learning

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Rule Based Approach


Dataset is a collection of related sets of Information that is composed of separate elements but can be manipulated by a computer as a unit.

In Rule based Approach we will deal with 2 divisions of dataset:

1. Training Data - A subset required to train the model
2. Testing Data - A subset required while testing the trained the model

Training Set

Testing Set

Training vs Testing Data

Base Training Set Testing Set
Use Used for Training the Model Used for Testing the Model after it is trained
Size Is allot bigger than testing data and constitutes about 70% to 80% It is smaller than Training Set and constitutes about 20% to 30%


Evaluation is the method of understanding the reliability of an API Evaluation and is based on the outputs which is received by the feeding the data into the model and comparing the output with the actual answers.

API Stands for Application Programming Interface