Data preprocessing is the predominant and one of the most crucial steps to undertake any data science project. A standardization of dataset must be attained to use it in any machine learning estimator. All the features must be normalized to achieve the consistency that is in proposition with the model to be used. The data must be manipulated as per the needs of the results that are expected from the predictions. These tools help in not only pre-processing of data but also to remove redundant data to extract information from data.
Pandas is one of the most used python-based libraries available for data manipulation and preprocessing. It is extensively used in data science projects of all domains by the professionals of all levels. It is the best python based library available for managing and processing the datasets. Along with the processing of data, missing data can also be handled with ease using Pandas. Pandas can be used by anyone with prior experience in basic python programming.
RapidMiner is a fantastic cloud-based platform that is used in various applications such as machine learning, data segregation, and data processing. RapidMiner is very easy to use and a person requires no programming expertise to use it. Professionals can also easily develop predictive models and even deploy them on this tool. However, its main purpose is to deal with data mining and also readying the data for further modeling purposes.
3. R Studio
This is arguably the best available tool for data manipulation and visualization that uses R programming. R Studio is capable of handling tasks such as data manipulation, data analytics, predictive analysis. Another purpose is that it reduces our work of sort and manipulate data with only a couple of lines of code.
Data cleansing also comes handy with this tool as packages are already available and can be downloaded onto the platform. R programming language is the best way to analyze or even predict the data. Many advanced level packages can be included in the programming script to develop interactive charts and to preprocess the data to the full extent. R Studio is a very dynamic and feature-rich tool.
Orange is an easy to use great open-source tool for data visualization, data mining, data analysis, etc. It is widely used amongst beginners for data processing for any particular application. Orange also provides hands-on learning methodologies to understand the working of the platform intended for the best use. Numerous external functions are also present that are used in achieving a well processed and clean data. it is also ideal for mining crucial information from a dataset.
Apache OpenNLP is a widely used commodity in developing many NLP projects. Preprocessing is one of them. Apache is a highly advanced tool for NLP development as it provides top of the line features for data manipulation. OpenNLP helps to remove the noise as well as enhance the data for its optimal use in the modeling. Some of the other services provided by Apache OpenNLP include text tokenization, sentence segmentation, parsing, etc., all of which cater to the processing of the dataset.
NLTK or The Natural Language Toolkit is a perfect tool for processing the Natural Language-based datasets. NLTK is used for text processing as well as developing NLP applications in machine learning. NLTK can be used to write programs in python that provide excellent options for textual data pre-processing and manipulation. It can be used in applications such as speech recognition, sentiment analysis, Chatbots, etc. NLTK is the perfect library to mine useful information from the data available. With a rich variety of NLTK packages, numerous tasks such as data stemming, classification, and analysis can also be performed.
Scikit Learn is an advanced scientific library that is also quite effective in pre-processing the datasets. This library provides advanced features to process data as per the need of a business problem. It also contains inbuilt functions for segregating the dataset into training and testing purposes in machine learning. The data scaling option present in Scikit Learn is also very crucial in predictive modeling. It has its backend based on a python programming language so; a basic knowledge of python is a must to get started with the Scikit Learn.
Preparation and the extraction of information from a dataset is the most important aspect of any data science related business. These data mining tools and libraries are the top choices available that can be used for mining and processing of data.