Median

Mean

Standard Deviation

Mode

Removing outliers

Introducing errors into the dataset

Ignoring inconsistencies

Identifying unusual patterns or data points

Data Interpretation

Data Mining

Data Analysis

Data Exploration

To create missing values

To reduce unnecessary duplication

To introduce outliers

To add noise to the dataset

Any dataset with more than 1,000 rows

Data that is too large and complex to be processed by traditional methods

A large dataset that fits in a standard database

Data that is easy to analyze and interpret

feature score

Distribution score

 normalized score

standard score

Bar chart

Histogram

Scatter plot

Box-plot

Scikit-learn

Pandas

TensorFlow

Matplotlib

Any value below Q1-1.5*IQR or above Q3+1.5*IQR

Any value below Q1+1.5*IQR or above Q3-1.5*IQR

Any value below Q1+1.5*IQR or above Q3+0.5*IQR

Any value below Q1-0.5*IQR or above Q3-1.5*IQR

Q3 is the value below which 25 percent of the distribution lies.

 Q3 is the value below which 75 percent of the distribution lies.

Q3 is the value above which 25 percent of the distribution lies.

Q3 is the value above which 75 percent of the distribution lies.