BloggerOn
Python Language And Its Application
Python Language is very versatile and can be utilized for web development, applications, data analytics, etc. One of the key applications of Python is in Machine Learning and a broad way to understand the data and identify the hidden patterns with due processing of the underlying data. So to speak, there is a multitude of usage of Python, in this article, we will be understanding how we can make use of public Python libraries and make efficient data analysis and apply machine learning techniques with Python.
Machine Learning
Before we move forward, we need to understand the terminology of what is Machine Learning. If we have a 10000ft top view, artificial intelligence is a science and technique where machines have the intelligence to discern and apply intelligence. Under this umbrella, we have a subset where we adapt a few techniques to make the machine understand the pattern and conducive for the machine to learn. These techniques range from a gamut of regression, classification, or clustering and what we call machine learning.
Python Libraries – Pandas
As we have been mentioning that Python has efficient and easily re-usable libraries, we should be cognizant of these libraries and must familiarize ourselves with using them to have a seamless data analysis performed. Data is usually presented for analysis in several ways; integrating with a database on cloud infrastructure or on-premise virtual machines, read from a CSV, excel or text files, etc. While there are different techniques to read the data, if we assume that the data is provided in a CSV or excel sheet, we certainly can leverage on a very popular Python Library called ‘pandas’.
Pandas is very useful to load the data, scour through the data and glean to understand the data points present. It is very powerful that with the assistance of pandas, we can create a data-frame and assign columns for independent variables, we can work around the dataset to sort, apply a function on all data points, filter with respect to criteria or perhaps choose certain data points which adhere to a certain filter. Pandas is certainly the most used Python library and especially when we are dealing with Machine Learning with Python.
Data processing and Machine Learning
We know that machines need to learn based on the information we provide, but what differentiates the learning process by a machine is the quality of the data provided and the various methodologies to arrive at a clean and processed data. Processing data is very critical in examining the underlying structure and eventually leading to a proper result and subsequent comprehension of the results.
To understand the data pattern, we need to consider and carefully learn about the data and its types. Data Types can be integer, float, character or an object depending on the data that we deal with. A pertinent effort should be made to validate if there are any missing values and if there are what should be the methodology to be employed to either rectify them or a coherent decision to drop those rows should be made. A keen data-scientist primary job is to understand the distribution of the data, find the outliers and treatment of those outliers should be meticulously done as it has a very significant impact to the outcome of the analysis and it is not properly done, it leads much to the anguish of the analyst. The tenor of the solution entirely depends on the data quality and appreciable efforts has to be factored in to make the data as meaningful as possible and vouch that it contributes to diverse aspects to the data behavior.
Machine learning has many branches to study upon, but we would see the three most common usage of machine learning – regression, classification, and clustering. While the broader terminology used to differentiate the approach of machine learning is supervised and unsupervised methods. Supervised methods are where the machine is fed with a label indicating the outcome as part of a training set and the task is to predict the labels when fed a testing data set. The un-supervised methodology does not have any pre-set labels where the machine can learn, but it has to understand the entire data and construct a pattern that is not visible at first glance.
Regression is to arrive at a linear or nonlinear (polynomial, multinomial) equation to find the continuous value of the dependent variable with all the independent variables with the respective coefficient. Classification is a methodology to classify the data in two or more than two classes. Clustering methodology is to segment or cluster the data points based on their inherent patterns.
Importance of Statistics
We have already expressed the need to understand the data and to arrive at a ‘good’ data. Here comes the importance of Statistics and we need to be adept at applying statistics principles. The mathematics behind normalizing the data or comprehending the distribution, deriving the mean, median, and the mode, or treating the outliers in a specific way as to not hamper the ‘meaning’ of the data. Statistics is the backbone behind the machine learning application on the data.
