�ݺ�ߣshows by User: sohomg

�ݺ�ߣshows by User: sohomg / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: sohomg / Sun, 28 Feb 2016 15:42:58 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: sohomg Prediction of Salary From Profiles /sohomg/prediction-of-salary-from-profiles predictionofsalaryfromprofiles-160228154258
Predictive Analytics is the usage of historical information to forecast the future. For formulating better business strategies and making more informed decisions, it is of utmost importance to predict what is likely to happen. The task of prediction is difficult and complex. Using Machine Learning (ML) it is possible to devise some Intelligent Models which may aid in making informed decisions by predicting that is imminent. In this abstract, we discuss the methodologies for predicting salaries that students are likely to get by analyzing their profiles. We use publicly available Aspiring Minds’ Employment Outcomes 2015 dataset for our analysis.]]>
Predictive Analytics is the usage of historical information to forecast the future. For formulating better business strategies and making more informed decisions, it is of utmost importance to predict what is likely to happen. The task of prediction is difficult and complex. Using Machine Learning (ML) it is possible to devise some Intelligent Models which may aid in making informed decisions by predicting that is imminent. In this abstract, we discuss the methodologies for predicting salaries that students are likely to get by analyzing their profiles. We use publicly available Aspiring Minds’ Employment Outcomes 2015 dataset for our analysis.]]> Sun, 28 Feb 2016 15:42:58 GMT /sohomg/prediction-of-salary-from-profiles sohomg@slideshare.net(sohomg) Prediction of Salary From Profiles sohomg Predictive Analytics is the usage of historical information to forecast the future. For formulating better business strategies and making more informed decisions, it is of utmost importance to predict what is likely to happen. The task of prediction is difficult and complex. Using Machine Learning (ML) it is possible to devise some Intelligent Models which may aid in making informed decisions by predicting that is imminent. In this abstract, we discuss the methodologies for predicting salaries that students are likely to get by analyzing their profiles. We use publicly available Aspiring Minds’ Employment Outcomes 2015 dataset for our analysis. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/predictionofsalaryfromprofiles-160228154258-thumbnail.jpg?width=120&height=120&fit=bounds" /> Predictive Analytics is the usage of historical information to forecast the future. For formulating better business strategies and making more informed decisions, it is of utmost importance to predict what is likely to happen. The task of prediction is difficult and complex. Using Machine Learning (ML) it is possible to devise some Intelligent Models which may aid in making informed decisions by predicting that is imminent. In this abstract, we discuss the methodologies for predicting salaries that students are likely to get by analyzing their profiles. We use publicly available Aspiring Minds’ Employment Outcomes 2015 dataset for our analysis.

Prediction of Salary From Profiles from Sohom Ghosh

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0 A Model to Compute Degree of Polarity of Review Titles /slideshow/a-model-to-compute-degree-of-polarity-of-review-titles/58154768 reviewpolarityapam-160211153121
Review Polarity Computation has been a flourishing frontier in the Natural Language Processing community. In this paper, we thoroughly study review titles of electronic products and compute the sentiment scores. Firstly, we conduct our experiment by collecting the review titles from a popular e-commerce website to build our dataset. Our dataset contains more than 1000 positive and negative review titles. For preprocessing, several NLP operations like tokenization, stop-word removal, stemming and so on have been done on the dataset. We build our own unique word corpora separately for positive and negative words. Finally, we design a new innovative model which automatically generates the scores by analyzing the review title. The score vary from -5 to +5. A score of -5 indicates that the review title is extremely negative and that of +5 indicates that it is highly affirmative. Experimental results confirm the high efficiency of our model. A product can be rated automatically as soon as a user writes the title of the review. Thus, the company can decide which reviews to display in their front page just by analyzing the title of the review.]]>
Review Polarity Computation has been a flourishing frontier in the Natural Language Processing community. In this paper, we thoroughly study review titles of electronic products and compute the sentiment scores. Firstly, we conduct our experiment by collecting the review titles from a popular e-commerce website to build our dataset. Our dataset contains more than 1000 positive and negative review titles. For preprocessing, several NLP operations like tokenization, stop-word removal, stemming and so on have been done on the dataset. We build our own unique word corpora separately for positive and negative words. Finally, we design a new innovative model which automatically generates the scores by analyzing the review title. The score vary from -5 to +5. A score of -5 indicates that the review title is extremely negative and that of +5 indicates that it is highly affirmative. Experimental results confirm the high efficiency of our model. A product can be rated automatically as soon as a user writes the title of the review. Thus, the company can decide which reviews to display in their front page just by analyzing the title of the review.]]> Thu, 11 Feb 2016 15:31:21 GMT /slideshow/a-model-to-compute-degree-of-polarity-of-review-titles/58154768 sohomg@slideshare.net(sohomg) A Model to Compute Degree of Polarity of Review Titles sohomg Review Polarity Computation has been a flourishing frontier in the Natural Language Processing community. In this paper, we thoroughly study review titles of electronic products and compute the sentiment scores. Firstly, we conduct our experiment by collecting the review titles from a popular e-commerce website to build our dataset. Our dataset contains more than 1000 positive and negative review titles. For preprocessing, several NLP operations like tokenization, stop-word removal, stemming and so on have been done on the dataset. We build our own unique word corpora separately for positive and negative words. Finally, we design a new innovative model which automatically generates the scores by analyzing the review title. The score vary from -5 to +5. A score of -5 indicates that the review title is extremely negative and that of +5 indicates that it is highly affirmative. Experimental results confirm the high efficiency of our model. A product can be rated automatically as soon as a user writes the title of the review. Thus, the company can decide which reviews to display in their front page just by analyzing the title of the review. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/reviewpolarityapam-160211153121-thumbnail.jpg?width=120&height=120&fit=bounds" /> Review Polarity Computation has been a flourishing frontier in the Natural Language Processing community. In this paper, we thoroughly study review titles of electronic products and compute the sentiment scores. Firstly, we conduct our experiment by collecting the review titles from a popular e-commerce website to build our dataset. Our dataset contains more than 1000 positive and negative review titles. For preprocessing, several NLP operations like tokenization, stop-word removal, stemming and so on have been done on the dataset. We build our own unique word corpora separately for positive and negative words. Finally, we design a new innovative model which automatically generates the scores by analyzing the review title. The score vary from -5 to +5. A score of -5 indicates that the review title is extremely negative and that of +5 indicates that it is highly affirmative. Experimental results confirm the high efficiency of our model. A product can be rated automatically as soon as a user writes the title of the review. Thus, the company can decide which reviews to display in their front page just by analyzing the title of the review.

A Model to Compute Degree of Polarity of Review Titles from Sohom Ghosh

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0 Resume /slideshow/resume-56866850/56866850 sohomcv-160110013726
My Resume]]>
My Resume]]> Sun, 10 Jan 2016 01:37:26 GMT /slideshow/resume-56866850/56866850 sohomg@slideshare.net(sohomg) Resume sohomg My Resume <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sohomcv-160110013726-thumbnail.jpg?width=120&height=120&fit=bounds" /> My Resume

Resume from Sohom Ghosh

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0 Automated Car Rental System /sohomg/automated-car-rental-system java7-group1-presentationofautomatedcarrentalsystem-160106021951
This is an automated application software for Car booking . This application is used in the Car rental Company for managing the Car booking efficiently. The primary project goals consist of: To provide a user-friendly and time efficient application for both the customers and the company employees The employees of the company are able to keep the track of the renting process easily when any submission is made on an auto. To keep the whole data into a database system. It not only provides a quick access to the desired data but also it helps to organize and store the data easily. In order to meet with the security issues, these data are not allowed to be reachable since they will include private data. To provide the payment, date and time interval phases to the renting customers.]]>
This is an automated application software for Car booking . This application is used in the Car rental Company for managing the Car booking efficiently. The primary project goals consist of: To provide a user-friendly and time efficient application for both the customers and the company employees The employees of the company are able to keep the track of the renting process easily when any submission is made on an auto. To keep the whole data into a database system. It not only provides a quick access to the desired data but also it helps to organize and store the data easily. In order to meet with the security issues, these data are not allowed to be reachable since they will include private data. To provide the payment, date and time interval phases to the renting customers.]]> Wed, 06 Jan 2016 02:19:51 GMT /sohomg/automated-car-rental-system sohomg@slideshare.net(sohomg) Automated Car Rental System sohomg This is an automated application software for Car booking . This application is used in the Car rental Company for managing the Car booking efficiently. The primary project goals consist of: To provide a user-friendly and time efficient application for both the customers and the company employees The employees of the company are able to keep the track of the renting process easily when any submission is made on an auto. To keep the whole data into a database system. It not only provides a quick access to the desired data but also it helps to organize and store the data easily. In order to meet with the security issues, these data are not allowed to be reachable since they will include private data. To provide the payment, date and time interval phases to the renting customers. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/java7-group1-presentationofautomatedcarrentalsystem-160106021951-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is an automated application software for Car booking . This application is used in the Car rental Company for managing the Car booking efficiently. The primary project goals consist of: To provide a user-friendly and time efficient application for both the customers and the company employees The employees of the company are able to keep the track of the renting process easily when any submission is made on an auto. To keep the whole data into a database system. It not only provides a quick access to the desired data but also it helps to organize and store the data easily. In order to meet with the security issues, these data are not allowed to be reachable since they will include private data. To provide the payment, date and time interval phases to the renting customers.

Automated Car Rental System from Sohom Ghosh

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0 Solving Real Life Problems using Data Science Part - 1 /slideshow/solving-real-life-problems-using-data-science-part-1/56239961 1251117-151217130834
Modeling Customer Churn using Data Science, Prediction of Cancellations of Taxi Reservations]]>
Modeling Customer Churn using Data Science, Prediction of Cancellations of Taxi Reservations]]> Thu, 17 Dec 2015 13:08:34 GMT /slideshow/solving-real-life-problems-using-data-science-part-1/56239961 sohomg@slideshare.net(sohomg) Solving Real Life Problems using Data Science Part - 1 sohomg Modeling Customer Churn using Data Science, Prediction of Cancellations of Taxi Reservations <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/1251117-151217130834-thumbnail.jpg?width=120&height=120&fit=bounds" /> Modeling Customer Churn using Data Science, Prediction of Cancellations of Taxi Reservations

Solving Real Life Problems using Data Science Part - 1 from Sohom Ghosh

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0 Analysis of Online Product Purchase and Predicting Items for Co-purchase - ICACNI 2015 /slideshow/icacni-2015/50077370 icacni2015-335-150702040400-lva1-app6891
In recent years, online market places have become popular among the buyers. During this course of time, not only have they sustained the business model but also generated large amount of profit, turning it into a lucrative business model. In this paper, we take a look at a temporal dataset from one of the most successful online businesses to analyze the nature of the buying patterns of the users. Arguably, the most important purchase characteristic of such networks is follow-up purchase by a buyer, otherwise known as a co-purchase. In this paper, we also analyze the co-purchase patterns to build a knowledge-base to recommend potential co-purchase items for every item.]]>
In recent years, online market places have become popular among the buyers. During this course of time, not only have they sustained the business model but also generated large amount of profit, turning it into a lucrative business model. In this paper, we take a look at a temporal dataset from one of the most successful online businesses to analyze the nature of the buying patterns of the users. Arguably, the most important purchase characteristic of such networks is follow-up purchase by a buyer, otherwise known as a co-purchase. In this paper, we also analyze the co-purchase patterns to build a knowledge-base to recommend potential co-purchase items for every item.]]> Thu, 02 Jul 2015 04:03:59 GMT /slideshow/icacni-2015/50077370 sohomg@slideshare.net(sohomg) Analysis of Online Product Purchase and Predicting Items for Co-purchase - ICACNI 2015 sohomg In recent years, online market places have become popular among the buyers. During this course of time, not only have they sustained the business model but also generated large amount of profit, turning it into a lucrative business model. In this paper, we take a look at a temporal dataset from one of the most successful online businesses to analyze the nature of the buying patterns of the users. Arguably, the most important purchase characteristic of such networks is follow-up purchase by a buyer, otherwise known as a co-purchase. In this paper, we also analyze the co-purchase patterns to build a knowledge-base to recommend potential co-purchase items for every item. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/icacni2015-335-150702040400-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> In recent years, online market places have become popular among the buyers. During this course of time, not only have they sustained the business model but also generated large amount of profit, turning it into a lucrative business model. In this paper, we take a look at a temporal dataset from one of the most successful online businesses to analyze the nature of the buying patterns of the users. Arguably, the most important purchase characteristic of such networks is follow-up purchase by a buyer, otherwise known as a co-purchase. In this paper, we also analyze the co-purchase patterns to build a knowledge-base to recommend potential co-purchase items for every item.

Analysis of Online Product Purchase and Predicting Items for Co-purchase - ICACNI 2015 from Sohom Ghosh

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0 Social network analysis /slideshow/social-network-analysis-49266432/49266432 socialnetworkanalysis-150611122355-lva1-app6891
Tools for SNA Page Rank Algorithm Hierarchical Clustering Recommendation System based on SNA (Collaborative Filtering) How Facebook/Amazon uses SNA for recommendations? Two Hop degree Dynamism in Friendship Network of CSE-B Online Social Networks and Clusters Influential Nodes and Their Importance Bibliography Question - Answer Session]]>
Tools for SNA Page Rank Algorithm Hierarchical Clustering Recommendation System based on SNA (Collaborative Filtering) How Facebook/Amazon uses SNA for recommendations? Two Hop degree Dynamism in Friendship Network of CSE-B Online Social Networks and Clusters Influential Nodes and Their Importance Bibliography Question - Answer Session]]> Thu, 11 Jun 2015 12:23:55 GMT /slideshow/social-network-analysis-49266432/49266432 sohomg@slideshare.net(sohomg) Social network analysis sohomg Tools for SNA Page Rank Algorithm Hierarchical Clustering Recommendation System based on SNA (Collaborative Filtering) How Facebook/Amazon uses SNA for recommendations? Two Hop degree Dynamism in Friendship Network of CSE-B Online Social Networks and Clusters Influential Nodes and Their Importance Bibliography Question - Answer Session <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/socialnetworkanalysis-150611122355-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Tools for SNA Page Rank Algorithm Hierarchical Clustering Recommendation System based on SNA (Collaborative Filtering) How Facebook/Amazon uses SNA for recommendations? Two Hop degree Dynamism in Friendship Network of CSE-B Online Social Networks and Clusters Influential Nodes and Their Importance Bibliography Question - Answer Session

Social network analysis from Sohom Ghosh

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0 Extraction and Analysis of Publication Data of Conferences - ICACCE 2015 /slideshow/extraction-and-analysis-of-publication-data-of-conferences/49265355 extractionandanalysisofpublicationdataofconferences-150611115444-lva1-app6891
Discovering critical nodes in social networks is crucial for comprehending the structural characteristics, connectivity and importance of its presence in the network. In this paper, we focus on detecting vital nodes which are part of multiple dense sub-graphs and play a major role in connecting other nodes thereby forming a global network. Here, we analyze the data of some major data mining conferences to explore if there exist some nodes which play a major role in submission and acceptance of paper in those conferences. We try to figure out the relationship between the Programme Committee members and the authors and their co-authors. We look for the top authors of these conferences. Furthermore, we mine the patterns in which some authors’ and their co-authors’ papers are being accepted. In order to understand the underlying dynamics of the network over time we examine how these patterns change periodically and with what factors. Lastly, we add to this a comparative study stating the affiliation of authors and examine if local influence of their ethnicity does exists with respect to continents where these conferences are taking place.]]>
Discovering critical nodes in social networks is crucial for comprehending the structural characteristics, connectivity and importance of its presence in the network. In this paper, we focus on detecting vital nodes which are part of multiple dense sub-graphs and play a major role in connecting other nodes thereby forming a global network. Here, we analyze the data of some major data mining conferences to explore if there exist some nodes which play a major role in submission and acceptance of paper in those conferences. We try to figure out the relationship between the Programme Committee members and the authors and their co-authors. We look for the top authors of these conferences. Furthermore, we mine the patterns in which some authors’ and their co-authors’ papers are being accepted. In order to understand the underlying dynamics of the network over time we examine how these patterns change periodically and with what factors. Lastly, we add to this a comparative study stating the affiliation of authors and examine if local influence of their ethnicity does exists with respect to continents where these conferences are taking place.]]> Thu, 11 Jun 2015 11:54:43 GMT /slideshow/extraction-and-analysis-of-publication-data-of-conferences/49265355 sohomg@slideshare.net(sohomg) Extraction and Analysis of Publication Data of Conferences - ICACCE 2015 sohomg Discovering critical nodes in social networks is crucial for comprehending the structural characteristics, connectivity and importance of its presence in the network. In this paper, we focus on detecting vital nodes which are part of multiple dense sub-graphs and play a major role in connecting other nodes thereby forming a global network. Here, we analyze the data of some major data mining conferences to explore if there exist some nodes which play a major role in submission and acceptance of paper in those conferences. We try to figure out the relationship between the Programme Committee members and the authors and their co-authors. We look for the top authors of these conferences. Furthermore, we mine the patterns in which some authors’ and their co-authors’ papers are being accepted. In order to understand the underlying dynamics of the network over time we examine how these patterns change periodically and with what factors. Lastly, we add to this a comparative study stating the affiliation of authors and examine if local influence of their ethnicity does exists with respect to continents where these conferences are taking place. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/extractionandanalysisofpublicationdataofconferences-150611115444-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Discovering critical nodes in social networks is crucial for comprehending the structural characteristics, connectivity and importance of its presence in the network. In this paper, we focus on detecting vital nodes which are part of multiple dense sub-graphs and play a major role in connecting other nodes thereby forming a global network. Here, we analyze the data of some major data mining conferences to explore if there exist some nodes which play a major role in submission and acceptance of paper in those conferences. We try to figure out the relationship between the Programme Committee members and the authors and their co-authors. We look for the top authors of these conferences. Furthermore, we mine the patterns in which some authors’ and their co-authors’ papers are being accepted. In order to understand the underlying dynamics of the network over time we examine how these patterns change periodically and with what factors. Lastly, we add to this a comparative study stating the affiliation of authors and examine if local influence of their ethnicity does exists with respect to continents where these conferences are taking place.

Extraction and Analysis of Publication Data of Conferences - ICACCE 2015 from Sohom Ghosh

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0 A Model of Opinion Mining to Compute Score from Curriculum Vitae - WBSSTC 2015 /sohomg/new-nbu newnbu-150611114955-lva1-app6892
Opinion Mining has become a major field of study of late. Curriculum vitae or resume reveals the potential of an interviewee. It creates his first impression. A recent study revealed that more than 90% of resumes are rejected at the very first glance. Thus, it is extremely important to prepare a resume which is both impressive and informative. In this paper, I will be discussing about the innovative model which I have built to compute score from resume. Based, on this score a user will have an idea about where his CV stands. This model can work with any kind of CV format. The best part of it is that it can suggest the areas of improvement after analyzing the content of the CV. This original model uses natural language processing tasks like tokenization, stop word removal, POS tagger for preprocessing. Concepts of text mining have been used for examining the corpus. Finally, I have used a mapping function to compute the grade from score. The main advantage of this time savvy model is it can do away with the manual process of evaluating resume. It is useful for both recruiters and job seekers.]]>
Opinion Mining has become a major field of study of late. Curriculum vitae or resume reveals the potential of an interviewee. It creates his first impression. A recent study revealed that more than 90% of resumes are rejected at the very first glance. Thus, it is extremely important to prepare a resume which is both impressive and informative. In this paper, I will be discussing about the innovative model which I have built to compute score from resume. Based, on this score a user will have an idea about where his CV stands. This model can work with any kind of CV format. The best part of it is that it can suggest the areas of improvement after analyzing the content of the CV. This original model uses natural language processing tasks like tokenization, stop word removal, POS tagger for preprocessing. Concepts of text mining have been used for examining the corpus. Finally, I have used a mapping function to compute the grade from score. The main advantage of this time savvy model is it can do away with the manual process of evaluating resume. It is useful for both recruiters and job seekers.]]> Thu, 11 Jun 2015 11:49:55 GMT /sohomg/new-nbu sohomg@slideshare.net(sohomg) A Model of Opinion Mining to Compute Score from Curriculum Vitae - WBSSTC 2015 sohomg Opinion Mining has become a major field of study of late. Curriculum vitae or resume reveals the potential of an interviewee. It creates his first impression. A recent study revealed that more than 90% of resumes are rejected at the very first glance. Thus, it is extremely important to prepare a resume which is both impressive and informative. In this paper, I will be discussing about the innovative model which I have built to compute score from resume. Based, on this score a user will have an idea about where his CV stands. This model can work with any kind of CV format. The best part of it is that it can suggest the areas of improvement after analyzing the content of the CV. This original model uses natural language processing tasks like tokenization, stop word removal, POS tagger for preprocessing. Concepts of text mining have been used for examining the corpus. Finally, I have used a mapping function to compute the grade from score. The main advantage of this time savvy model is it can do away with the manual process of evaluating resume. It is useful for both recruiters and job seekers. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/newnbu-150611114955-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> Opinion Mining has become a major field of study of late. Curriculum vitae or resume reveals the potential of an interviewee. It creates his first impression. A recent study revealed that more than 90% of resumes are rejected at the very first glance. Thus, it is extremely important to prepare a resume which is both impressive and informative. In this paper, I will be discussing about the innovative model which I have built to compute score from resume. Based, on this score a user will have an idea about where his CV stands. This model can work with any kind of CV format. The best part of it is that it can suggest the areas of improvement after analyzing the content of the CV. This original model uses natural language processing tasks like tokenization, stop word removal, POS tagger for preprocessing. Concepts of text mining have been used for examining the corpus. Finally, I have used a mapping function to compute the grade from score. The main advantage of this time savvy model is it can do away with the manual process of evaluating resume. It is useful for both recruiters and job seekers.

A Model of Opinion Mining to Compute Score from Curriculum Vitae - WBSSTC 2015 from Sohom Ghosh

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0 R programming Basic & Advanced /slideshow/r-programming-basic-advanced/37575461 rprogrammingbasicadvanced-140801101208-phpapp01
Presentation on R programming. Topics covered are: Manage your Workspace Data types Fiddle with Data Types Lists Vs Vectors R as calculator!!! Decision making statements, looping, functions Interact with R!!! Visualization!!! Time for U!!! Clustering Regression (with curve fitting) ]]>
Presentation on R programming. Topics covered are: Manage your Workspace Data types Fiddle with Data Types Lists Vs Vectors R as calculator!!! Decision making statements, looping, functions Interact with R!!! Visualization!!! Time for U!!! Clustering Regression (with curve fitting) ]]> Fri, 01 Aug 2014 10:12:08 GMT /slideshow/r-programming-basic-advanced/37575461 sohomg@slideshare.net(sohomg) R programming Basic & Advanced sohomg Presentation on R programming. Topics covered are: Manage your Workspace Data types Fiddle with Data Types Lists Vs Vectors R as calculator!!! Decision making statements, looping, functions Interact with R!!! Visualization!!! Time for U!!! Clustering Regression (with curve fitting) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/rprogrammingbasicadvanced-140801101208-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presentation on R programming. Topics covered are: Manage your Workspace Data types Fiddle with Data Types Lists Vs Vectors R as calculator!!! Decision making statements, looping, functions Interact with R!!! Visualization!!! Time for U!!! Clustering Regression (with curve fitting)

R programming Basic & Advanced from Sohom Ghosh

]]> 7173 9 https://cdn.slidesharecdn.com/ss_thumbnails/rprogrammingbasicadvanced-140801101208-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post

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0 https://cdn.slidesharecdn.com/profile-photo-sohomg-48x48.jpg?cb=1642461148 I am a "Data Geek"! Being a Data Science Enthusiast, I enjoy exploring Data Mining, Machine Learning, Natural Language Processing, Recommender Systems and Predictive Analytics. My passion for Data Science drives me to solve "mysteries" (problems) relating to it. Being well equipped with R, Python, No/SQL, Big Data and related technologies, I love to "Deal with Data". I have worked on various research projects relating to Predictive Analytics, Sentiment Analysis, Social Network Analysis, Deep Learning and Automation. My works has been published in reputed International Journals [like ISSE (Springer, NASA Journal)] and International Conference proceedings [like ICACNI (Springer)] sites.google.com/view/sohomghosh https://cdn.slidesharecdn.com/ss_thumbnails/predictionofsalaryfromprofiles-160228154258-thumbnail.jpg?width=320&height=320&fit=bounds sohomg/prediction-of-salary-from-profiles Prediction of Salary F... https://cdn.slidesharecdn.com/ss_thumbnails/reviewpolarityapam-160211153121-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/a-model-to-compute-degree-of-polarity-of-review-titles/58154768 A Model to Compute Deg... https://cdn.slidesharecdn.com/ss_thumbnails/sohomcv-160110013726-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/resume-56866850/56866850 Resume