# What is conditional statement with example?

Example: We have a conditional statement If it is raining, we will not play. Let, A: It is raining and B: we will not play. Then; If A is true, that is, it is raining and B is false, that is, we played, then the statement A implies B is false.

## What is a conditional statement Short answer?

Conditional statements help you to make a decision based on certain conditions. These conditions are specified by a set of conditional statements having boolean expressions which are evaluated to a boolean value of true or false.

## What is conditional statement in algorithm?

A CONDITIONAL is a type of step in an algorithm where a decision must be made. Computers follow logical instructions and they need to know how to handle different decisions so that programs can proceed no matter what the outcome of those selections may be.

## What are conditional statements in C++?

C++ has the following conditional statements: Use if to specify a block of code to be executed, if a specified condition is true. Use else to specify a block of code to be executed, if the same condition is false. Use else if to specify a new condition to test, if the first condition is false.

What is conditional statement with example? – Related Questions

## Why are conditional statements important in programming?

Conditional statements help mathematicians and computer programmers make decisions based on the state of a situation. While they vary in use and complexity, professionals typically use conditional statements to test hypotheses and establish rules for programs to follow.

## What is a condition in computing?

A condition is something that a computer can decide is either true or false. True is like the computer is answering yes and false is like answering no. You can tell your app to do different things depending on if the condition is true or false.

## What are the 3 algorithm constructs?

An algorithm is made up of three basic building blocks: sequencing, selection, and iteration.

## What are the 4 ways we can represent an algorithm?

Expressing an algorithm
• We can express an algorithm many ways, including natural language, flow charts, pseudocode, and of course, actual programming languages.
• Natural language is a popular choice, since it comes so naturally to us and can convey the steps of an algorithm to a wide audience.

## What are 3 examples of algorithms?

Common examples include: the recipe for baking a cake, the method we use to solve a long division problem, the process of doing laundry, and the functionality of a search engine are all examples of an algorithm.

## Which algorithm is used in real life?

Recipes. Just like sorting papers and even tying your shoes, following a recipe is a type of algorithm. The goal of course being to create a duplicated outcome. In order to complete a recipe you have to follow a given set of steps.

## Why do we need an algorithm?

Algorithms are used in every part of computer science. They form the field’s backbone. In computer science, an algorithm gives the computer a specific set of instructions, which allows the computer to do everything, be it running a calculator or running a rocket.

## What is the difference between data structure and algorithm?

A data structure is a named location that can be used to store and organize data. And, an algorithm is a collection of steps to solve a particular problem. Learning data structures and algorithms allow us to write efficient and optimized computer programs.

## How do you create an algorithm?

How to build an algorithm in six steps
1. Step 1: Determine the goal of the algorithm.
2. Step 2: Access historic and current data.
3. Step 3: Choose the right models.
4. Step 4: Fine tuning.
5. Step 5: Visualize your results.
6. Step 6: Running your algorithm continuously.

## What is the difference between AI and algorithm?

With those definitions of algorithm and A.I./ML, their differences become clearer. In short, a regular algorithm simply performs a task as instructed, while a true A.I. is coded to learn to perform a task.

## Why Python is useful for AI?

The ease of use of the programming language plays an important role in exchanging ideas, tools, and algorithms. Therefore, Ai-professionals can easily use Python to bring minor or major changes into their projects. Apart from the general schedule, numerous tools help create an interactive design.

## How do I start learning about artificial intelligence?

You can learn artificial intelligence by taking an online course or enrolling in a data science bootcamp. Many bootcamps provide an introduction to machine learning. Machine learning is a tool used by AI that involves exposing an algorithm to a large amount of data. It allows the AI to learn faster.

## What’s the difference between data mining and machine learning?

What is the Difference Between Data Mining and Machine Learning? Data mining is the probing of available datasets in order to identify patterns and anomalies. Machine learning is the process of machines (a.k.a. computers) learning from heterogeneous data in a way that mimics the human learning process.

## How is deep learning related to artificial intelligence and data?

Deep learning is a kind of machine learning but this approach uses neural networks for making predictions based on processed data. Most AI work involves either ML or DL since the so-called “intelligent” behavior of machines requires massive knowledge, which, in turn, requires data science and data mining research.

## What is the role of artificial intelligence and machine learning in data analysis?

Artificial intelligence essentially makes machines simulate human intelligence while ML deals with learning from past data without being explicitly programmed. AI focuses on making systems that can solve complex problems while ML aims to make machines learn from available data and generate accurate outputs.

## Which is better data mining or artificial intelligence?

Data Mining produces accurate results which are used by machine learning and thereby makes machine learning produce better results. As data mining requires human intervention, it may miss important relationships. Machine learning algorithms are proved to be more accurate than the Data Mining techniques.