Data Preprocessing- Feature Selection and Merging.

Data Preprocessing- Feature Selection and Merging.

1. Data Science Data Preprocessing (Feature Selection and Merging )

2. Scaling, Normalization, Categorical Encoding Handling missing values Outliers, duplicates Selecting relevant features/Column, Data Combining multiple datasets/ Merging

3. Feature Selection A feature is an attribute that has an impact on a problem or is useful for the problem, and choosing the important features for the model is known as feature selection. Feature selection is often performed to remove irrelevant or redundant features from the dataset. We can define feature Selection as, "It is a process of automatically or manually selecting the subset of most appropriate and relevant features to be used in model building." Feature selection is performed by either including the important features or excluding the irrelevant features in the dataset without changing them.

4. Feature Selection Techniques Supervised Feature Selection technique : Supervised Feature selection techniques consider the target variable and can be used for the labeled dataset. Unsupervised Feature Selection technique: Unsupervised Feature selection techniques ignore the target variable and can be used for the unlabeled dataset.

5. Common techniques for selecting relevant features Feature Importance Recursive Feature Elimination(RFE) Forward/Backward Elimination Principal Component Analysis (PCA) Filter Method Domain Knowledge

6. Feature Extraction Feature extraction involves transforming the original features into a new set of features through mathematical transformations or projections. F eature selection involves selecting a subset of the original features based on their relevance, while feature extraction involves transforming the original features into a new set of features. Both techniques are used for dimensionality reduction to improve model performance, reduce overfitting, and enhance interpretability.

7. Merging: Combining Multiple Datasets Merging also known as joining, is a fundamental operation in data science where we combine data from multiple datasets based on a common attribute or key. Merging is essential when dealing with essential datasets or when integrating data from multiple sources. In merging it is important to ensure that the keys used for merging are consistent and that we handle missing values appropriately.

8. Types of Merges The most common method for merging data is through a process called “joining”. There are several types of joins. • Inner Join: Uses a comparison operator to match rows from two tables that are based on the values in common columns from each table. • Left join/left outer join. Returns all the rows from the left table that are specified in the left outer join clause, not just the rows in which the columns match. • Right join/right outer join Returns all the rows from the right table that are specified in the right outer join clause, not just the rows in which the columns match.

9. Continue… • Full outer join Returns all the rows in both the left and right tables. • Cross joins (cartesian join) Returns all possible combinations of rows from two tables.

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