Data Preprocessor

A Python class for data preprocessing tasks such as object to numeric transformation, handling null values, standardization, and normalization.

Table of Contents

• Introduction
• Usage
• Methods
• Examples
• License

Introduction

The DataPreprocessor class provides methods to perform common data preprocessing tasks. It supports object to numeric transformation, handling null values, standardization, and normalization. The class utilizes the pandas library and scikit-learn's StandardScaler and MinMaxScaler for certain operations.

Usage

To use the DataPreprocessor class, follow these steps:

1. Import the necessary libraries:

   import pandas as pd
   from sklearn.preprocessing import StandardScaler
   from sklearn.preprocessing import MinMaxScaler

2. Create an instance of the DataPreprocessor class:

   preprocessor = DataPreprocessor()

3. Perform the desired data preprocessing tasks using the available methods:

   • Object to Numeric Transformation:

     transformed_data = preprocessor.object_to_numeric(dataset)

   • Handling Null Values:

     filled_data = preprocessor.handle_null_values(dataset, fill_value)

   • Standardization:

     standardized_data = preprocessor.standardize_dataset(dataset)

   • Normalization:

     normalized_data = preprocessor.normalize_dataset(dataset)

Methods

• object_to_numeric(dataset): Converts object data to numerical data in the dataset.
• handle_null_values(dataset, fill_value): Handles null values in the dataset by filling them with a specified value.
• standardize_dataset(dataset): Standardizes the dataset by scaling the numerical features to have zero mean and unit variance.
• normalize_dataset(dataset): Normalizes the dataset by scaling the numerical features to a fixed range (e.g., [0, 1]).

Examples

Here are some examples of how to use the DataPreprocessor class:

# Create an instance of DataPreprocessor
preprocessor = DataPreprocessor()

# Object to Numeric Transformation
transformed_data = preprocessor.object_to_numeric(dataset)

# Handling Null Values
filled_data = preprocessor.handle_null_values(dataset, fill_value)

# Standardization
standardized_data = preprocessor.standardize_dataset(dataset)

# Normalization
normalized_data = preprocessor.normalize_dataset(dataset)