title

Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka

description

( R Training : https://www.edureka.co/data-analytics-with-r-certification-training )
This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session:
1. Why Data Mining?
2. What is Data Mining
3. Knowledge Discovery in Database
4. Data Mining Tasks
5. Programming Languages for Data Mining
6. Case study using R
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How it Works?
1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get Lifetime Access to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate!
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About the Course
Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
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Why Learn Data Science?
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the Data Science course, you should be able to:
1. Gain insight into the 'Roles' played by a Data Scientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
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Who should go for this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies
For more information, please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free).
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Customer Reviews:
Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
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detail

{'title': 'Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka', 'heatmap': [{'end': 1032.42, 'start': 1008.203, 'weight': 0.715}, {'end': 1120.474, 'start': 1073.671, 'weight': 0.968}, {'end': 1278.092, 'start': 1229.265, 'weight': 0.799}], 'summary': 'Tutorial series on data mining using r covers core concepts, kdd process, techniques and languages, house price prediction case study, data visualization, and impact of independent variables on house prices, providing hands-on sessions, and a comparison of linear regression models with rmse values and adjusted r squared values.', 'chapters': [{'end': 135.588, 'segs': [{'end': 29.257, 'src': 'embed', 'start': 0.089, 'weight': 2, 'content': [{'end': 5.673, 'text': 'Hello people, this is Bharani from Edoica and this session is all about data mining.', 'start': 0.089, 'duration': 5.584}, {'end': 13.639, 'text': "We'll start off by understanding the core concepts of data mining, following which we'll have a hands-on session with R.", 'start': 6.273, 'duration': 7.366}, {'end': 14.82, 'text': "Let's have a look at the agenda.", 'start': 13.639, 'duration': 1.181}, {'end': 18.802, 'text': "We'll begin this session by knowing what is the need of data mining.", 'start': 15.32, 'duration': 3.482}, {'end': 23.586, 'text': "Thereafter, we'll comprehensively understand what exactly is data mining.", 'start': 19.343, 'duration': 4.243}, {'end': 29.257, 'text': "Next, we'll learn about the sequential steps involved in extracting knowledge from the data.", 'start': 24.213, 'duration': 5.044}], 'summary': 'Bharani from edoica discusses data mining: core concepts, hands-on with r, need and steps of data mining.', 'duration': 29.168, 'max_score': 0.089, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j889.jpg'}, {'end': 113.773, 'src': 'embed', 'start': 68.354, 'weight': 0, 'content': [{'end': 71.018, 'text': "And this is the data deluge that I'm talking about.", 'start': 68.354, 'duration': 2.664}, {'end': 79.063, 'text': 'Data is everywhere and it is expanding exponentially and this data is being generated from multiple sources.', 'start': 71.601, 'duration': 7.462}, {'end': 81.483, 'text': 'This is not just coming from social media.', 'start': 79.503, 'duration': 1.98}, {'end': 91.145, 'text': 'It comes from sectors such as healthcare sector financial sector telecom sector and so on and this data is also available in multiple formats.', 'start': 81.843, 'duration': 9.302}, {'end': 94.706, 'text': "Let's have a quick look at the different data types we have.", 'start': 91.805, 'duration': 2.901}, {'end': 100.507, 'text': 'Broadly speaking, we have structured, semi-structured, quasi-structured, and unstructured.', 'start': 95.464, 'duration': 5.043}, {'end': 108.491, 'text': 'Structured data type has a defined format, and since it has a defined format, extracting information from it is quite easy.', 'start': 101.087, 'duration': 7.404}, {'end': 113.773, 'text': 'Semi-structured data does not have a rigid pattern, but it does have a noticeable pattern.', 'start': 108.891, 'duration': 4.882}], 'summary': 'Data is expanding exponentially from various sectors and comes in structured, semi-structured, quasi-structured, and unstructured formats.', 'duration': 45.419, 'max_score': 68.354, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j868354.jpg'}], 'start': 0.089, 'title': 'Data mining fundamentals', 'summary': 'Covers core concepts of data mining, need for data mining in the information age, data formats, hands-on session with r, and a case study using r.', 'chapters': [{'end': 135.588, 'start': 0.089, 'title': 'Data mining fundamentals', 'summary': 'Covers the core concepts of data mining, the need for data mining in the age of information, the types of data formats, and the abundance of data from various sectors, including a hands-on session with r and a case study using r.', 'duration': 135.499, 'highlights': ['The data deluge is expanding exponentially with millions of people uploading millions of selfies every single day, leading to an abundance of data from various sources.', 'Data comes from sectors such as healthcare, financial, telecom, and social media, and it is available in structured, semi-structured, quasi-structured, and unstructured formats.', 'The session includes a hands-on session with R and a case study using R, providing practical application of the concepts learned.', 'Structured data type has a defined format, making it easier to extract information from it compared to semi-structured, quasi-structured, and unstructured data types.']}], 'duration': 135.499, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j889.jpg', 'highlights': ['The data deluge is expanding exponentially with millions of people uploading millions of selfies every single day, leading to an abundance of data from various sources.', 'Data comes from sectors such as healthcare, financial, telecom, and social media, and it is available in structured, semi-structured, quasi-structured, and unstructured formats.', 'The session includes a hands-on session with R and a case study using R, providing practical application of the concepts learned.', 'Structured data type has a defined format, making it easier to extract information from it compared to semi-structured, quasi-structured, and unstructured data types.']}, {'end': 523.922, 'segs': [{'end': 220.383, 'src': 'embed', 'start': 175.472, 'weight': 0, 'content': [{'end': 184.078, 'text': 'So his friend Jim is a data scientist who suggests him to use data mining techniques to find solutions to each of the three problem statements.', 'start': 175.472, 'duration': 8.606}, {'end': 191.664, 'text': 'That is, John can use data mining techniques to find out how many of the financial transactions were fraudulent.', 'start': 184.699, 'duration': 6.965}, {'end': 197.408, 'text': 'Similarly, he can also use data mining techniques to find out if the mail was spam or not.', 'start': 192.164, 'duration': 5.244}, {'end': 203.452, 'text': 'And again, John can again use data mining techniques to find out how many of the customers will churn out.', 'start': 197.908, 'duration': 5.544}, {'end': 206.54, 'text': "We've understood the need of data mining.", 'start': 204.4, 'duration': 2.14}, {'end': 209.941, 'text': "Now let's understand what exactly is data mining.", 'start': 207.02, 'duration': 2.921}, {'end': 211.361, 'text': "Let's look at the definition.", 'start': 210.341, 'duration': 1.02}, {'end': 220.383, 'text': 'Data mining is the computing process of discovering patterns in large data sets, involving methods at the intersection of machine learning,', 'start': 212.041, 'duration': 8.342}], 'summary': 'John can use data mining to solve 3 problems: fraud detection, spam classification, and customer churn prediction.', 'duration': 44.911, 'max_score': 175.472, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j8175472.jpg'}, {'end': 321.502, 'src': 'embed', 'start': 297.382, 'weight': 3, 'content': [{'end': 305.129, 'text': 'And that is why it is always necessary to check the validity of the information before we reach to any sort of conclusion.', 'start': 297.382, 'duration': 7.747}, {'end': 308.071, 'text': 'You understood that the information is new and correct.', 'start': 305.689, 'duration': 2.382}, {'end': 311.895, 'text': "Now it's time to see if the information is useful or not.", 'start': 308.492, 'duration': 3.403}, {'end': 318.58, 'text': 'As we saw in the first example, we found out an information which told us that smoking causes cancer.', 'start': 312.995, 'duration': 5.585}, {'end': 321.502, 'text': 'Now, that information did not serve a purpose.', 'start': 319.24, 'duration': 2.262}], 'summary': 'Validating information is crucial; new info must be useful, e.g., smoking causes cancer.', 'duration': 24.12, 'max_score': 297.382, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j8297382.jpg'}, {'end': 367.823, 'src': 'embed', 'start': 344.713, 'weight': 1, 'content': [{'end': 356.158, 'text': 'KDD has these steps selection of data, pre-processing of data, mining of data, evaluation of patterns and representation of knowledge.', 'start': 344.713, 'duration': 11.445}, {'end': 361.04, 'text': "Let's start with the first step which is selection of data.", 'start': 357.418, 'duration': 3.622}, {'end': 367.823, 'text': 'We already know that data comes from multiple sources and that is also available in multiple formats.', 'start': 361.9, 'duration': 5.923}], 'summary': 'Kdd process includes data selection, pre-processing, mining, evaluation, and knowledge representation.', 'duration': 23.11, 'max_score': 344.713, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j8344713.jpg'}, {'end': 423.951, 'src': 'embed', 'start': 393.743, 'weight': 2, 'content': [{'end': 398.045, 'text': 'Reprocessing tasks involves such as understanding the structure of data.', 'start': 393.743, 'duration': 4.302}, {'end': 407.329, 'text': 'You can also use visualization techniques to understand how are the variables in the data set related to each other, the correlation among them.', 'start': 398.465, 'duration': 8.864}, {'end': 413.412, 'text': 'You can also apply simple operations such as summarizing, aggregation, and normalization.', 'start': 407.349, 'duration': 6.063}, {'end': 415.505, 'text': 'Data selection has been done.', 'start': 414.164, 'duration': 1.341}, {'end': 417.466, 'text': "We've also done a bit of pre-processing.", 'start': 415.725, 'duration': 1.741}, {'end': 423.951, 'text': "Now it's time for the most important step in KDD process, which is mining of data.", 'start': 418.047, 'duration': 5.904}], 'summary': 'Reprocessing tasks involve understanding data structure, visualizing variables, applying operations, selecting and pre-processing data, leading to the crucial step of data mining.', 'duration': 30.208, 'max_score': 393.743, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j8393743.jpg'}, {'end': 495.877, 'src': 'embed', 'start': 466.326, 'weight': 4, 'content': [{'end': 469.827, 'text': 'Those were the sequential steps involved in KDD process.', 'start': 466.326, 'duration': 3.501}, {'end': 473.59, 'text': "Now, let's look at the data mining techniques.", 'start': 470.669, 'duration': 2.921}, {'end': 483.773, 'text': "In this session, we'll be looking at anomaly detection, association rule mining, clustering, classification, and regression.", 'start': 474.57, 'duration': 9.203}, {'end': 489.235, 'text': "Let's proceed with the first data mining technique, which is anomaly detection.", 'start': 484.373, 'duration': 4.862}, {'end': 495.877, 'text': 'This technique helps us to identify unusual patterns or outliers in the data.', 'start': 489.915, 'duration': 5.962}], 'summary': 'Data mining techniques include anomaly detection, association rule mining, clustering, classification, and regression.', 'duration': 29.551, 'max_score': 466.326, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j8466326.jpg'}], 'start': 136.309, 'title': 'Data mining and kdd process', 'summary': 'Covers the application of data mining in identifying fraudulent financial transactions, spam emails, and customer churn, emphasizing the need for new information. it also discusses the kdd process, data selection, pre-processing, mining, evaluation of patterns, and representation of knowledge, along with various data mining techniques such as anomaly detection, association rule mining, clustering, classification, and regression.', 'chapters': [{'end': 296.781, 'start': 136.309, 'title': 'Data mining and its applications', 'summary': 'Discusses the application of data mining in identifying fraudulent financial transactions, spam emails, and customer churn, emphasizing the need for new, correct, and potentially useful information, and the consequences of failing to meet these criteria.', 'duration': 160.472, 'highlights': ['Data mining techniques can be used to identify fraudulent financial transactions, spam emails, and customer churn. John can use data mining techniques to find out how many of the financial transactions were fraudulent, how many of the mails were spam, and how many of the customers will churn out.', 'Data mining is the computing process of discovering patterns in large data sets, involving methods at the intersection of machine learning, statistics, and database systems. Data mining is the computing process of discovering patterns in large data sets, involving methods at the intersection of machine learning, statistics, and database systems.', 'The need for new, correct, and potentially useful information in data mining is emphasized through examples of redundant or incorrect findings. Data mining should yield new, correct, and potentially useful information; otherwise, it may lead to redundant or incorrect findings, as illustrated by the examples of discovering known information or making incorrect predictions.']}, {'end': 523.922, 'start': 297.382, 'title': 'Data mining and kdd process', 'summary': 'Discusses the kdd process, which involves data selection, pre-processing, mining, evaluation of patterns, and representation of knowledge, and delves into data mining techniques like anomaly detection, association rule mining, clustering, classification, and regression.', 'duration': 226.54, 'highlights': ['The KDD process involves data selection, pre-processing, mining, evaluation of patterns, and representation of knowledge. The KDD process consists of sequential steps, including data selection, pre-processing, mining, evaluation of patterns, and representation of knowledge.', 'Data mining techniques covered include anomaly detection, association rule mining, clustering, classification, and regression. The data mining techniques discussed are anomaly detection, association rule mining, clustering, classification, and regression.', "Understanding the structure of data, using visualization techniques, and applying operations like summarizing, aggregation, and normalization are part of the pre-processing tasks. Pre-processing tasks involve understanding the data's structure, utilizing visualization techniques, and applying operations such as summarizing, aggregation, and normalization.", "Mr. Mukesh Ambani's attendance at a movie theater causing a significant increase in the average salary of moviegoers is given as an example of anomaly detection. An example is provided to illustrate anomaly detection, where Mr. Mukesh Ambani's attendance at a movie theater causes a drastic increase in the average salary of moviegoers."]}], 'duration': 387.613, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j8136309.jpg', 'highlights': ['Data mining techniques can be used to identify fraudulent financial transactions, spam emails, and customer churn.', 'The KDD process involves data selection, pre-processing, mining, evaluation of patterns, and representation of knowledge.', 'Understanding the structure of data, using visualization techniques, and applying operations like summarizing, aggregation, and normalization are part of the pre-processing tasks.', 'The need for new, correct, and potentially useful information in data mining is emphasized through examples of redundant or incorrect findings.', 'Data mining techniques covered include anomaly detection, association rule mining, clustering, classification, and regression.', 'Data mining is the computing process of discovering patterns in large data sets, involving methods at the intersection of machine learning, statistics, and database systems.']}, {'end': 1024.135, 'segs': [{'end': 574.007, 'src': 'embed', 'start': 547.168, 'weight': 0, 'content': [{'end': 551.809, 'text': 'This has a very interesting example, and it goes by the name of the Biotipo syndrome.', 'start': 547.168, 'duration': 4.641}, {'end': 561.941, 'text': 'So, in a survey done by a supermarket, it was found out that whenever a single father came to the store to buy a diaper,', 'start': 552.52, 'duration': 9.421}, {'end': 566.023, 'text': 'there was very high likelihood that he would also buy a bottle of beer.', 'start': 561.941, 'duration': 4.082}, {'end': 574.007, 'text': "Now that's quite an uncanny relationship, isn't it? A single father coming to a store to buy a diaper also buys a bottle of beer.", 'start': 566.483, 'duration': 7.524}], 'summary': 'Single fathers buying diapers have high likelihood to also buy a bottle of beer.', 'duration': 26.839, 'max_score': 547.168, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j8547168.jpg'}, {'end': 873.429, 'src': 'embed', 'start': 850.261, 'weight': 1, 'content': [{'end': 857.723, 'text': 'Now why am I using R? So R is a programming language which is used for many statistical modeling and data science tasks.', 'start': 850.261, 'duration': 7.462}, {'end': 862.644, 'text': 'And I believe one of the best feature of R is that it provides more than 10,000 free packages.', 'start': 858.263, 'duration': 4.381}, {'end': 869.267, 'text': 'Whatever your need is, if you want to do data visualization, R provides a package for that.', 'start': 864.424, 'duration': 4.843}, {'end': 873.429, 'text': 'If you want to do statistical analysis, R provides a package for that.', 'start': 869.667, 'duration': 3.762}], 'summary': 'R is a powerful programming language for statistical modeling and data science with over 10,000 free packages for various tasks.', 'duration': 23.168, 'max_score': 850.261, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j8850261.jpg'}, {'end': 957.285, 'src': 'embed', 'start': 932.881, 'weight': 2, 'content': [{'end': 940.804, 'text': 'Facebook users are for behavior analysis, Google users are for advertising effectiveness, and Twitter users are for data visualization.', 'start': 932.881, 'duration': 7.923}, {'end': 948.926, 'text': 'So if your idea is to do some data science tasks, then R is definitely the programming language that you need to use.', 'start': 941.324, 'duration': 7.602}, {'end': 952.222, 'text': "So you've understood why R, who uses R.", 'start': 949.921, 'duration': 2.301}, {'end': 954.904, 'text': "Let's finally go ahead and install R.", 'start': 952.222, 'duration': 2.682}, {'end': 957.285, 'text': "So this is the site from where you'll be installing R.", 'start': 954.904, 'duration': 2.381}], 'summary': 'Facebook for behavior analysis, google for advertising, twitter for visualization. r is the go-to language for data science tasks.', 'duration': 24.404, 'max_score': 932.881, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j8932881.jpg'}], 'start': 524.502, 'title': 'Data mining techniques and languages', 'summary': "Provides an overview of data mining techniques such as association rule mining, clustering, unsupervised learning, classification, and regression, with examples like biotipo syndrome and netflix recommender system. it also explores programming languages r, python, julia, and sas for data mining, emphasizing r's 10,000 free packages and its usage by big companies like facebook, google, and twitter for behavior analysis, advertising effectiveness, and data visualization.", 'chapters': [{'end': 830.018, 'start': 524.502, 'title': 'Data mining techniques overview', 'summary': 'Provides an overview of data mining techniques including association rule mining, clustering, unsupervised learning, classification, and regression, with examples such as the biotipo syndrome and netflix recommender system.', 'duration': 305.516, 'highlights': ['Association rule mining example of Biotipo syndrome - single fathers buying diapers and beer In a supermarket survey, it was found that single fathers buying diapers also had a high likelihood of buying beer, demonstrating the uncanny relationship discovered through association rule mining.', 'Explanation of clustering and unsupervised learning Clustering involves segregating observations into clusters based on their similarity, while unsupervised learning algorithms, which operate without predefined labels, are used to cluster observations based on their similarities.', 'Explanation of classification and supervised learning Classification involves assigning predefined labels to observations, as opposed to unsupervised learning, and comes under the purview of supervised learning.', 'Introduction to regression and its types Regression techniques, falling under supervised learning, allow for the understanding of how one variable changes with respect to another, including types such as linear regression, logistic regression, and Poisson regression.']}, {'end': 915.338, 'start': 830.478, 'title': 'Programming languages for data mining', 'summary': "Explores the programming languages r, python, julia, and sas for data mining, emphasizing r's 10,000 free packages and dynamically typed nature.", 'duration': 84.86, 'highlights': ['R provides over 10,000 free packages, catering to various needs such as data visualization, statistical analysis, and data manipulation.', 'R is a dynamically typed programming language, focusing on the value of the variable rather than the data type, allowing for flexible variable assignments.', 'R is used for many statistical modeling and data science tasks, making it a versatile choice for data mining.']}, {'end': 1024.135, 'start': 916.379, 'title': 'Introduction to r programming', 'summary': 'Introduces r programming by highlighting its ease of integration with popular softwares like tableau and sql server, its usage by big companies like facebook, google, and twitter for behavior analysis, advertising effectiveness, and data visualization, and provides guidance for installing r and rstudio for different operating systems.', 'duration': 107.756, 'highlights': ['Big companies like Facebook, Google, and Twitter use R for behavior analysis, advertising effectiveness, and data visualization. Facebook uses R for behavior analysis, Google uses R for advertising effectiveness, and Twitter uses R for data visualization.', 'R is easily integrated with popular softwares like Tableau and SQL Server. R can be easily integrated with popular softwares like Tableau and SQL Server.', 'Guidance for installing R and RStudio for different operating systems is provided. Guidance for installing R and RStudio for Windows, Mac, and Linux systems is provided, including the link to download R from the CRAN repository and RStudio from rstudio.com.']}], 'duration': 499.633, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j8524502.jpg', 'highlights': ['Association rule mining example of Biotipo syndrome - single fathers buying diapers and beer In a supermarket survey, it was found that single fathers buying diapers also had a high likelihood of buying beer, demonstrating the uncanny relationship discovered through association rule mining.', 'R provides over 10,000 free packages, catering to various needs such as data visualization, statistical analysis, and data manipulation.', 'Big companies like Facebook, Google, and Twitter use R for behavior analysis, advertising effectiveness, and data visualization. Facebook uses R for behavior analysis, Google uses R for advertising effectiveness, and Twitter uses R for data visualization.']}, {'end': 1371.442, 'segs': [{'end': 1050.147, 'src': 'embed', 'start': 1024.474, 'weight': 0, 'content': [{'end': 1032.42, 'text': 'We have the houses for sale data set which comprises of price of house, lot size, number of rooms, living area and so on.', 'start': 1024.474, 'duration': 7.946}, {'end': 1041.226, 'text': 'And our task is to understand the data set and design a model which will help us to predict the prices of houses with respect to other variables.', 'start': 1032.8, 'duration': 8.426}, {'end': 1045.127, 'text': "let's look at the tasks which we'll be performing in the case study.", 'start': 1041.986, 'duration': 3.141}, {'end': 1050.147, 'text': "we'll start off by importing the data set, and then we'll do a bit of pre-processing.", 'start': 1045.127, 'duration': 5.02}], 'summary': 'Analyzing house sales dataset to predict prices using pre-processing and modeling', 'duration': 25.673, 'max_score': 1024.474, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j81024473.jpg'}, {'end': 1120.474, 'src': 'heatmap', 'start': 1060.089, 'weight': 1, 'content': [{'end': 1067.43, 'text': "then comes the important part, which is data mining, and the data mining technique which we'll be using is linear regression.", 'start': 1060.089, 'duration': 7.341}, {'end': 1073.671, 'text': "so we'll use linear regression to predict the prices of houses, and we'll be building two models actually,", 'start': 1067.43, 'duration': 6.241}, {'end': 1080.277, 'text': 'and of those two models will try to understand which model is more accurate or which model will give us better results.', 'start': 1073.671, 'duration': 6.606}, {'end': 1083.039, 'text': "Let's start with the hands-on session.", 'start': 1081.198, 'duration': 1.841}, {'end': 1086.902, 'text': 'This is our studio and this is how it looks like.', 'start': 1083.72, 'duration': 3.182}, {'end': 1089.264, 'text': 'This is a script window.', 'start': 1088.324, 'duration': 0.94}, {'end': 1090.385, 'text': 'This is the console window.', 'start': 1089.284, 'duration': 1.101}, {'end': 1092.047, 'text': "Let's start with the first step.", 'start': 1091.006, 'duration': 1.041}, {'end': 1097.719, 'text': 'So read.csv, this is the function with which we are importing the CSV file.', 'start': 1093.211, 'duration': 4.508}, {'end': 1101.426, 'text': 'And that CSV file consists of the houses dataset.', 'start': 1098.401, 'duration': 3.025}, {'end': 1103.831, 'text': "Let's look at this dataset.", 'start': 1102.829, 'duration': 1.002}, {'end': 1107.066, 'text': 'This is the data set guys.', 'start': 1105.805, 'duration': 1.261}, {'end': 1110.308, 'text': 'So these two columns are so purpose.', 'start': 1107.566, 'duration': 2.742}, {'end': 1111.529, 'text': 'They just are numberings.', 'start': 1110.508, 'duration': 1.021}, {'end': 1113.53, 'text': 'So we would have to delete them.', 'start': 1112.069, 'duration': 1.461}, {'end': 1115.791, 'text': 'This is the price of the house.', 'start': 1113.93, 'duration': 1.861}, {'end': 1117.132, 'text': 'This is the lot size.', 'start': 1116.092, 'duration': 1.04}, {'end': 1120.474, 'text': 'This column tells us if the house has a waterfront or not.', 'start': 1117.552, 'duration': 2.922}], 'summary': 'Using linear regression to predict house prices, building 2 models to assess accuracy.', 'duration': 22.95, 'max_score': 1060.089, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j81060089.jpg'}, {'end': 1183.234, 'src': 'embed', 'start': 1133.002, 'weight': 2, 'content': [{'end': 1136.106, 'text': 'These two are the fuel types and the heat types used in-house.', 'start': 1133.002, 'duration': 3.104}, {'end': 1143.255, 'text': 'This is the sewer system, living area, the number of fireplaces, number of bathrooms and number of rooms.', 'start': 1136.607, 'duration': 6.648}, {'end': 1145.137, 'text': 'That is the data set.', 'start': 1144.096, 'duration': 1.041}, {'end': 1147.32, 'text': "Let's also look at the structure of the data set.", 'start': 1145.478, 'duration': 1.842}, {'end': 1153.487, 'text': 'STR function, with the help of STR function, we can understand the structure.', 'start': 1150.322, 'duration': 3.165}, {'end': 1159.095, 'text': 'Now this tells us there are 1728 observations and 16 variables in total.', 'start': 1153.827, 'duration': 5.268}, {'end': 1162.661, 'text': 'And most of these variables are of the type integer.', 'start': 1159.456, 'duration': 3.205}, {'end': 1164.56, 'text': 'that was the structure.', 'start': 1163.419, 'duration': 1.141}, {'end': 1167.622, 'text': "now we've understood that this data is not clean.", 'start': 1164.56, 'duration': 3.062}, {'end': 1171.185, 'text': "so let's go ahead and do the pre-processing or a bit of cleaning.", 'start': 1167.622, 'duration': 3.563}, {'end': 1175.428, 'text': 'so the first task would be to delete these two columns after that.', 'start': 1171.185, 'duration': 4.243}, {'end': 1178.85, 'text': 'now these values seem to be a bit ambiguous.', 'start': 1175.428, 'duration': 3.422}, {'end': 1183.234, 'text': 'so if the house has a waterfront, we can change it to yes instead of one.', 'start': 1178.85, 'duration': 4.384}], 'summary': 'Dataset contains 1728 observations and 16 variables, mostly integers. pre-processing required for data cleaning.', 'duration': 50.232, 'max_score': 1133.002, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j81133002.jpg'}, {'end': 1278.092, 'src': 'heatmap', 'start': 1229.265, 'weight': 0.799, 'content': [{'end': 1235.726, 'text': "I type out deployer select it now when I say install the package will be installed and since I've already installed the package.", 'start': 1229.265, 'duration': 6.461}, {'end': 1237.007, 'text': "I don't need to do it again.", 'start': 1235.987, 'duration': 1.02}, {'end': 1238.787, 'text': 'I loaded the package now.', 'start': 1237.027, 'duration': 1.76}, {'end': 1240.968, 'text': "It's time to delete the first two columns.", 'start': 1238.847, 'duration': 2.121}, {'end': 1250.483, 'text': 'The select function comes from the deployer package and this is the data set and this symbol over here that you see is the pipe operator.', 'start': 1242.717, 'duration': 7.766}, {'end': 1253.265, 'text': 'The pipe operator helps us to connect things.', 'start': 1250.843, 'duration': 2.422}, {'end': 1261.151, 'text': 'So, from the houses data set, we are selecting all other variables except the first and the second column,', 'start': 1253.605, 'duration': 7.546}, {'end': 1264.113, 'text': 'and we are storing the result again in the houses data set.', 'start': 1261.151, 'duration': 2.962}, {'end': 1268.156, 'text': 'So we applied the command and we see that the changes have taken place.', 'start': 1264.573, 'duration': 3.583}, {'end': 1270.137, 'text': 'The first two columns have been deleted.', 'start': 1268.316, 'duration': 1.821}, {'end': 1272.419, 'text': "Let's go ahead with the pre-processing.", 'start': 1270.918, 'duration': 1.501}, {'end': 1278.092, 'text': 'We saw that wherever the values were 0 and 1, they have been changed to know.', 'start': 1273.889, 'duration': 4.203}], 'summary': 'Using deployer package, deleted first 2 columns from dataset.', 'duration': 48.827, 'max_score': 1229.265, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j81229265.jpg'}, {'end': 1340.671, 'src': 'embed', 'start': 1316.219, 'weight': 4, 'content': [{'end': 1322.162, 'text': "So I've selected the construction column changed it to a categorical variable and change the labels to no and yes.", 'start': 1316.219, 'duration': 5.943}, {'end': 1324.644, 'text': "I've done the same for the waterfront column as well.", 'start': 1322.382, 'duration': 2.262}, {'end': 1331.103, 'text': "Now let's go ahead and also change the fuel type, heat type, and the sewer system.", 'start': 1325.478, 'duration': 5.625}, {'end': 1332.804, 'text': "Let's go ahead and do that.", 'start': 1331.643, 'duration': 1.161}, {'end': 1336.788, 'text': "So we've changed fuel and sewer.", 'start': 1334.926, 'duration': 1.862}, {'end': 1340.671, 'text': 'So the fuel type has been changed to gas, electric, and oil.', 'start': 1337.328, 'duration': 3.343}], 'summary': 'Converted construction and waterfront columns to categorical variables; updated fuel type to gas, electric, and oil.', 'duration': 24.452, 'max_score': 1316.219, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j81316219.jpg'}], 'start': 1024.474, 'title': 'House price prediction case study', 'summary': 'Discusses the task of understanding and designing a model to predict house prices using the houses for sale dataset, including importing, pre-processing, and using linear regression to build and compare two models for prediction. it also covers the pre-processing and cleaning of a dataset consisting of 1728 observations and 16 variables, involving tasks such as deleting columns, changing ambiguous values, and converting numerical variables into categorical ones.', 'chapters': [{'end': 1083.039, 'start': 1024.474, 'title': 'House price prediction case study', 'summary': 'Discusses the task of understanding and designing a model to predict house prices using the houses for sale dataset, including importing, pre-processing, and using linear regression to build and compare two models for prediction.', 'duration': 58.565, 'highlights': ['The chapter focuses on understanding and designing a model to predict house prices using the houses for sale dataset, involving tasks such as importing, pre-processing, and utilizing linear regression for model building.', 'The data set includes variables such as price of house, lot size, number of rooms, and living area, and the goal is to predict house prices with respect to these variables.', 'The pre-processing phase involves understanding the data structure, performing data cleaning to tidy the untidy data, and preparing it for further analysis.', 'The key technique for data mining is linear regression, and the chapter aims to build and compare two models to determine the one that provides better results for predicting house prices.']}, {'end': 1371.442, 'start': 1083.72, 'title': 'Data pre-processing and cleaning', 'summary': 'Covers the pre-processing and cleaning of a dataset consisting of 1728 observations and 16 variables, involving tasks such as deleting columns, changing ambiguous values, and converting numerical variables into categorical ones.', 'duration': 287.722, 'highlights': ['The dataset consists of 1728 observations and 16 variables, mainly of the type integer. The dataset contains 1728 observations and 16 variables, predominantly integers.', 'Pre-processing tasks include deleting ambiguous columns and converting numerical values to categorical ones. Pre-processing involves deleting ambiguous columns and transforming numerical values to categorical ones.', 'The process involves changing labels for columns such as air conditioning, construction, waterfront, fuel type, heat type, and sewer system. Labels for air conditioning, construction, waterfront, fuel type, heat type, and sewer system are changed during the process.']}], 'duration': 346.968, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j81024473.jpg', 'highlights': ['The chapter focuses on understanding and designing a model to predict house prices using the houses for sale dataset, involving tasks such as importing, pre-processing, and utilizing linear regression for model building.', 'The key technique for data mining is linear regression, and the chapter aims to build and compare two models to determine the one that provides better results for predicting house prices.', 'The dataset consists of 1728 observations and 16 variables, mainly of the type integer.', 'Pre-processing tasks include deleting ambiguous columns and converting numerical values to categorical ones.', 'The process involves changing labels for columns such as air conditioning, construction, waterfront, fuel type, heat type, and sewer system.']}, {'end': 1947.072, 'segs': [{'end': 1529.443, 'src': 'embed', 'start': 1483.089, 'weight': 0, 'content': [{'end': 1485.972, 'text': 'that basically stands for 2 into 10 power 5, 4 into 10.', 'start': 1483.089, 'duration': 2.883}, {'end': 1487.833, 'text': 'power 5, 6 into 10 power 5..', 'start': 1485.972, 'duration': 1.861}, {'end': 1491.556, 'text': 'that again means the value is around 2 lakhs, 4 lakhs or 6 lakhs and 8 lakhs.', 'start': 1487.833, 'duration': 3.723}, {'end': 1506.07, 'text': 'So from this graph we can infer that the average salary of the houses is around two lakhs and the maximum price of the house would be around somewhere close to 7.5 lakhs.', 'start': 1492.236, 'duration': 13.834}, {'end': 1507.671, 'text': "We're done with this.", 'start': 1506.99, 'duration': 0.681}, {'end': 1511.515, 'text': "Let's add a bit of color to the plot so that it looks prettier.", 'start': 1508.392, 'duration': 3.123}, {'end': 1515.07, 'text': "In the geometry, I've added two more attributes.", 'start': 1512.708, 'duration': 2.362}, {'end': 1517.492, 'text': 'I have fill and I have color.', 'start': 1515.671, 'duration': 1.821}, {'end': 1521.896, 'text': 'So the fill is of light blue and color, which is the boundary, is of dark blue.', 'start': 1517.753, 'duration': 4.143}, {'end': 1524.859, 'text': 'So the same graph, I just added color to it.', 'start': 1522.457, 'duration': 2.402}, {'end': 1529.443, 'text': "Now let's understand how does price vary with respect to waterfront.", 'start': 1525.419, 'duration': 4.024}], 'summary': 'Average salary of houses around 2 lakhs, maximum price close to 7.5 lakhs.', 'duration': 46.354, 'max_score': 1483.089, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j81483089.jpg'}, {'end': 1599.801, 'src': 'embed', 'start': 1575.498, 'weight': 2, 'content': [{'end': 1582.382, 'text': 'So this the median value of the house which has a waterfront is higher and the median value of a house which does not have a waterfront is lower.', 'start': 1575.498, 'duration': 6.884}, {'end': 1587.505, 'text': "Now let's see how does the price vary with respect to the air conditioning.", 'start': 1583.022, 'duration': 4.483}, {'end': 1595.379, 'text': 'Again, data set is houses, y-axis is price, x-axis is determined by whether the house has air conditioning or not.', 'start': 1588.416, 'duration': 6.963}, {'end': 1599.801, 'text': 'The fill is determined by, again, whether the house has air conditioning or not.', 'start': 1595.639, 'duration': 4.162}], 'summary': 'The median value of waterfront houses is higher; air conditioning affects price.', 'duration': 24.303, 'max_score': 1575.498, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j81575498.jpg'}, {'end': 1951.553, 'src': 'embed', 'start': 1923.926, 'weight': 1, 'content': [{'end': 1928.507, 'text': 'so actual values, predicted values and error in prediction.', 'start': 1923.926, 'duration': 4.581}, {'end': 1931.128, 'text': 'we have something known as root mean square error.', 'start': 1928.507, 'duration': 2.621}, {'end': 1935.209, 'text': 'to check for its accuracy, we store it in rmsc1.', 'start': 1931.128, 'duration': 4.081}, {'end': 1936.589, 'text': "let's look at its value.", 'start': 1935.209, 'duration': 1.38}, {'end': 1938.97, 'text': 'so this is the error in the prediction 52 837.57.', 'start': 1936.589, 'duration': 2.381}, {'end': 1943.211, 'text': 'is the error in prediction.', 'start': 1938.97, 'duration': 4.241}, {'end': 1947.072, 'text': "now let's also have a look at the summary of the model built.", 'start': 1943.211, 'duration': 3.861}, {'end': 1951.553, 'text': 'So this was the model which we built on the training site and this is the summary.', 'start': 1947.711, 'duration': 3.842}], 'summary': "Model's root mean square error is 52,837.57, indicating prediction accuracy.", 'duration': 27.627, 'max_score': 1923.926, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j81923926.jpg'}], 'start': 1371.442, 'title': 'Data visualization and linear regression', 'summary': 'Covers visualizing data with ggplot2 and linear regression, including installation of ggplot2, understanding the grammar of graphics, selecting data, aesthetics, and geometry for plotting, and using ggplot2 to visualize the distribution of house prices, inferring an average price of around 2 lakhs and a maximum price of around 7.5 lakhs. it also discusses adding color to plots, visualizing price variations with respect to different features such as waterfront, air conditioning, living area, and the age of the house, and then building and evaluating a linear regression model with a root mean square error of 52,837.57.', 'chapters': [{'end': 1507.671, 'start': 1371.442, 'title': 'Visualizing data with ggplot2', 'summary': 'Covers the installation of ggplot2, understanding the grammar of graphics, selecting data, aesthetics, and geometry for plotting, and using ggplot2 to visualize the distribution of house prices, inferring an average price of around 2 lakhs and a maximum price of around 7.5 lakhs.', 'duration': 136.229, 'highlights': ['The chapter covers the installation of ggplot2, understanding the grammar of graphics, selecting data, aesthetics, and geometry for plotting, and using ggplot2 to visualize the distribution of house prices. Covers installation of ggplot2, understanding grammar of graphics, selecting data, aesthetics, and geometry for plotting, visualization of house price distribution.', 'Infer the average price of houses as around two lakhs and the maximum price as around 7.5 lakhs from the graph showing price distribution. Infer average price as around 2 lakhs and maximum price as around 7.5 lakhs from the price distribution graph.']}, {'end': 1947.072, 'start': 1508.392, 'title': 'Data visualization and linear regression', 'summary': 'Discusses adding color to plots, visualizing price variations with respect to different features such as waterfront, air conditioning, living area, and the age of the house, and then building and evaluating a linear regression model with a root mean square error of 52,837.57.', 'duration': 438.68, 'highlights': ['Building and evaluating a linear regression model with a root mean square error of 52,837.57. The chapter concludes with the evaluation of a linear regression model, producing a root mean square error of 52,837.57, which measures the accuracy of the model in predicting house prices.', 'Visualizing price variations with respect to different features such as waterfront, air conditioning, living area, and the age of the house. The chapter extensively discusses visualizing the variations in house prices with respect to different features such as waterfront, air conditioning, living area, and the age of the house, providing insights into how these features impact house prices.', 'Adding color to plots to enhance visualization. The chapter begins by discussing the addition of color to plots to enhance visualization, providing a more visually appealing representation of the data.']}], 'duration': 575.63, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j81371442.jpg', 'highlights': ['Infer average price as around 2 lakhs and maximum price as around 7.5 lakhs from the price distribution graph.', 'The chapter concludes with the evaluation of a linear regression model, producing a root mean square error of 52,837.57, which measures the accuracy of the model in predicting house prices.', 'The chapter extensively discusses visualizing the variations in house prices with respect to different features such as waterfront, air conditioning, living area, and the age of the house, providing insights into how these features impact house prices.', 'The chapter begins by discussing the addition of color to plots to enhance visualization, providing a more visually appealing representation of the data.']}, {'end': 2189.201, 'segs': [{'end': 1999.91, 'src': 'embed', 'start': 1970.925, 'weight': 0, 'content': [{'end': 1975.106, 'text': 'So if the house has a waterfront then it will have a greater impact on the price of the house.', 'start': 1970.925, 'duration': 4.181}, {'end': 1983.947, 'text': "If the land value again has a greater impact on the price of the house and if it's newly constructed it again has a greater impact on the price.", 'start': 1975.806, 'duration': 8.141}, {'end': 1988.468, 'text': "Let's look at the opposite side that which do not have an impact at all.", 'start': 1984.567, 'duration': 3.901}, {'end': 1996.689, 'text': 'So heat sewer, the number of fireplaces, the type of heat used or the type of fuel used, or the number of fireplaces used.', 'start': 1988.728, 'duration': 7.961}, {'end': 1999.91, 'text': 'they do not have an impact on the dependent variable at all.', 'start': 1996.689, 'duration': 3.221}], 'summary': 'Waterfront and land value impact house price, while heat sewer, type of heat, and number of fireplaces do not.', 'duration': 28.985, 'max_score': 1970.925, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j81970925.jpg'}, {'end': 2140.069, 'src': 'embed', 'start': 2085.496, 'weight': 1, 'content': [{'end': 2088.917, 'text': 'Actual values predicted values and arrow in prediction.', 'start': 2085.496, 'duration': 3.421}, {'end': 2092.858, 'text': "Let's go ahead and also calculate the root mean square error of it.", 'start': 2089.577, 'duration': 3.281}, {'end': 2095.895, 'text': 'Root mean square error is simple.', 'start': 2094.474, 'duration': 1.421}, {'end': 2100.838, 'text': 'So we square the error and then we find out its mean and then we find out its square root.', 'start': 2096.094, 'duration': 4.744}, {'end': 2105.981, 'text': "Let's compare the root mean square error of both the models built.", 'start': 2101.959, 'duration': 4.022}, {'end': 2110.449, 'text': 'So RMSE one is 52,837.', 'start': 2107.287, 'duration': 3.162}, {'end': 2112.75, 'text': 'RMSE two is 52,773.', 'start': 2110.449, 'duration': 2.301}, {'end': 2121.394, 'text': 'What we observe from this is the error of the second model is lesser than the error in the first model.', 'start': 2112.75, 'duration': 8.644}, {'end': 2124.876, 'text': 'And that is why the second model is better than the first model.', 'start': 2121.774, 'duration': 3.102}, {'end': 2127.117, 'text': "Let's also have a look at its summary.", 'start': 2125.416, 'duration': 1.701}, {'end': 2128.798, 'text': 'Summary of mod two.', 'start': 2127.677, 'duration': 1.121}, {'end': 2140.069, 'text': "So it's r squared value is 0.6522 and the r squared value of the first model was 0.651.", 'start': 2129.622, 'duration': 10.447}], 'summary': 'Comparing rmse, model 2 is better with 52,773 < 52,837. mod 2 r-squared: 0.6522.', 'duration': 54.573, 'max_score': 2085.496, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j82085496.jpg'}], 'start': 1947.711, 'title': 'Impact of independent variables on house prices and linear regression model comparison', 'summary': 'Discusses the impact of independent variables such as waterfront, land value, and new construction on house prices, with a focus on the degree of impact. additionally, it compares two linear regression models, highlighting the superiority of the second model with an rmse value of 52,773 compared to 52,837 and a higher adjusted r squared value of 0.6522 compared to 0.651.', 'chapters': [{'end': 1988.468, 'start': 1947.711, 'title': 'Impact of independent variables on house prices', 'summary': 'Discusses the impact of independent variables on the price of a house, with a focus on waterfront, land value, and new construction, where the number of stars indicates the degree of impact.', 'duration': 40.757, 'highlights': ['The number of stars indicates the degree of impact, with a greater number of stars representing a greater impact on the dependent variable.', 'Variables such as waterfront, land value, and new construction have a significant impact on the price of the house, as indicated by the number of stars.', "It is observed that variables with three stars have a greater impact on the dependent variable, such as the house's waterfront, land value, and being newly constructed."]}, {'end': 2189.201, 'start': 1988.728, 'title': 'Linear regression model comparison', 'summary': 'Discusses building and comparing two linear regression models to predict a dependent variable, showing that the second model outperforms the first with an rmse value of 52,773 compared to 52,837 and a higher adjusted r squared value of 0.6522 compared to 0.651.', 'duration': 200.473, 'highlights': ['The second model outperforms the first model with a lower root mean square error (RMSE) of 52,773 compared to 52,837, indicating better predictive accuracy. The root mean square error (RMSE) of the second model is 52,773, which is lower than the RMSE of the first model at 52,837, showing improved predictive accuracy.', 'The second model demonstrates a higher adjusted R squared value of 0.6522 compared to 0.651 for the first model, indicating a better fit for the data. The adjusted R squared value for the second model is 0.6522, which is higher than the value of 0.651 for the first model, signifying a better fit for the data.', 'The chapter concludes by summarizing the process of importing the dataset, pre-processing, visualizing correlations, splitting the dataset, building two linear regression models, and evaluating their performance. The chapter wraps up by summarizing the steps involved, including importing the dataset, pre-processing, visualizing correlations, splitting the dataset, building two linear regression models, and comparing their performance.']}], 'duration': 241.49, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BB2O4VCu5j8/pics/BB2O4VCu5j81947711.jpg', 'highlights': ['Variables such as waterfront, land value, and new construction have a significant impact on the price of the house, as indicated by the number of stars.', 'The second model outperforms the first model with a lower root mean square error (RMSE) of 52,773 compared to 52,837, indicating better predictive accuracy.', 'The second model demonstrates a higher adjusted R squared value of 0.6522 compared to 0.651 for the first model, indicating a better fit for the data.']}], 'highlights': ['The data deluge is expanding exponentially with millions of people uploading millions of selfies every single day, leading to an abundance of data from various sources.', 'Data comes from sectors such as healthcare, financial, telecom, and social media, and it is available in structured, semi-structured, quasi-structured, and unstructured formats.', 'The session includes a hands-on session with R and a case study using R, providing practical application of the concepts learned.', 'Structured data type has a defined format, making it easier to extract information from it compared to semi-structured, quasi-structured, and unstructured data types.', 'Data mining techniques can be used to identify fraudulent financial transactions, spam emails, and customer churn.', 'The KDD process involves data selection, pre-processing, mining, evaluation of patterns, and representation of knowledge.', 'Understanding the structure of data, using visualization techniques, and applying operations like summarizing, aggregation, and normalization are part of the pre-processing tasks.', 'The need for new, correct, and potentially useful information in data mining is emphasized through examples of redundant or incorrect findings.', 'Data mining techniques covered include anomaly detection, association rule mining, clustering, classification, and regression.', 'Data mining is the computing process of discovering patterns in large data sets, involving methods at the intersection of machine learning, statistics, and database systems.', 'Association rule mining example of Biotipo syndrome - single fathers buying diapers and beer In a supermarket survey, it was found that single fathers buying diapers also had a high likelihood of buying beer, demonstrating the uncanny relationship discovered through association rule mining.', 'R provides over 10,000 free packages, catering to various needs such as data visualization, statistical analysis, and data manipulation.', 'Big companies like Facebook, Google, and Twitter use R for behavior analysis, advertising effectiveness, and data visualization. Facebook uses R for behavior analysis, Google uses R for advertising effectiveness, and Twitter uses R for data visualization.', 'The chapter focuses on understanding and designing a model to predict house prices using the houses for sale dataset, involving tasks such as importing, pre-processing, and utilizing linear regression for model building.', 'The key technique for data mining is linear regression, and the chapter aims to build and compare two models to determine the one that provides better results for predicting house prices.', 'The dataset consists of 1728 observations and 16 variables, mainly of the type integer.', 'Pre-processing tasks include deleting ambiguous columns and converting numerical values to categorical ones.', 'The process involves changing labels for columns such as air conditioning, construction, waterfront, fuel type, heat type, and sewer system.', 'Infer average price as around 2 lakhs and maximum price as around 7.5 lakhs from the price distribution graph.', 'The chapter concludes with the evaluation of a linear regression model, producing a root mean square error of 52,837.57, which measures the accuracy of the model in predicting house prices.', 'The chapter extensively discusses visualizing the variations in house prices with respect to different features such as waterfront, air conditioning, living area, and the age of the house, providing insights into how these features impact house prices.', 'The chapter begins by discussing the addition of color to plots to enhance visualization, providing a more visually appealing representation of the data.', 'Variables such as waterfront, land value, and new construction have a significant impact on the price of the house, as indicated by the number of stars.', 'The second model outperforms the first model with a lower root mean square error (RMSE) of 52,773 compared to 52,837, indicating better predictive accuracy.', 'The second model demonstrates a higher adjusted R squared value of 0.6522 compared to 0.651 for the first model, indicating a better fit for the data.']}