Data Science is the field of study in which large volumes of data are mined, analysed to build predictive models, and help the business in the process. The data used over here is often unstructured, and it’s huge in quantity. Such data which encompasses the definition of volume, velocity, veracity, and variety is known as Big Data.
Hadoop and Spark are two of the most popular open-source framework used to deal with big data. The Hadoop architecture includes the following –
HDFS – It is the storage mechanism which stores the big data across multiple clusters.
Map Reduce – The data are processed parallel in the form of programming model known as Map Reduce.
Yarn – It manages resources required for the data processing.
Ozzie – A scheduling system to manage Hadoop jobs.
Mahout – For Machine Learning operations in Hadoop, the Mahout framework is used.
Pig – Executes Map Reduce programs. It allows the flow of programs which are higher level.
Flume – Used for streaming data into the Hadoop platform.
Sqoop – The data transfer between Hadoop Distributed File System, and the Relational Database Management system is facilitated by Apache Sqoop.
Hbase – A database management system which is column oriented, and works best with sparse data sets.
Hive – Allows SQL like query operations for data manipulation in Hadoop.
Impala – It is a SQL query engine for data processing but works faster than Hive.
As you can see there are numerous components of Hadoop with their own unique functionalities. In this article we would look into the basics of Hive and Impala.
Basics of Hive
Hive allows processing of large datasets using SQL which resides in the distributed storage. It is a Data Warehousing Tool which is built on top of the HDFS making operations like Data encapsulation, ad-hoc queries, data analysis, easy to perform.
The structure of Hive is such that first the tables, and the databases are created, and the tables are loaded with the data then after. It is platform designed to perform queries on only structured data which are loaded into the Hive tables. Unlike Map-Reduce, Hive has optimization features like UDFs which improves the performance. As Map-Reduce could be quite difficult to program, Hive resolved this difficulty, and allows to write queries in SQL which runs Map Reduce jobs in the backend. There is a Metastore in Hive as well which generally resides in a relational database.
Two of methods of interacting with Hive are Web GUI, and Java Database Connectivity Interface. There is a command line interface in Hive on which you could write queries using the Hive Query Language that is syntactically similar to SQL. Text file, Sequence file, ORC, RC file are some of the formats supported by Hive. The derby database is used for a single user storage metadata, and MYSQL is used for multiple user metadata.
Some notable points related to Hive are –
The Hive Query Language is executed on the Hadoop infrastructure while the SQL is executed on the traditional database.
Once a Hive query is ran, a series of Map Reduce jobs is generated automatically at the backend.
The bucket, and the partition concepts in Hive allows for easy retrieval of data.
The custom User Defined Functions could perform operations like filtering, cleaning, and so on.
Now, Hive allows you to execute some functionalities which could not be done in the relational databases. In production, it is highly necessary to reduce the execution time for the queries and thus Hive provides the advantage in this regard as the results are obtained in the second’s time.
There is a reason why queries are executed quite fast in Hive.
The Schema on Read and Write system in the relational databases allows one to create a table first, and then insert data into it. Thus insertions, modifications, updates could be performed over there. On the other hand, the Schema on Read only mechanism in Hive doesn’t allow modifications, updates to be done. The modifications across multiple nodes is not possible because on a typical cluster, the query is run on multiple data nodes. There is also a Read many write once mechanism in Hive where the tables could be updated in the latest versions after insertion is done.
The three core parts in Hive are – Hive Clients, Hive Services, Hive Storage and Computing.
To enable communication across different type of applications, there are different drives which are provided by Hive. The Thrift client is provided for communication in Thrift based applications. The JDBC drivers are provided for the java related applications. The ODBC drivers are provided for the other type of applications. In the Hive service, there is again communication between these drivers and the Hiver server.
The Hive Services allows client interactions. All operations in Hive are communicated through the Hiver Services before it is performed. The Hive service of the Data Definition Language is the Command Line Interface. The ODBC, JDBC, etc., is communicated by the drivers in the service.
Services such as file system, Metastore, etc., performs certain actions after communicating with the storage.
In Hive, the query is first executed through the User Interface, and then its metadata information is gathered after an interaction between the driver, and the compiler. The plan is created by the compiler, and the metadata request is obtained. The compiler receives the metadata information back from the Meta store and starts communication to execute the query. The Execution engine receives the execution plans from the Driver. The bridge between Hadoop and Hive is the engine which processes the query. The results are fetched from the driver and sent to the Execution Engine which would eventually send the results to the front end via the driver.
There are two modes – Local, and Map Reduce on which Hive could operate. The local mode used in case of small data sets, and the data is processed at a faster speed in the local system. In Map Reduce mode, there are multiple data nodes in Hadoop and used to execute large datasets in a parallel manner. A better performance on large data sets could be achieved through this. The Map Reduce mode is default in Hive.
The server interface in Hive is known as HS2 or the Hive Server2 where the query execution against the Hive is enabled for the remote clients. Authentication and concurrency for multiple clients are some of the advanced features included in the latest versions.
Basics of Impala
Impala is a parallel query processing engine running on top of the HDFS. Between both the components the table’s information is shared after integrating with the Hive Metastore. As Hive is mostly used to perform batch operations by writing SQL queries, Impala makes such operations faster, and efficient to be used in different use cases.
There are some changes in the syntax in the SQL queries as compared to what is used in Hive. The transform operation is a limitation in Impala. Distributed across the Hadoop clusters, and used to query Hbase tables as well.
The queries in Impala could be performed interactively with low latency.
Impala produces results in second unlike the Hive Map Reduce jobs which could take some time in processing the queries.
For real-time analytical operations in Hadoop, Impala is more suited and thus is ideal for a Data Scientist.
Reporting tools like Pentaho, Tableau benefits form the real-time functionality of Impala as they already have connectors where visualizations could be performed directly from the GUI.
All formats of files like ORC, Parquet are supported by Impala.
The parquet file used by Impala is used for large scale queries. In this format, the data is stored vertically i.e., the columnar storage of data. Thus the performance while using aggregation functions increases as only the columns split files are read. The encoding and compression schemes are efficiently supported by Impala. Impala could be used in scenarios of quick analysis or partial data analysis. Along with real-time processing, it works well for queries processed several times.
The architecture of Impala consists of three daemons – Impalad, Statestored, and Catalogd.
The Impalad is the core part of Impala which allows processing of data files and accepts queries with JDBC ODBC connections. The distribution of work across the nodes and the transmission of results to the coordinator node immediately is facilitated by the Impalad.
The Impala daemons availability is checked by the Statestored. The health of the nodes are continuously checked by constant communication between the daemons, and the Statestored. In case of a node failure, all other Impalad daemons are notified by the Statestored to leave that daemon out for future task assignment.
The metadata changed from DDL to other nodes are notified by the Catalogd daemon. Its configuration is required in a single host.
The Impalad takes any query requests, and the execution plan is created. Impalad communicates with the Statestored, and the hive Metastore before the execution.
Various built-in functions like MIN, MAX, AVG are supported in Impala. It also supports the dynamic operation. The VIEWS in Impala acts as aliases.
Conclusion
Big Data plays a massive part in the modern world with Hive, and Impala being two of the mechanisms to process such data. This article gave a brief understanding of their architecture and the benefits of each.
Dimensionless has several blogs and training to get started with Data Science.
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The analytics market is booming, and so is the use of the keyword – Data Science. Professionals from different disciplines are using data in their day to day activities, and feel the need to master the start-of-the-art technology in order to get maximum insights from the data, and subsequently help the business to grow.
Moreover, there are professionals who want to keep them updated with this latest skills such as Machine Learning, Deep Learning, Data Science, and so either to elevate their career or move to a different career altogether. The role of a Data Scientist is regarded as the sexiest job in the 21st century making it increasingly lucrative for most people to turn down.
However, making a transition to Data Science, or starting a career in it as a fresher is not an easy task. The supply-demand gap is gradually diminishing as more, and more people are willing to master this technology. There is often a misconception among professionals, and companies as to what Data Science is, and in many scenarios the term has been misused for various small scale tasks.
To be a Data Scientist, you need to have a passion, and zeal to play with data, and a desire to make digits and numbers talk. It is a mixture of various things, and there are a plethora of skills one has to master to be a called a Full Stack Data Scientist. The list of skills often gets overwhelming for an individual who could quit, given the enormity of its applications, and a continuous learning mindset the field of Data Science demands.
In this article, we would walk you through the ten areas in Data Science which are a key part of a project, and you need to master those to be able to work as a Data Scientist in much big organization.
Data Engineering – To work in any Data Science project, the most important aspect of it is the data. You need to understand which data to use, how to organize the data, and so on. This bit of manipulation with the data is done by a Data Engineer in a Data Science team. It is a superset of Data Warehousing and Business Intelligence which included the concept of big data in the context.
Building, and maintain a Data warehouse is a key skill which a Data Engineer must have. They would prepare the structured, and the unstructured data to be used by the Analytics team for model building purpose. They build pipelines which extract data from multiple sources and then manipulates it to make it usable.
Python, SQL, Scala, Hadoop, Spark, etc., are some of the skills that a Data Engineer has. They should also understand the concept of ETL. The data lakes in Hadoop is one of the key areas of work for a Data Engineer. The NoSQL database is mostly used as part of the data workflows. Lambda architecture allows both batch and real-time processing.
Some of the job role available in the data engineering domain is Database Developer, Data Engineer, etc.
Data Mining – It is the process of extracts insights from the data using certain methodologies for the business to make smart decisions. It distinguishes the previously unknown patterns and relationships from the data. Through data mining, one could transform the data into various meaningful structures in accordance with the business. The application of data mining depends on the industry. Suppose in finance, it is used in risk or fraud analytics. In manufacturing, product safety, and quality issues could be analyzed with accurate mining. Some of the parameters in data mining are Path Analysis, Forecasting, Clustering, and so on. Business Analyst, Statistician are some of the related jobs in the data mining space.
Cloud Computing – A lot of companies these days are migrating their infrastructure from local to the cloud merely because of the ready-made availability of the resources, and the huge computational power which not always available in a system. Cloud computing generally refers to the implementation of platforms for distributed computing. The system requirements are analyzed to ensure seamless integration with present applications. Cloud Architect, Platform Engineer are some of the jobs related to it.
Database Management – The rapidly changing data makes it imperative for the companies to ensure accuracy in tracking the data on a regular basis. This minute data could empower the business to make time strategic decisions, and maintain a systematic workflow. The collected data is used to generate reports and is made available for the management in the form of relational databases. The Database management system maintains a link among the data, and also allows newer updates. The structured format in the form of databases helps management to look for data in an efficient manner. Data Specialist, Database Administrator are some of the jobs for it.
Business Intelligence – The area of business intelligence refers to finding patterns in historical data of a business. Business Intelligence analysts would find the trends for a data scientist to build predictive models upon. It is about answering not-so-obvious questions. Business Intelligence answers the ‘what’ of a business. Business Intelligence is about creating dashboards and drawing insights from the data. For a BI analyst, it is important to learn data handling, and masters the tools like Tableau, Power BI, SQL, and so on. Additionally, proficiency in Excel is a must in business intelligence.
Machine Learning – Machine Learning is the state-of-the-art methodology to make predictions from the data, and help the business make better decisions. Once the data is curated by the Data Engineer and analyzed by a Business Intelligence Analyst, it is provided to a Machine Learning Engineer to build predictive models based on the use case in hand. The field of machine learning is categorized into supervised, unsupervised, and reinforcement learning. The dataset is labeled in supervised unlike in unsupervised learning. To build a model, it is first trained with data to let them identify the patterns and learn from it to make predictions on the unknown set of data. The accuracy of the model is determined based on the metric, and the KPI used which is decided by the business beforehand.
Deep Learning – Deep Learning is a branch of Machine Learning which h uses neural network to make predictions. The neural networks work similar to our brain and makes builds predictive models compared to the traditional ML systems. Unlike in Machine Learning, no manual feature selection is required in Deep Learning but huge volumes of data and enormous computational power is needed to run deep learning frameworks. Some of the Deep Learning frameworks like TensorFlow, Keras, PyTorch.
Natural Language Processing – NLP or Natural Language Processing is a specialization in Data Science which deals with raw text. The natural language or speech is processed using several NLP libraries, and various hidden insights could be extracted from it. NLP has gained popularity in recent times with the amount of unstructured raw text that’s getting generated from a plethora of sources, and the unprecedented information that those natural data carries. Some of the applications of Natural Language Processing are Amazon’s Alexa, Google’s Siri. Even many companies are using NLP for sentiment analysis, resume parsing, and so on.
Data Visualization – Needless to say, the importance of presenting your insights either through scripting or with the help of various visualization tools. A lot of Data Science tasks could be solved with an accurate data visualizations as the charts, and the graphs presents enough hidden information for the business to take relevant decisions. Often, it gets difficult for an organization to build predictive models, and thus they rely on only visualizing the data for their workflow. Moreover, one needs to understand which graphs or charts to use for a particular business, and keep the visualization simple, as well as informative.
Domain Expertise – As mentioned earlier, professionals from different disciplines are using data in their business, and thus its wide range of applications makes it imperative for people to understand the domain they are applying their Data Science skills. The domain knowledge could be operations-related where you would leverage the tools to improve the business operations that could be focused on financials, logistics, etc. It could also be sector specific such as Finance, Healthcare, etc.
Conclusion
Data Science is a broad field with a multitude of skills, and technology that needs to be mastered. It is a life-long learning journey, and with frequent arrival of new technologies, one has to update themselves constantly.
Often it could be challenging to keep up with some frequent changes. Thus it is required to learn all these skills, and at least be a master of one particular skill. In a big corporation, a Data Science team would comprise of people assigned with different roles such as data engineering, modeling, and so on. Thus focusing on one particular area would give you an edge over others in finding a role within a Data Science team in an organization.
Data Scientist is the most sort after job in this decade, and it would continue to be so in years to come. Now is the right time to enter this field, and Dimensionless has several blogs and training to get started with Data Science.
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Data Science is everyone’s word of the mouth in the current analytical eco-space. The study of Data Science which encompasses various subjects like Machine Learning, Deep Learning, Artificial Intelligence, Natural Language Processing, and so on has made tremendous advancement in the recent past.
Data Science is not something that emerged recently. It was there since computers were invented as the first Data Science application was classifying an email as Spam or Not Spam based on certain trends in the mail. However, the recent hype is a result of the massive amounts of data that are available, and the huge computational capacity that modern computers possess.
In terms of career, Data Science is considered as one of the most lucrative jobs in the 21st with salaries next to none. Hence, out of the curiosity to mine insights from the data, and also for a better career, professionals from various disciplines such as Healthcare, Physics, Marketing, Human Resource, IT, want to master the state-of-the-art Data Science methodologies.
To be called a Full Stack Data Scientist, one needs to master a plethora of skills as mentioned below.
Statistics and Probability – The first, and arguably the most important part of Data Science as various statistical methods are used to make assumptions from the data.
Programming – One needs to master at least one programming language out of Python, R, and SAS.
Machine Learning – To make predictions from the data, one needs to be aware of the several programmed algorithms, and understand their usage for the right application.
Communication – Extracting insights from the data are useless unless it is communicated in layman terms to the business and the stakeholders who would make crucial decisions based on your analysis.
Apart from these four basic skills, there are few other skills like building data pipelines are also important, but on most occasions, an organization would have a separate team for that.
Why Programming is Needed for Data Science?
In layman terms, Data Science is a process of automating certain manual tasks to mitigate the resource, budget, and time constraints. Thus learning to code is an important component to automate those tasks.
To build a simple predictive model, the data set should be first loaded and cleaned. There are several libraries, and packages available for that. You need to choose the language to code, and use those libraries for such operations. After the data is cleaned, there are several programmed algorithms which need to be used to build the predictive model.
Now, each algorithm is a set of a class which needs to be imported first, and then an object is created for that class which would use the methods or the functions associated with that particular class. Thus this entire process is a concept of Object Oriented Programming. Even, to understand the process behind the algorithms, one needs to be familiar with programming
Why R Programming is Used?
There is an ongoing debate about which is the best programming language for Data Science. It never harms to master all the three languages but one needs to be expert in a particular language, and understand its various functionalities in different situations.
The choice of language depends on interest, and how comfortable the person is to program in that language. Python is generally considered as the Holy Grail due to its simplicity, flexibility, and the huge community which makes it easier to find solutions to all sorts of problems faced during the building stage. However, R is not far behind either as people from different backgrounds other than IT, seems to prefer R, as their go-to language for Data Science.
R is an open-source programming language which is supported by the R Foundation and is used in statistical computing, and graphics. Like Python, it is easy to install and is better than SAS which however is high-level, and easy to learn designed additionally for Data Manipulation.
The graphical representations and the statistical computations of the data gives R an edge over Python in this regard. Additionally, the programming environment of R has input, and output facilities, and several user-defined recursive functions. In the early ’90s, R was first developed, and since then its interface has been improved with constant efforts. R has made an outstanding journey from being a text editor to R studio, and now to the Jupyter Notebooks which has intrigued all the Data Scientist across the world.
Below are some of the key reasons why R is important in Data Science.
Academic Preference – R is one of the most popular languages in universities, and it is the language that many researchers use for their experiments. In fact, in several Data Science books, all the statistical analysis is done in R. This academic preference creates more people with the proficiency in R. As more students study R in their undergraduate or graduate courses, it would help them perform statistical analysis in the industry.
Data Pre-processing – Often the dataset used for analysis requires cleaning to make it ideal for building a model which is a time-consuming process. R comes to the rescue in such cases as it has several libraries, and packages to perform data wrangling. Some of its packages are-
dplyr – One of the popular R package used for data exploration, and transformation.
table – Data aggregation is simplified with this package as well as the computational time to manipulate the dataset is reduced.
readr – This package allows to read the various forms of data ten times faster due to the non-conversion of characters into factors.
Visualization – R allows the visualization of various structured or tabular data in graphical form. It has several tools which perform the task of analysis, visualization, and representation. ggplot2 is the most popular package in R for data visualization. ggedit is another package which users the aesthetics of a plot are correct.
Specificity – The goal of the R language is to make data analysis simpler, approachable, and accurate. As R is used for statistical analysis, it enables new statistical methods through its libraries. Moreover, the supportive community of R makes which helps one to get all the required solution of a problem. The discussion forums of R is next to none when it comes to statistical analysis. More often than not, there is an instant response to any question posted in the community which makes helps Data Scientists in their project.
Machine Learning – Exploratory data analysis is the first step in an end-to-end Data Science project where the data is wrangled and analyzed to extract insights through visualization. The next step is to build predictive models with the help of that cleaned data to solve various business problems. In Machine Learning, one needs to train the model first where it could capture the underlying trends in the data, and then make a prediction on the unknown data. R has a list of extensive tools which simplifies the process of developing the model to predict future events. Few of those packages are –
MICE – It deals with missing values in the data.
PARTY – To create Data partitions, this package is used.
CARET – The classification and regression problems could be solved with the CARET package.
randomFOREST – To create a decision tree.
Open Source – The open source feature of R makes it suitable to be run on any platform such as Windows, Linux, Mac, etc. In fact, there is an unlimited scope to play around with the R code without the hassle of cost, limits, license, and so on. Apart from a few libraries which are restricted to commercial access, rest could be accessed for free.
All-in-one Package Toolkit – Apart from standard tools which are used for various data analysis operations like transformation, aggregation, etc., R has several tools for statistical models like Regression, GLM, ANOVA which are included in a single object-oriented framework. Hence, instead of copy, and paste, this feature allows to extract the required information.
Availability – As R is an open-source programming language with a huge community, it has a plethora of learning resources making it ideal for anyone starting out in Data Science. Additionally, the exploration of the R landscape makes it easier to recruit R developers. R is rapidly growing in popularity and it would scale up in the future. Various techniques such as time-series modeling, regression, classification, clustering, etc., could be practiced with R making it an ideal choice for predictive analytics.
There are several companies who have used R in their applications. For example, the monitoring of user experience in Twitter is done in R. Also, in Microsoft, professionals use R on sales, marketing, Azure data. To forecast elections, and improve traditional reporting, the New York Times uses R language. In fact, R is used by Facebook as well for analyzing its 500TB of data. Companies like Nordstrom ensures customer delight by using R to deliver data-driven products.
Conclusion
Data Science is the sexiest job of the 21st century, and it would remain so for years to come. The exponential increase in the generation of data would only allow more development in the Data Science field, and there could be a gap in supply-demand at a certain age.
As several professionals are trying to enter this field, it is necessary that they first learn to programme, and R is an ideal language to start off their programming journey.
Dimensionless has several blogs and training to get started with R, and Data Science in general.
Follow this link, if you are looking to learn more about data science online!
Additionally, if you are having an interest in learning Data Science, Learnonline Data Science Course to boost your career in Data Science.
Furthermore, if you want to read more about data science, you can read our blogs here
Data Science, Machine Learning, Deep Learning, and Artificial Intelligence are some of the most heard about buzzwords in the modern analytical eco-space. The exponential growth of technology in this regard has simplified our lives and made us more machine dependent. The astonishing hype surrounding such technologies has prompted professionals from various disciples to hop on to the ship and consider analytics as their career option.
To master Data Science or Artificial Intelligence in that regard, one needs a myriad of skills which includes Programming, Mathematics, Statistics, Probability, Machine Learning, and also Deep Learning. The most sort after languages for programming in Data Science is Python, and R with the former being regarded as the holy grail of the programming world because of its functionality, flexibility, community, and others.
Python is comparatively easy to master but given its importance, it has various usages which demand certain specific areas to be mastered more efficiently compared to others. In this blog, we would learn about the virtual environments in Python and how they could be used.
What is a Python Virtual Environment?
A python virtual environment is a tool which ensures the separation of resources, and dependencies of a project by creating separate virtual environments for them.
As the virtual environments are just directories running a few scripts, it ensures the creation of an unlimited number of virtual environments.
Why Do We Need Virtual Environments?
Python has a rich list of modules, and packages used for different applications. However, often those packages would not come in the form of a standard library. Thus to ensure the fixation of a common bus, an application might need a version of a library specific to it.
It is often impossible for a single installation of python to include the requirements of every application. A conflict would be created when two applications would need two different versions of a particular module.
In our system, by default, each and every application would use the same directory for storing, and retrieval of the site-packages which are the third party libraries. This kind of situation may not be a cause of concern for system packages but certainly is for site-packages.
To eliminate such scenarios, Python has the facility of creating virtual environments which would separate the modules, and packages needed by each application in its own isolated environment. It would also have a standard self-contained directory consisting of the version of the python installed.
Imagine a scenario where both project A, and project B has their dependencies on the same project C. Now, at this points everything might seem fine, but when project A would need version v1.0.0 of Project C, and project B would need v2.0.0 of the project C, then a conflict would arise as it’s not possible for Python to differentiate between the two different versions in the directory called site-packages. As a result, both the versions would have the same name in the same directory.
This would lead to both the projects using the same version which would not be acceptable in many cases in real life. Thus Python Virtual Environments and the virtualenv/tools come to the rescue in those cases.
Creating a Virtual Environment
Python 3 already has the venv module for creating, and managing the virtual environments. For Python 2 users, the virtual environment could be created using the pip install virtualenv command. The venv module would ensure the installation of the last version of python available. In case of having multiple versions, the specific version like python3 could be selected for the creation.
The selection of directory is the first step as it is the place where the virtual environment would be located. Once the directory is decided, the command – python3 -m venv dimensionless-env could be executed on it to create a directory named dimensionless-env if it didn’t exist before, and would also create several directories inside it which includes the Python interpreter, various files, the standard library, and so on.
Once the virtual environment is created, it needs to be activated using the below commands –
dimensionless-env\Scripts\activate.bat in the Windows operating system.
source dimensionless-env/bin/activate in the Unix or Mac operating system. The bash shell uses this script. For csh, or fish shells, there are alternate scripts that could be used such as activate.csh, and activate.fish.
The shell’s prompt would display the virtual environment that’s being used after its being activated. It would also modify the Python environment to get the exact version of Python, and its installation.
The creation of the virtual environment allows you to do anything like installing, upgrading or removing packages using the pip command. Let’s search for the package called astronomy in our environment.
(dimensionless-env) $ pip search astronomy
There are several sub-commands in pip like install, freeze, etc. The latest version of any package could be installed by specifying its name.
Often, an application needs a specific version of a particular package to be installed which could be accomplished using the == sign to mention the version number as shown below.
Re-running the same command would do nothing but to install the latest version from here, either the version name could be specified or the ‘upgrade’ keyword could be used as shown below.
To uninstall a particular package pip uninstall package-name command is used. In order to get detailed information about a particular package, the pip show command is used. All the installed packages in the virtual environment could be displayed using the pip list command.
(dimensionless-env) $ pip list
novas (3.1.1.3)
numpy (1.9.2)
pip (7.0.3)
requests (2.7.0)
setuptools (16.0)
The pip freeze command would also do the same task but in the format of pip install. Thus a generic notion is to put that in a requirments.txt file.
This requirements.txt file could be shipped and committed to allowing users making necessary installations using the install –r command.
What is Virtualenvwrapper?
Python virtual environments provide flexibility in the development, and the maintenance of our project as creating isolated environments allows projects to be separated from each other with the required dependencies for an individual project could be installed in that particular environment.
Though the virtual environments resolve the conflicts which arise due to the packages management, it is not completely perfect. Some problems often arise while managing the environment which is resolved by the virtualenvwrapper tool.
Some of the useful features of virtualenvwrapper are –
Organization – Virtualenvwrapper ensures all the virtual environments are organized in one particular location
Flexibility – It eases the process of creating, deleting, and copying environments by proving the respective methods for each.
Simplicity – There is a single command which allows switching between the environments.
The virtualenvwrapper could be installed using the pip install virtualenvwrapper command and then activating it either by running source or by executing the virtualenvwrapper.sh script. After the first installation using pip, the exact location of the virtualenvwrapper.sh would be known from the output of the installation.
How Python Virtual Environment is Used in Data Science?
The field of Data Science encompasses several methodologies which include Deep Learning as well. Deep Learning works with the principle of neural networks which is similar to the neurons in the human brain. Unlike the traditional Machine Learning algorithms, Deep Learning needs a huge volume of data, and vast computational power to make accurate predictions.
There are several Python libraries used for deep Learning such as TensorFlow, Keras, PyTorch, and so on. TensorFlow, which was created by Google is mostly used for Deep Learning operations. However, to work with TensorFlow in the Jupyter Notebook, we need to create a virtual environment first, and then install all the necessary packages inside that environment.
Once, you are into the Anaconda prompt, the conda create -n myenv python=3.6 command would create a new virtual environment known as myenv. The environment could be activated using the conda activate myenv command. The activation of the environment would let us install all the below necessary packages required to work TensorFlow.
conda install jupyter
conda install scipy
pip install –upgrade tensorflow
TensorFlow is used in applications like Object Detection, Image Processing, and so on.
Conclusion
Python is the most important programming language to master in the 21st century, and mastering it would open the door for numerous career opportunities. Its virtual environment feature allows to efficiently create, and manage a project, and its dependencies.
In this article, we learned that it’s not only about how virtual environments allows storing dependencies flawlessly but resolves various issues surrounding packaging, and the versioning in a project. The huge community of Python helps you find any tools needed for your project.
Dimensionless has several blogs and training to get started with Python Learning and Data Science in general.
Follow this link, if you are looking to learn more about data science online!
Additionally, if you are having an interest in learning Data Science, Learnonline Data Science Course to boost your career in Data Science.
Machine Learning is the word of the mouth for everyone involved in the analytics world. Gone are those days of the traditional manual approach of taking key business decisions. Machine Learning is the future and is here to stay.
However, the term Machine Learning is not a new one. It was there since the advent of computers but has grown tremendously in the last decade due to the massive amounts of data that’s getting generated, and the enormous computational power that modern-day system possesses.
Machine Learning is the art of Predictive Analytics where a system is trained on a set of data to learn patterns from it and then tested to make predictions on a new set of data. The more accurate the predictions are, the better the model performs. However, the metric for the accuracy of the model varies based on the domain one is working in.
Predictive Analytics has several usages in the modern world. It has been implemented in almost all sectors to make better business decisions and to stay ahead in the market. In this blog post, we would look into one of the key areas where Machine Learning has made its mark is the Customer Churn Prediction.
What is Customer Churn?
For any e-commerce business or businesses in which everything depends on the behavior of customers, retaining them is the number one priority for the organization. Customer churn is the process in which the customers stop using the products or services of a business.
Customer Churn or Customer Attrition is a better business strategy than acquiring the services of a new customer. Retaining the present customers is cost-effective, and a bit of effort could regain the trust that the customers might have lost on the services.
On the other hand, to get the service of the new customer, a business needs to spend a lot of time, and money on to the sales, and marketing department, more lucrative offers, and most importantly earning their trust. It would take more recourses to earn the trust of a new customer than to retain the existing one.
What are the Causes of Customer Churn?
There is a multitude of reasons why a customer could decide to stop using the services of a company. However, a couple of such reasons overwhelms others in the market.
Customer Service – This is one of the most important aspects on which business the growth of a business depends. Any customer could leave the services of a company if it’s poor or doesn’t live up to the expectations. A study showed that nearly ninety percent of the customer leave due to poor experience as modern era deems exceptional services, and experiences.
When a customer doesn’t receive such eye-catching experience from a business, it tends to lean towards its competitors leaving behind negative reviews in various social media from their past experiences which also stops potential new customers from using the service. Another study showed that almost fifty-nine percent of the people aged between twenty-five, and thirty share negative client experiences online.
Thus, poor customer experience not only results in the loss of a single customer but multiple customers as well which hinders the growth of the business in the process.
Onboarding Process – Whenever the business is looking to attract a new customer to use their service, it is necessary that the on-boarding process which includes timely follow-ups, regular communications, updates about new products, and so on are being followed, and maintained consistently over a period of time.
What are some of the Disadvantages of Customer Churn?
A customer’s lifetime value and the growth of the business maintains a direct relationship between each other i.e., more chances that the customer would churn, the less is the potential for the business to grow. Even a good marketing strategy would not save a business if it continues to lose customers at regular intervals due to other reasons and spend more money on acquiring new customers who are not guaranteed to be loyal.
There is a lot of debate surrounding customer churn and acquiring new customers because the former is much more cost-effective and ensures business growth. Thus companies spend almost seven times more effort, and time to retain old customers than acquire a new one. The global value of a customer lost is nearly two hundred, and forty-three dollars which makes churning a costly affair for any business.
What Strategies could a Business Undertake to prevent Customer Churn?
Customer Churn hinders or prevents the growth of an organization. Thus it is necessary that any business or organization has a flexible system in place to prevent the churn of customers and ensure its growth in the process. The companies need to find the metrics to identify the probability of a customer leaving, and chalk out strategies for improvement of its services, and products.
The calculation of the possibility of the customer churning varies from one business to another. There is no one fixed methodology that every organization uses to prevent churn. A churn rate could represent a variety of things such as – the total number of customers lost, the cost of the business loss, what percentage of the customers left in comparison to the total customer count of the organization, and so on.
Improving the customer experience should be the first strategy on the agenda of any business to prevent churn. Apart from that, marinating customer loyalty by providing better, personalized services is another important step one could undertake. Additionally, many organizations sent out customer survey time, and again to keep track of their customer experiences, and also seek reasons from them who have already churned.
A company should understand and learn about its customers beforehand. The amount of data that’s available all over the internet is sufficient to analyze a customer’s behavior, his likes, and dislikes, and improve the services based on their needs. All these measures, if taken with utmost care could help a business prevent its customers from churning.
Telecom Customer Churn Prediction
Previously, we learned how Predictive Analytics is being employed by various businesses to prevent any event from occurring and reduce the chances of losing by putting the right system in place. As customer churn is a global issue, we would now see how Machine Learning could be used to predict the customer churn of a telecom company.
Gender – Determines whether the customer is a male or a female.
Senior Citizen – A binary variable with values as 1 for senior citizen and 0 for not a senior citizen.
Partner – Values as ‘yes’ or ‘no based on whether the customer has a partner.
Dependents – Values as ‘yes’ or ‘no’ based on whether the customer has dependents.
Tenure – A numerical feature which gives the total number of months the customer stayed with the company.
Phone Service – Values as ‘yes’ or ‘no’ based on whether the customer has phone service.
Multiple Lines – Values as ‘yes’ or ‘no’ based on whether the customer has multiple lines.
Internet Service – The internet service providers the customer has. The value is ‘No’ if the customer doesn’t have internet service.
Online Security – Values as ‘yes’ or ‘no’ based on whether the customer has online security.
Online Backup – Values as ‘yes’ or ‘no’ based on whether the customer has online backup.
Device Protection – Values as ‘yes’ or ‘no’ based on whether the customer has device protection.
Tech Support – Values as ‘yes’ or ‘no’ based on whether the customer has tech support.
Streaming TV – Values as ‘yes’ or ‘no’ based on whether the customer has a streaming TV.
Streaming Movies – Values as ‘yes’ or ‘no’ based on whether the customer has streaming movies.
Contract – This column gives the term of the contract for the customer which could be a year, two years or month-to-month.
Paperless Billing – Values as ‘yes’ or ‘no’ based on whether the customer has a paperless billing.
Payment Method – It gives the payment method used by the customer which could be a credit card, Bank Transfer, Mailed Check, or Electronic Check.
Monthly Charges – This is the total charge incurred by the customer monthly.
Total Charges – The value of the total amount charged.
Churn – This is our target variable which needs to be predicted. Its values are either Yes (if the customer has churned), or No (if the customer is still with the company)
The following steps are the walkthrough of the code which we have written to predict the customer churn.
First, we have imported all the necessary libraries we would need to proceed further in our code
Just to get an idea of how our data looks likes, we have read the CSV file and printed out the first five rows of our data in the form of a data frame
Once, the data is read, some pre-processing needed to be done to check for null, outliers, and so on
Once the pre-processing is done, the next step is to get the relevant features to use in our model for the prediction. For that, we have done some data visualization to find out the relevancy of each predictor variables
After the data has been plotted, it is observed that Gender doesn’t have much influence on churn, whereas senior citizens are more likely to leave the company. Also, Phone Service has more influence on Churn than Multiple Lines
A model cannot take categorical data as input, hence those features are encoded into numbers to be used in our prediction
Based on our observation, we have taken the features which have more influence on churn prediction
The data is scaled, and split it into train and test set
We have fitted the Random Forest classifier to our new scaled data
Predicted the result and using the confusion matrix as the metric for our model
The model gives us (1155 + 190 = 1345) correct predictions and (273 + 143 = 416) incorrect predictions
The entire code could be found in this GitHub link
Conclusion
We have built a basic Random Forest Classifier model to predict the Customer Churn for a telecom company. The model could be improved with further manipulation of the parameters of the classifier and also by applying different algorithms.
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