Data is the fuel of machine learning, but not all data is created equal. To make the most of your data, you need to understand its characteristics, quality, and limitations. In this article, we will explore some of the best practices and tools for data understanding in machine learning, and how they can help you improve your models and results.
Data exploration is the process of getting a first impression of your data by examining its structure, size, distribution, and relationships. You can use various methods and techniques, such as summary statistics, histograms, scatter plots, correlation matrices, and dimensionality reduction, to gain insights into your data. Data exploration can help you identify patterns, outliers, anomalies, and missing values, as well as formulate hypotheses and questions for further analysis.
Having domain knowledge is a must to understand the data much data, using python and statistics, we just get the trends and patterns but with domain knowledge, we can understand why that trend and pattern occur and whether is it good or bad for the data. So before exploring data, get to know more about the domain from which that data come.
Data cleaning is the process of fixing or removing errors, inconsistencies, and duplicates in your data. Data cleaning can improve the accuracy, reliability, and usability of your data, as well as reduce the risk of bias and noise in your models. You can use various methods and techniques, such as data validation, data transformation, data imputation, and data normalization, to clean your data. Data cleaning can help you prepare your data for further processing and modeling.
Yes, Data Cleaning is very necessary for a data science project, as per my experience I did very in-depth data cleaning from handling null values to removing junk data from the column. Sometimes, Data need to be clean not only null values or junk values but from issues like noise and bias so having knowledge about statistics is must to remove bias and noise in data.
Data visualization is the process of presenting your data in a graphical or pictorial form to communicate its meaning and significance. Data visualization can enhance your data understanding by revealing trends, patterns, correlations, and outliers that might not be obvious from numbers or tables. You can use various methods and techniques, such as charts, graphs, maps, dashboards, and interactive tools, to visualize your data. Data visualization can help you explore, analyze, and communicate your data to different audiences and purposes.
Data preprocessing is the process of transforming your data into a suitable format for machine learning algorithms. Data preprocessing can improve the performance, efficiency, and generalization of your models, as well as reduce the complexity and dimensionality of your data. You can use various methods and techniques, such as feature engineering, feature selection, feature scaling, encoding, and sampling, to preprocess your data. Data preprocessing can help you optimize your data for machine learning tasks and objectives.
Data quality assessment is the process of evaluating your data based on various criteria, such as accuracy, completeness, consistency, timeliness, and relevance. Data quality assessment can help you measure the impact of your data understanding efforts, as well as identify areas for improvement and potential risks. You can use various methods and techniques, such as data quality metrics, data quality rules, data quality reports, and data quality audits, to assess your data quality. Data quality assessment can help you ensure that your data meets your expectations and requirements.
Data understanding is not a linear or one-time process, but rather an iterative and continuous cycle that involves different stages and activities. You can use a data understanding framework, such as CRISP-DM (Cross-Industry Standard Process for Data Mining), to guide and organize your data understanding process. A data understanding framework can help you define your goals, plan your actions, execute your steps, evaluate your results, and document your findings. A data understanding framework can help you manage and improve your data understanding process.
For the field of Natural Language Processing, Data Pipelines are extremely important! You could be leveraging the state of the art NLP architectures and models, but if your data isn't sanitized, then the performance can be sub par and unsatisfactory! Understanding your data, and data cleaning should be the first steps for any NLP Machine Learning Exercise. For this, domain expertise can play a major role, for it helps you better understand the nuances in your data. Another important aspect is ensuring your training data & the real world inference data are following the same distributions. A lot of times, we train on an ideal set of data & the inferencing data is very different, which leads to poor results. Your data decides performance!