Project Cycle Glossary Class 10
Project Cycle Overview
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.
- Boil Water .
- Put Tea Powder
- Put Milk
Creating a birthday card.
- Checking the factors like budget,etc Which will help us decide the next steps and understanding the Project.
- Acquiring data from different sources like online, with friends etc for Designs and ideas.
- Making a list of the gathered data.
- Creating or Modelling a card on the basis of the data collected.
- 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.
|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 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
Problem Statement Template helps us to summarize all the key points into one
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 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 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
Google chart tools are powerful, simple to use, and free. Try out our rich gallery of interactive charts and data tools.
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.
It can produce 90 different chart types and integrates with a large number of platforms and frameworks giving a great deal of flexibility.
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 is based on Machine learning experience with the data feeded.
Machine learning is a subset of artificial Intelligence (AI) which 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 is where a computer algorithm is trained on input data that has been labeled for a particular output example: a shape with three sides is labelled as a traingle, Classification and Regression models are also type of supervised Learning
What is classification ?
classification in which algorithm’s job is to separate the labelled data to predict the output. example: to predict weather which of them is apple and pineapple.
What is Regression ?
Regression is a type of supervised learning which is used to predict continuos value. example: Regression is used to predict wether . it is also used widely for wether forecasting .
In terms of machine learning, unsupervised learning is in which a system learn through data-sets created by its own. In this the training is not labeled.
Learning by own is termed as Unsupervised learning.
Basically, in unsupervised learning where the data is un-tagged or un-named, the machine create a learning algorithm using its structural data-sets present in its input.
Example: Suppose a boy sees someone performing tricks by a ball, so he also learnt the tricks by himself. This is what we call as unsupervised learning.
Learning through feed-back or trial and error method is called Reinforcement learning.
In this type of learning, The system works on Reward or Penalty policy. In this a agent perform a action positive or negative, in the environment which is taken as input from the system, then the system changes the state in the environment and the agent is provided with a reward or penalty.
The system also builds a policy, that what action should be taken under a specific condition.
Example: A very good example of these is Vending machines.
Suppose you put a coin (action) in a Juice Vending machine(environment), now the system detects the amount of coin give(state) you get the drink corresponding to the amount(reward) or if the coin is damaged or there is any another problem, then you get nothing (penalty).
Here the machine is building a policy that which drink should be provided under what condition and how to handle a error in the environment.
Rule Based Approach
- Rule Based Approach Refers to the AI modelling where the relationship or patterns in data are defined by the developer.
- That means the machine works on the rules and information given by the developer and performs the task accordingly.
For example: Suppose have a datasets containing 100 images of apples and bananas each. Now you created a machine using Computer-Vision, and trained it with the labelled images of apples and bananas. If you test your machine with a image of an apple it will give you the output by comparing the images in its datasets. This is known as Rule Based Approach.
To know in detail visit Decision-tree
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
|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