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**Formal Classification and prediction Definition: **

- Classification and prediction are two forms of data analysis those can be used to extract models describing important data classes or to predict future data trends.
- Such analysis can help to provide us with a better understanding of the data at large.
- Classification predicts categorical (discrete, unordered) labels, prediction models continuous valued functions.

**Let’s Understand Classification ****a morsel more:**

- The goal of data classification is to organize and categorize data in distinct classes.
- A model is first created based on the data distribution.
- The model is then used to classify new data.
- Given the model, a class can be predicted for new data.
- In general way of saying classification is for discrete and nominal values.

**Let’s Understand Prediction a morsel more:**

- The goal of prediction is to forecast or deduce the value of an attribute based on values of other attributes.
- A model is first created based on the data distribution.
- The model is then used to predict future or unknown values.

**Summarization of Classification and Prediction:**

- If forecasting
**discrete**value ( Classification ) - If forecasting
**continuous**value ( Prediction )

**Understanding Classification and prediction in DataAspirant way:**

**Classification:**

- Suppose from your past data (
**train data**) you come to know that your best friend likes above movies. - Now one new movie (
**test data**) released and hopefully you want to know your best friend like it or not. - If you strongly conformed about chances of liking that movie by your friend, you can take your friend to movie this weekend.
- If you clearly observe the problem it is just whether your friend
**like or not.** - Finding solution to this type of problem is called as
**classification.**This is because we are classifying the things to their belongings**(yes or no, like or dislike )** - Keep in mind here we are forecasting
**discrete**value( classification ) and the other thing this classification belongs to**Supervised learning.** - This is because you are
**learning**this from your**train data.** - Mostly classification is
**binary classification**in which we have to predict whether output belongs to class 1 or class 2**(class 1 : yes, class 2: no )** - We can use classification for predicting more classes too. Like
**(suppose colors: RED,GREEN,BLUE,YELLOW,ORANGE)**

**Prediction:**

- Suppose from your past data (
**train data**) you come to know that your best friend liked above movies and you also know how many times each particular movie seen by your friend. - Now one new movie
**( test data )**released same like above, now your are going to find how many times this present newly released movie will your friend watch is it , 5 times, 6 times,10 times anything. - If you clearly observe the problem it is about finding the
**count**, some times we can say this as**predicting the value.** - Keep in mind, here we are forecasting
**continuous value**( Prediction ) and the other thing this prediction is also belongs to**Supervised learning.** - This is because you are
**learning**this from you**train data.**

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Can you explain me difference between Discrete data values and Continuous data values with examples?

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Hi,

Discrete data values can only take certain values. For example you will have certain number of friends like 4 or 5 but you can’t have 4.5(4 and half) friends. So These type of data values are called as discrete. weight of some object, height of the person these type of data are called as Continuous data.

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[…] is the basic building block for techniques such as Recommendation engines, clustering, classification and anomaly […]

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[…] is the very basic building block for activities such as Recommendation engines, clustering, classification and anomaly […]

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