Machine learning a subset of Artificial Intelligence is a very vast and statistical calculations oriented field where one needs to be not only good in statistics but also good in visualizing the data as well as preprocess the same. For carrying out Machine learning-related activities many researchers and scientists use various methods like using handwritten statistical algorithms or carrying out the same using Excel and various programming languages.
One such programming language that is gaining popularity in terms of ML is undoubtedly Python. This is an Object-Oriented Programming Language that has many built-in as well third party libraries which help in carrying out Data Analysis as well as Machine Learning very easily. This is because the necessary algorithms required for this task are already embedded in these libraries, and one just needs to call the same, and their work will be done within few minutes.
Due to its efficiently carrying out ML activities Python is gaining huge popularity in the market and is getting extensively used by many Data Scientists. Many top organizations are also giving huge packages to Python programmers in comparison to R, Scala, Java programmers. So, let’s learn which are the libraries that are generally needed for carrying out ML and Data Analysis activities:
Libraries that are generally used to carry out ML and Data Analysis activities
Pandas is one of the major libraries that is required by every Data Scientist and Analyst. This library contains various features like importing the necessary file we want to work with say for eg. CSV, Xls, xlsx, tsv, etc. After importing the necessary dataset the other things we can perform with this library are checking the data type of the columns in the dataset and then switching the column data type as per our choice that is from categorical to numeric or float, Boolean. After the column switching is done we can do a lot of things like interpolating the null values in the dataset or dropping the null values, filling the null values, transposing the columns, concatenating various datasets, merging the datasets, etc. This is a very powerful library and is considered much better than Pyspark for Machine Learning.
This is yet another powerful library that is used by Data Scientist; the full form of this library is Numeric Python. This library helps in solving various calculations related problems and converting the dataset into standard distribution, Gaussian distribution, shuffling the dataset, converting the data type of the columns, and much more. This library also helps in creating dummy datasets by using random integers, linspace, random numbers, etc. This library also allows users to save their data into .npz format which then can be used for further calculations rather than writing the whole code again and again. Many other functions are there that can be performed with this library and for proper documentation, you can visit the official website of Numpy which is numpy.org.
A powerful library generally used for data visualization that is creating various graphs to generate the trend analysis in our data. Matplotlib library is the most preferred library when solving various Kaggle, Hackathon competitions as well as solving real-world cases. The main good thing about this library is it is swift, fast and the graphs are generated on the screen within seconds. Some of the most common graphs that can be built using this library are Bar graph, Histograms (Probability Density), Pie charts, Scatter plots, Line Plots, Sine graphs, 3D graphs, etc. For a proper understanding of this library, you can visit the official website that is matplotlib.org.
This is another data visualization library that is a high-level API built on top of Matplotlib. It allows users to visualize their graphs in a very beautiful manner rather than using the old fashioned graphs. It also allows users to see the trend of their data by using various features like Hue, colors, and many more. The graphs built using this library come under second priority by the Data Scientists and researchers due to the same reason that is very fast.
Plotly, as its name suggests it also falls in the data visualization library category with high-level API. This library helps in visualizing the data more dynamically as it allows users to see various points by hovering over the graph, panning the screen, animating the graph by setting timers, cutting sections of the graph to see various fluctuations and many more. This library is used by the medical sector for visualizing the sections of the brain, cancers, pneumonia, and other diseases. The library is officially created by Plotly personnel and allows different types of data visualization graphs and glyphs like Scatter plots, Line plots, Sunburst plots, Bar plots, and many more. For more information visit the official website and read the documentation. The website link is plotly.com.
When it comes to carrying out Machine learning using Python, Scikit Learn would always strike the mind. This allows users to import all the necessary classification and regression algorithms and also allows users to do various feature engineerings related works like standardizing the data, normalizing the data, splitting the data into train, test, and validation, generating classification reports, getting the weights and biases of the data for regression-based problems, balancing the data either by downsampling or by upsampling and many more. This is the most preferred library by all the Data Scientist who works with Python and helps to solve maximum problems in the real world.
All the libraries mentioned above are pip installable through the Command Prompt and can be downloaded through pypi.org where the proper installation of these libraries is given or can be done through their official website. Also, for a better experience, you should use the Jupyter Notebook as it allows very nice data visualization within its console.
Use these libraries if you are concerned with carrying out ML and Data Analysis work through Python as these can help you in getting your results at a faster pace and also helps you to properly visualize your data as well as remove any kind of outliers from your data.