Understanding to PDF and CDF

Data Science, AI and ML
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What are PDF and CDF?

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CDF is the cumulative density function which is used for continuous types of variables.
PDF is the probability density function for both discrete & continuous variables, each probability is between 0&1, all probability is the sum to 1.

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  1. PDF ( Probability Density Function)
    This basically is a probability law for a continuous random variable say X ( for discrete, it is probability mass function).
    The probability law defines the chances of the random variable taking a particular value say x, i.e. P (X=x).
    However this definition is not valid for continuous random variables because the probability at a given point is zero.
    An alternate to this is: pdf= P (x-e<X<=x)/e as e tends to zero.

  2. CDF ( Cumulative Distribution Function)
    As the name cumulative suggests, this is simply the probability upto a particular value of the random variable, say x. Generally denoted by F, F= P (X<=x) for any value of x in the X space. It is defined for both discrete and continuous random variables.