Data cleaning is the process of ensuring data is correct, consistent, and usable. You can clean data by identifying errors or corruptions, correcting or deleting them, or manually processing data as needed to prevent the same errors from occurring.
The quality of data will have a huge impact on the quality of the model. Data preprocessing is a technique to convert the data collected into a clean dataset. It is one of the vital and important steps in building a machine learning model. It is also known as data wrangling or data cleaning.
Some datasets contain missing values, duplicate values, or incorrect data. In those cases, remove a specific row that has a null value for a feature or a particular column where the values are missing. Or calculate the mean of a particular row that contains a missing value and replace the result for the missing value. Most datasets have a large number of features. Which increases planes, it is difficult to model and visualize. The volume of data is reduced by methods like Principal Component Analysis (PCA) and SVD.
Some of the reasons why pre-cleaning steps are important to complete prior to data cleaning are as follows
- Using tools to pre-clean the data will make it more efficient as we can able quickly get what you need from the data available.
- It removes major errors and inconsistencies that are inevitable when multiple sources of data are being pulled into one dataset.
- It allows for mapping different data functions, a better understanding of what the available data is intended to do, and learning where it is coming from.
Also Read:
- Flower classification using CNN
- Music Recommendation System in Machine Learning
- Top 15 Python Libraries For Data Science in 2022
- Top 15 Python Libraries For Machine Learning in 2022
- Setup and Run Machine Learning in Visual Studio Code
- Diabetes prediction using Machine Learning
- 15 Deep Learning Projects for Final year
- Machine Learning Scenario-Based Questions
- Why are pre-cleaning steps important to complete prior to data cleaning?
- OpenRefine
- What does the attribute “Veracity” imply in the context of Big data?
- What does the attribute “Value” imply in the context of Big data?
- Is it difficult to be absolutely certain about the Big data?
- What does the attribute “Velocity” imply in the context of Big data?
- Customer Behaviour Analysis – Machine Learning and Python
- NxNxN Matrix in Python 3
- 3 V’s of Big data
- Naive Bayes in Machine Learning
- Automate Data Mining With Python
- Support Vector Machine(SVM) in Machine Learning
- Convert ipynb to Python
- Data Science Projects for Final Year
- Multiclass Classification in Machine Learning
- Movie Recommendation System: with Streamlit and Python-ML
- Getting Started with Seaborn: Install, Import, and Usage
- List of Machine Learning Algorithms
- Recommendation engine in Machine Learning
- Machine Learning Projects for Final Year
- ML Systems
- Python Derivative Calculator
