Top 10 Data Preparation Techniques to Use in ML Projects

Here are the 10 important techniques of data preparation that make your ML projects better

Data preparation is the process of cleaning and transforming raw data prior to processing and analysis so that data scientists and analysts can run it through machine learning algorithms to uncover insights or make predictions. It may be one of the most difficult steps in any ML project. ML depends heavily on data. It's the most crucial aspect that makes algorithm training possible and explains why machine learning became so popular in recent years. Here are some important techniques for ML projects.

Acquire the dataset: Firstly acquire the relevant dataset, to build and develop machine learning models. This will be comprised of data gathered from multiple and disparate sources which are then combined in a proper format to form a dataset.

Checking data quality: Machine-learning algorithms can't work with poor data. Data is collected or labelled by humans; checked for a subset of data and estimate how often mistakes happen. The issue of poor data quality is hindering organizations from performing to their full potential.

Import all the crucial libraries: Python libraries are important for data pre-processing in Machine Learning. The three core Python libraries used for this data pre-processing in Machine Learning are:

NumPy – It is the fundamental package for scientific calculation in Python.

Pandas – It is an excellent open-source Python library for data manipulation and analysis.

Matplotlib – It is a Python 2D plotting library that is used to plot any type of chart in Python.

Format data: Data formatting is sometimes referred to as the file format. And this isn't much of a problem to convert a dataset into a file format that fits the machine learning system. Format consistency of records themselves. These may be date formats, sums of money, addresses, etc. The input format should be the same across the entire dataset.

Data exploration: It is a process to analyze data to understand and summarize its main characteristics using statistical and visualization methods. It can also include opportunities to improve model performance, like reducing the dimensionality of a data set. Data visualization helps to improve the data exploration process.

Data structuring: Data-Structures is the concept used to store data efficiently, and algorithms around them allow us to write efficient and optimized computer programs. Data structuring in machine learning includes data reduction, through techniques such as attribute or record sampling and, data normalization, which includes dimensionality reduction.

Data cleansing and validation: This technique can help analytics teams identify and rectify inconsistencies, outliers, anomalies, missing data, and other issues. A wide range of commercial and open-source tools can be used to cleanse and validate data for machine learning and ensure good quality data.

Join transactional and attribute data: Transactional data consists of events that snapshot specific moments. Attribute data is more static, like user demographics or age, and doesn't directly relate to specific events. You may have several data sources or logs where these types of data reside. These both enhance each other to achieve greater predictive power in ML projects.

Rescale data: Data rescaling belongs to a group of data normalization procedures that aim at improving the quality of a dataset by reducing dimensions and avoiding the situation. Scaling of the data makes it easy for a model to learn and understand the problem. Scaling of the data comes under the set of steps of data pre-processing when we are performing machine learning algorithms in the data set.

Data discretization: Discretization refers to the process of converting or partitioning continuous attributes, features, or variables to discretize. Many machine learning algorithms prefer or perform better when numerical input variables have a standard probability distribution. The discretization transform provides an automatic way to change a numeric input variable to have a different data distribution, which in turn can be used as input to a predictive model.