Exploratory data analysis

Exploratory data analysis (EDA) is the first step in the Machine Learning pipeline. It allows us to make informed decisions about tools used to analyze the data.

Dataset description

During the EDA phase, we can make choices regarding which model to use depending on the attributes of a dataset.

Other tasks

EDA also detects unwanted values/noise that lead to inaccurate predictions.

Outlier

Outliers in a dataset are those samples that are abnormally extreme (i.e. far from the other data points). They can affect the overall accuracy of the model trained on the data. They are anomalies, causing biased or incorrect data.

During the EDA phase, these outliers are detected

• Background knowledge such as impossible values like negative age
• Visualization such as scatter plot
• Data Analysis such as box plot
• ML algorithms such as One-Class-SVM

and strategies are used to deal with them

• Correct values
• Delete values

Skewed data tend to have outliers on the side it's skewed to.

Data Normalization

Normalization is a technique used to scale numeric data to the same scale. It

• helps in faster convergence
• prevents some features from dominating others due to scale
• improves the overall performance of the model

There are several strategies to do this

• Min-max scaling: $X_{norm}={\frac {X-X_{min}}{X_{max}-X_{min}}}$
• Z-score scaling: $X_{\text{norm}}={\frac {X-\mu }{\sigma }}$