Data Collection:
by using one of ML repository or using SQL or build web scraping application to collect specific data.
Data Inspection:
- Outliers
- Missing or incomplete values
- Data that needs to be transformed or preprocessed so it's in the correct format to be used by your model
Summary Statistics:
you can calculate things like the mean, inner-quartile range (IQR), and standard deviation. These tools can give you insight into the scope, scale, and shape of the dataset.
Data Visualization:
You can use data visualization to see outliers and trends in your data and to help stakeholders understand your data.
Impute is a common term referring to different statistical tools which can be used to calculate missing values from your dataset.
Outliers are data points that are significantly different from others in the same sample. ____>>> False
True or false: Data visualizations are the only way to identify outliers in your data.
False- Impute is a common term referring to different statistical tools which can be used to calculate missing values from your dataset.
- Outliers are data points that are significantly different from others in the same sample.
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