Data Pre-processing in Machine Learning

Summary:

Data pre-processing is an indispensable phase in constructing a machine learning model, serving as the mechanism to convert raw, unfiltered data into an effectively analysed format. Real-world data often comes riddled with inconsistencies like noise, missing values, and unstructured formats that can adversely impact the model’s accuracy and efficiency. To navigate these hurdles, the pre-processing pipeline generally involves several steps: acquiring the dataset, importing necessary libraries like Numpy, Matplotlib, and Pandas, importing the dataset into the code, treating missing values, encoding categorical data, and finally splitting the data into training and test sets. Different datasets require distinct pre-processing; for instance, business and healthcare datasets will vastly differ in structure and nature. Formats like CSV files are commonly used due to their adaptability and ease of use. Libraries like Scikit-learn offer tools to handle missing data, usually by deleting the row or replacing the missing value with the mean of that column. Categorical variables, which can’t be directly interpreted by machine learning algorithms, are converted into numerical values through encoding techniques such as LabelEncoder and OneHotEncoder. Subsequently, the data is partitioned into a training set to train the model and a test set to evaluate it. The aim is to ensure the model performs well on the data it is trained on and generalizes well to new data.

Excerpt:

Data Pre-processing in Machine Learning

Data Pre-processing in Machine learning

Data pre-processing prepares the raw data and makes it suitable for a machine learning model. It is the first and crucial step in creating a machine-learning model.

Why do we need Data Pre-processing?

Real-world data generally contains noises missing values and may be in an unusable format that cannot be directly used for machine learning models. Data pre-processing is a required task for cleaning the data and making it suitable for a machine learning model, which also increases the accuracy and efficiency of a machine learning model.

It involves the following steps:

• Getting the dataset
• Importing libraries
• Importing datasets
• Finding Missing Data
• Encoding Categorical Data
• Splitting dataset into training and test set
• Feature scaling