How to Perform Data Cleaning and Preprocessing in Python

Mastering Data Cleaning and Preprocessing in Python for Robust Analysis and Machine Learning

Data cleaning and preprocessing are important steps in the data analytics and machine learning pipeline. Clean and well-preprocessed data not only ensures successful analysis but also contributes to the success of machine learning models. In this article, we will go through the steps and techniques needed for data cleaning and preprocessing using Python.

Data Preprocessing is the most important step while building our model. In the Data Preprocessing step, the data are transformed into a form suitable for model ingestion. There are several steps in Data Preprocessing which are shown in the flowchart below. In this article, we will only discuss the first step of Data Preprocessing which is Data Cleaning.

Steps To Perform Data Cleaning and Preprocessing in Python

Step 1: Data cleaning

The first step in Data Preprocessing is Data Cleaning. Most of the data we work with today is impure and requires extensive Data Cleaning. Some have missing values ​​and some have junk data in them. Without proper handling of these missing values ​​and inconsistencies, our model would not provide convincing results.

Step 2: Collection of information

Data is raw information, it is a representation of the human and machine observations of the world. The dataset depends entirely on the type of problem you want to solve. Each problem in machine learning has its unique approach.

Step 3: Import dataset & Libraries

The first step is usually to import the libraries that the program will need. A library is a collection of modules that can be called and used. And with the help of the ‘import’ keyword libraries can be imported into Python code.

Step 4: The behavior of missing values

Sometimes we find that some data is missing from the data set. When we find it we will remove those rows or we can calculate the mean, mode, or median of the feature with replace it with missing values. This is an approximation that can add variance to the dataset.

Step 5: Encode categorical items

The next step is to convert categorical features into numeric features. The machine learning model can only work with numbers, not strings. Before proceeding, you should distinguish between two types of categorical variables: nonordinal variables and ordinal variables.

Step 6: Divide the data set into training and test sets

It’s time to split the dataset into three fixed subsets: the most common way is to use 60% for training, 20% for validation, and 20% for testing. As the amount of data increases, while the percentage for validation and testing decreases. Having three subsets is important because the training set is used to train the model, while the validation and test sets can be useful for understanding how the model performs on new data.

Step 7: Feature Scaling

There are machine learning models, such as Linear Regression, Logistic Regression, KNN, Support Vector Machine, and Neural Networks, which require the use of scaling features. Attribute scaling only supports the variables to be within a range, without changing the distribution. There are three popular types of feature scaling: Normalization, Standardization, and Robust scaling.

Conclusion: Follow these steps, and you’ll be well prepared to clean and pre-process your data in Python, setting the stage for more complex analytics or machine learning prototypes Change methods based on the nature of your dataset and the specific goals of your project.