The amount of data being generated these days is huge. Companies and Businesses deal with a lot of data and users also generate large amounts of data. In today’s digital era, the most important asset for any company or business is data. Data has also been referred to as the next “Oil”. This data revolution in recent times has fueled many changes. Organizations have to make changes to the way they operate and become more data-oriented and make data-driven decisions. Making data-driven decisions helps organizations to stay ahead of the curve. Proper analysis and predictions from data can show patterns and insights which were not available before. Using Machine Learning for Data Science processes is very important and let us see how we can implement them.
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What is Data Science?
Data Science is a multidisciplinary field that concerns extracting insights from data, understanding patterns in data, performing analysis and arriving at predictions and conclusions. Digital Data, in the 21st century, carries huge importance. Data has immense uses in today’s world. This data can be your Amazon purchase history, your tweets on Twitter, your Facebook posts, Google search history and so on. Amazon can use your past purchase history to recommend you new products, companies can use Twitter Tweet analytics to understand public sentiment and general happenings, your Facebook check-ins can be used by Facebook to show you targeted ADs and Google can do the same using your Google search history. Data can be used to generate actionable insights with vast importance and use.
The overall data science process is quite vast and involves many steps and usually is implemented by various teams working on different aspects.
- First, data is collected. Data collection is done using Data Mining, Data acquisition and Data Extraction. Often, data entry has to be done in cases where data has to be gathered manually from a source.
- Next, the data is maintained using Data Warehousing and Data processing. After the data is ready for use, the data is analyzed and processed.
- Machine Learning methods and Predictive Analytics can be performed on the data to arrive at a conclusion.
- Data Science methods, if implemented properly can deliver breakthrough insights and help in making business decisions.
- Data Science, Machine Learning, Artificial Intelligence are closely related to each other and a good understanding of these concepts is important for anyone who wants to enter the industry.
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What is Machine Learning?
Machine Learning is the process of training computers with past data to make accurate predictions on the basis of that data. Machine Learning is basically a subset of Artificial Intelligence.
In Machine Learning, computer systems learn from the data available and make informed predictions. Machine Learning models “train” from the data provided to them and arrive at a conclusion. Machine Learning Algorithms improve themselves with more data provided to them. The accuracy of ML models is usually directly proportional to the number of data samples used in their training, the more the data samples, the more is the accuracy of Machine Learning models.
How are Machine Learning and Data Science Related?
Machine Learning deals with programming Machines to learn from their experiences, whereas Data Science deals with inference, analysis and prediction from structured and unstructured data. Data Science and Machine Learning are closely related, and Machine Learning actually falls under the whole Data Science process. The main difference, however, is that Data Science is the much broader aspect of dealing with Data and Machine Learning is a branch of Artificial Intelligence where Data-Driven programs are made better at making predictions without the need for explicit programming. The whole Data Science process deals with steps like Data Analytics, Machine Learning, Predictive Analytics, Cluster Analysis, Natural Language Processing etc.
Data Science is the broad way of extracting insights from data and Machine Learning is a method of making predictions based on data.
These predictions are done by developing algorithms which take data and leverage statistical models to give an output. These algorithms are also updated as new data is added. Data Science is the overall process of understanding the data and making decisions based on the data. That is why, in Data Science, we deal with the origin of data, and how the data can be used as a valuable resource. Proper usage of data in making informed decisions can help Businesses and Enterprises in:
- Gaining and entering new markets.
- Gain a competitive advantage over competitors
- Increase business efficiency and reduce running costs.
- Retain customers and improve revenue per customer.
So, we can understand that an important part of Data Science, is making informed predictions and arriving at Conclusions. Machine Learning can be an important part of Data Science for this. Machine Learning algorithms are able to find patterns in data and give near accurate predictions. Making efforts towards Data Science, Machine Learning, especially, in learning them, will help in the long run.
How Machine Learning fits in the Data Science Lifecycle
The Data Science life cycle involves a lot of steps. Machine Learning fits within the whole Data Science lifecycle. The data science lifecycle will involve Data Gathering, Data Mining, Visualizations, Pattern Recognition, Statistical Analysis, Machine Learning etc.
The whole process starts with Business understanding and ends with a Model deployment to solve a problem, predict something or give the desired solution.
Machine Learning comes into play after the Data Preparation and Exploratory data analysis is done. After these steps, Data Science teams generally arrive at a conclusion of how to train the Machine Learning models. In this step, the most appropriate type of model is taken and the model is trained with the data. The Machine Learning model is supposed to take inputs and provide appropriate outputs. The data science life cycle takes into consideration that with time, the business requirements might change, and new data will also be available. In that case, the Machine Learning model will be fed the new data and the model “learns” from the new data as well. The modelling process involves training the machine learning model, making sample predictions and also calculating the accuracy on the basis of test data. The model hyperparameters must be tuned to get the optimal model which can deliver results according to our needs. The appropriate model usually varies according to the business case and data available at hand. Let us have a look at the various machine learning algorithms commonly used.
Machine Learning Algorithms
Machine Learning commonly uses two types of techniques:
- Supervised Learning
- Unsupervised Learning
Supervised Learning is based on training the model with data and getting predictions on the basis of that data. The model trains on known input and output data. The training process can be controlled, and the model once trained can be used to generate reasonable predictions when fed with new data. Supervised learning methods are used when we have the data available for the output we want to predict. Supervised learning methods basically map a function between the input and outputs. In supervised learning methods, machines basically learn by example. For this reason, the datasets must be huge in the case of Supervised learning problems.
Example problems of Supervised Learning are Classification and Regression.
Some examples of algorithms which can be used for Supervised Learning are:
Linear Regression, Random Forest Regression, Logistic Regression, Decision Tree Classifier etc
Regression is the type of supervised Machine Learning algorithm that can be used to predict continuous data. Regression is the process of finding relations between independent and dependent data. Regression is done by mapping the independent variables (x) to their dependent variables (y). Regression can be used to perform tasks like predicting the rainfall, estimating prices of property, estimating stock prices and so on. Regression algorithms are typically used when we are dealing with a data range of real numbers.
Independent vs Dependent variable
The term "independent variable" means precisely what it says. It's a stand-alone variable that isn't affected by the other variables you're attempting to track. A dependent variable is dependent on a variety of things.
Let us say, we are trying to understand the salary of a person on the basis of his/her years of experience in a job. So, we shall be having data of salary and years of experience. In this case, the years of experience in the job will be independent variable, and the salary will be the dependent variable, which depends on the years of experience.
Commonly used Regression Algorithms are:
Linear Regression, Polynomial Regression, Random Forest Regression, Support Vector Regression etc.
Classification is the supervised Machine Learning algorithm that can be used to predict discrete data points or discrete outcomes. Examples are, predicting if a mail is spam or important, predicting if a credit card transaction is genuine or fraudulent and so on. Classification can also be used to perform more complex tasks as well, like image classification, predicting if a person has health risks or not etc. Classification algorithms have immense use in the medical, banking and financial sectors. Classification algorithms are to be used if the data can be categorized, tagged and grouped into classes. In classification, the algorithm maps the input features to a discrete output which is the class.
Commonly used Classification Algorithms are:
K-Nearest Neighbours, Support Vector Machines, Kernel SVM, Naïve Bayes, Decision Tree Classification etc
|The target variable in Regression must be continuous, ie it must be in a range of real numbers.||The target variables in Classification are discrete. The target variable can be simple like Spam Mail or Useful Mail, or it can be complex like 4 or 5 types of Real Estate properties.|
|Regression algorithms can be divided into Linear and Non Linear algorithms||Classification Algorithms can be divided into Binary Classifiers and Multi-class Classifiers.|
|Regression problem requires the prediction of a quantity from input data.||In classification problems, data is predicted into one of two of many discrete classes.|
Unsupervised Machine Learning Algorithms
Unsupervised Machine Learning algorithms deal with finding patterns and relations in data. They are used to find inferences from data and arrive at a conclusion. In case of such problems, the input data is not labelled and does not have a known result. In unsupervised ML problems, a model is prepared by identifying structures in data. Unsupervised ML algorithms do not have any labelled input data.
Clustering the most commonly implemented Unsupervised Machine Learning algorithm. Clustering algorithms are typically based on modelling approaches, which may be centroid based or hierarchical. These algorithms are designed to group data into the types of most commonality.
Clustering can be used for Market Research, Customer Segmentation, Market Basket Analysis and so on. The core aspect of clustering is that the algorithm tries to group data points by similarity.
Some Clustering Algorithms are:
K-Means, K-Medians, Hierarchical Clustering etc
Use cases of Machine Learning:
Credit Card Fraud Detection
Machine Learning algorithms are widely used in the financial world. One of the important uses is credit card fraud detection. This can be done on the basis of past financial transaction data on the card, type of purchase, location of the transaction and so on. A vast amount of data is needed for this purpose. This is a classification problem where the result would be determining if a transaction is genuine or fraudulent.
Predicting Weather parameters:
A very interesting use of Machine Learning is predicting weather parameters like rainfall etc. Rainfall (in cm) at a particular place can be calculated on the basis of past data like temperature, humidity, solar insolation, wind speed and so on. This would qualify as a regression problem.
Malware code usually remains similar, with very minute variations. With the help of Machine Learning methods, we can accurately predict which files are malware with sufficient accuracy. Machine Learning models can also be used to predict anomalies in data security and predict data breaches
Many E-commerce websites and travel/film tickets booking websites use Dynamic pricing. Dynamic pricing is based on ticket availability and ticket demands. This causes variation in prices of tickets with time. Travel and Booking companies have a set of rules to predict dynamic prices, and they implement Machine Learning algorithms to set the prices.
Machine Learning and Data Science go hand in hand in solving many real-life problems with a data-driven approach. Data Science is a broad term used to describe the various steps involved in dealing with data, analysing data and making predictions from that data using Machine Learning. There are a wide variety of machine learning algorithms, each of which can be used to solve a particular problem. Machine Learning methods use training data to arrive at a result for new data. Machine Learning with Data Science can be used in various industries to cut costs and improve productivity and problem-solving capacity in various sectors. Machine Learning is basically, one of the tools in the arsenal of a Data Scientist. The advantages of using Data Science and Machine Learning are vast, and with a proper strategy, they can make miracles happen.