Data Preparation and Preprocessing in ML
- [Cornell University]
- Overview
Data preparation (also referred to as “data preprocessing”) is the process of transforming raw data so that data scientists and analysts can run it through machine learning (ML) algorithms to uncover insights or make predictions.
Data preparation is the process of taking raw data and making it ready for analysis or use in operations, while data preprocessing is the process of refining and transforming data after it's been acquired and integrated.
Data preprocessing, a component of data preparation, describes any type of processing performed on raw data to prepare it for another data processing procedure. It has traditionally been an important preliminary step for the data mining process. More recently, data preprocessing techniques have been adapted for training ML models and AI models and for running inferences against them.
Data preparation's main goal is to ensure that the data is accurate and consistent. This can be challenging because data is often created with errors, missing values, or different formats.
Data preprocessing's goal is to remove unwanted data so that the final dataset contains more valuable information. This can be done through data cleaning, which involves techniques like removing duplicates, handling missing values, and converting data types.
- The Data Science Process
A considerable chunk of any data-related project is about data preprocessing and data scientists spend around 80% of their time on preparing and managing data. The data science process is a dynamic and iterative process that transforms raw data into valuable insights.
Explore the data science process, from data collection and cleaning to modeling and analysis, to extract valuable insights and drive informed decisions.
Companies can use data from nearly endless sources - internal information, customer service interactions, and the entire Internet - to help them make choices and improve their businesses.
But you can't simply take raw data and run it through ML and analytics programs right away. You first need to preprocess the data so that a machine can successfully "read" or understand it.
Data preprocessing is a step in the data mining and data analysis process that takes raw data and converts it into a format that computers and machine learning can understand and analyze. Raw real data in the form of text, images, videos, etc. is messy. Not only can it contain bugs and inconsistencies, but it is often incomplete and has no regular, uniform design.
Machines love to process nice and neat information - they read data as 1s and 0s. Therefore, calculating structured data such as integers and percentages is easy. However, unstructured data in the form of text and images must first be cleaned and formatted before analysis.
- Data Preparation - Turns Insights into Action
Big data and data science are only useful if the insights can be turned into action, and if the actions are carefully defined and evaluated. Interpreting data refers to the presentation of your data to a non-technical layman.
Data preparation is the process of preparing raw data to make it suitable for further processing and analysis. Key steps include collecting, cleaning, and labelling raw data into a form suitable for machine learning (ML) algorithms, and then exploring and visualizing the data. Data preparation can take up to 80% of the time spent on an ML project. Using specialized data preparation tools is important to optimize this process.
Data preparation is a very important part of the data science process. In fact, this is where you will spend most of your time on any data science effort. It can be a tedious process, but it is a crucial step. Always remember, garbage in, garbage out. If you don't spend the time and effort to create good data for the analysis, you will not get good results no matter how sophisticated the analysis technique you're using is
- Data Preprocessing
Data preprocessing is the method of analyzing, filtering, transforming, and encoding data so that machine learning algorithms can understand and process the processed output.
Algorithms that learn from data are simply statistical equations that operate on values in a database. So, as the saying goes, "garbage in, garbage out." Your data project can only be successful if the data fed into the machine is of high quality.
Noise and missing values are always present in data extracted from real-life scenarios. This happens due to manual errors, unexpected events, technical issues, or various other obstacles.
Algorithms cannot consume incomplete and noisy data because they are generally not designed to handle missing values and the noise destroys the true pattern of the samples. Data preprocessing aims to solve these problems by thoroughly processing the data at hand.
- Feature Engineering
Feature engineering is a machine learning (ML) technique that involves transforming numeric representations of raw data into formats for ML models. It can improve the performance of a ML model by:
- Simplifying and speeding up data transformations
- Enhancing model accuracy
- Making certain algorithms converge faster
- Leading to better model performance
Some examples of feature engineering techniques include:
- Feature creation: Generating new features based on domain knowledge or by observing patterns in the data.
- Imputation: Managing missing values, which is one of the most common problems when it comes to preparing data for ML.
- Normalization: Bringing all the values on to the same scale so that the performance of the model will be improved.
- Handling missing data in your input features
- Using one-hot encoding for categorical data
- Considering feature scaling
- Creating interaction features where relevant
- Removing irrelevant features
- Data Labeling for Machine Learning
Data labeling for machine learning (ML) is the process of creating datasets for training ML models. In ML, data labeling is the process of identifying raw data (images, text files, videos, etc.) and adding one or more meaningful and informative labels to provide context so that ML models can learn from it.
For example, tags might indicate whether a photo contains a bird or a car, which words were said in an audio recording, or whether an X-ray contained a tumor. Data labels are required for a variety of use cases including computer vision, natural language processing, and speech recognition.
[More to come ...]
