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Linear Regression Analysis | Linear Regression in Python | Machine Learning Algorithms | Simplilearn

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This Linear Regression Analysis video will help you understand the basics of linear regression algorithm. You will learn how Simple Linear Regression works with solved examples, look at the applications of Linear Regression and Multiple Linear Regression model. In the end, we will implement a use case on profit estimation of companies using Linear Regression in Python.
Dataset Link - https://drive.google.com/drive/folders/1BNAsNI6cbwX8I81Wf42xzNtoOzDLw9to
Below topics are covered in this Linear Regression Analysis Tutorial:
1. Introduction to Machine Learning
2. Machine Learning Algorithms
3. Applications of Linear Regression
4. Understanding Linear Regression
5. Multiple Linear Regression
6. Usecase - Profit estimation of companies
What is Linear Regression Analysis?
Machine Learning is an application of Artificial Intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Linear regression is a statistical model used to predict the relationship between independent and dependent variables by examining two factors:
Which variables, in particular, are significant predictors of the outcome variable?
How significant is the regression line in terms of making predictions with the highest possible accuracy?
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{'title': 'Linear Regression Analysis | Linear Regression in Python | Machine Learning Algorithms | Simplilearn', 'heatmap': [{'end': 283.329, 'start': 235.817, 'weight': 0.844}, {'end': 601.762, 'start': 577.642, 'weight': 0.914}, {'end': 755.797, 'start': 726.646, 'weight': 0.701}, {'end': 796.763, 'start': 767.626, 'weight': 0.759}, {'end': 1204.586, 'start': 1092.12, 'weight': 0.773}, {'end': 1373.903, 'start': 1327.572, 'weight': 0.876}, {'end': 1461.146, 'start': 1434.83, 'weight': 0.767}, {'end': 1631.52, 'start': 1500.484, 'weight': 0.838}], 'summary': 'Covers linear regression in python for profit estimation, machine learning introduction, data analysis, graphing, model creation, and validation, achieving an r2 score of .9352, and providing key takeaways on linear regression application and prediction.', 'chapters': [{'end': 97.523, 'segs': [{'end': 36.653, 'src': 'embed', 'start': 3.595, 'weight': 3, 'content': [{'end': 5.016, 'text': 'Welcome to linear regression.', 'start': 3.595, 'duration': 1.421}, {'end': 6.737, 'text': 'My name is Richard Kirshner.', 'start': 5.296, 'duration': 1.441}, {'end': 7.897, 'text': "I'm with Simply Learn.", 'start': 6.897, 'duration': 1}, {'end': 11.419, 'text': "Let's look at an example of a common use for linear regression.", 'start': 8.096, 'duration': 3.323}, {'end': 13.5, 'text': 'Profit estimation of a company.', 'start': 11.679, 'duration': 1.821}, {'end': 18.202, 'text': 'If I was going to invest in a company, I would like to know how much money I could expect to make.', 'start': 13.86, 'duration': 4.342}, {'end': 24.726, 'text': "So we'll take a look at a venture capitalist firm and try to understand which companies they should invest in.", 'start': 18.603, 'duration': 6.123}, {'end': 28.808, 'text': "So we'll take the idea that we need to decide the companies to invest in.", 'start': 25.026, 'duration': 3.782}, {'end': 36.653, 'text': "We need to predict the profit the company makes, and we're going to do it based on the company's expenses, and even just a specific expense.", 'start': 29.188, 'duration': 7.465}], 'summary': 'Linear regression for profit estimation in venture capitalist firm.', 'duration': 33.058, 'max_score': 3.595, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE3595.jpg'}, {'end': 79.869, 'src': 'embed', 'start': 45.639, 'weight': 2, 'content': [{'end': 49.802, 'text': "we might have the location, we might have what kind of administration it's going through.", 'start': 45.639, 'duration': 4.163}, {'end': 53.845, 'text': 'Based on all this different information, we would like to calculate the profit.', 'start': 50.122, 'duration': 3.723}, {'end': 60.891, 'text': "Now in actuality, there's usually about 23 to 27 different markers that they look at if they're a heavy duty investor.", 'start': 54.065, 'duration': 6.826}, {'end': 63.313, 'text': "We're only going to take a look at one basic one.", 'start': 61.131, 'duration': 2.182}, {'end': 70.42, 'text': "We're going to come in and for simplicity, let's consider a single variable R&D and find out which companies to invest in based on that.", 'start': 63.634, 'duration': 6.786}, {'end': 79.869, 'text': "So we take our R&D and we're plotting the profit based on the R&D expenditure how much money they put into the research and development and then we look at the profit that goes with that.", 'start': 70.86, 'duration': 9.009}], 'summary': 'Analyzing r&d expenditure to calculate profit for investment decisions.', 'duration': 34.23, 'max_score': 45.639, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE45639.jpg'}, {'end': 115.507, 'src': 'embed', 'start': 82.932, 'weight': 0, 'content': [{'end': 84.454, 'text': 'So we draw a line right through the data.', 'start': 82.932, 'duration': 1.522}, {'end': 90.119, 'text': "When you look at that, you can see how much they invest in the R&D is a good marker as to how much profit they're going to have.", 'start': 84.694, 'duration': 5.425}, {'end': 97.523, 'text': "We can also note that companies spending more on R&D make good profit, so let's invest in the ones that spend a higher rate in their R&D.", 'start': 90.359, 'duration': 7.164}, {'end': 103.726, 'text': "What's in it for you? First, we'll have an introduction to machine learning, followed by machine learning algorithms.", 'start': 97.903, 'duration': 5.823}, {'end': 108.509, 'text': 'These will be specific to linear regression and where it fits into the larger model.', 'start': 103.967, 'duration': 4.542}, {'end': 115.507, 'text': "Then we'll take a look at applications of linear regression, understanding linear regression, and multiple linear regression.", 'start': 108.925, 'duration': 6.582}], 'summary': 'Higher r&d investment leads to higher profit. introduction to machine learning and linear regression for business decisions.', 'duration': 32.575, 'max_score': 82.932, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE82932.jpg'}], 'start': 3.595, 'title': 'Linear regression for profit estimation', 'summary': 'Delves into the application of linear regression to predict company profits using research and development (r&d) expenditure as a key indicator, aiding in expense-based profit estimation.', 'chapters': [{'end': 97.523, 'start': 3.595, 'title': 'Linear regression for profit estimation', 'summary': 'Discusses the use of linear regression in estimating company profits based on expenses, particularly focusing on research and development (r&d) expenditure as a key marker for predicting profit.', 'duration': 93.928, 'highlights': ['By using linear regression, we can predict the profit of a company based on its expenses, such as R&D, marketing, and administration.', 'Venture capitalist firms typically consider 23 to 27 different markers for heavy-duty investment analysis.', "R&D expenditure is a good indicator of a company's potential profit, and companies spending more on R&D tend to have higher profits."]}], 'duration': 93.928, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE3595.jpg', 'highlights': ["R&D expenditure is a good indicator of a company's potential profit.", 'Companies spending more on R&D tend to have higher profits.', 'Venture capitalist firms typically consider 23 to 27 different markers for heavy-duty investment analysis.', 'Using linear regression, we can predict the profit of a company based on its expenses.']}, {'end': 968.795, 'segs': [{'end': 132.374, 'src': 'embed', 'start': 97.903, 'weight': 0, 'content': [{'end': 103.726, 'text': "What's in it for you? 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example of machine learning.", 'start': 130.733, 'duration': 1.641}], 'summary': 'Introduction to machine learning, linear regression, and programming for profit estimation of companies.', 'duration': 34.471, 'max_score': 97.903, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE97903.jpg'}, {'end': 283.329, 'src': 'heatmap', 'start': 235.817, 'weight': 0.844, 'content': [{'end': 239.46, 'text': 'Supervised, unsupervised, reinforcement.', 'start': 235.817, 'duration': 3.643}, {'end': 241.701, 'text': "We're only going to look at supervised today.", 'start': 239.78, 'duration': 1.921}, {'end': 245.543, 'text': "Unsupervised means we don't have the answers and we're just grouping things.", 'start': 242.161, 'duration': 3.382}, {'end': 251.327, 'text': 'Reinforcement is where we give positive and negative feedback to our algorithm to program it.', 'start': 245.884, 'duration': 5.443}, {'end': 253.949, 'text': "And it doesn't have the information until after the fact.", 'start': 251.567, 'duration': 2.382}, {'end': 257.87, 'text': "But today we're just looking at supervised because that's where linear regression fits in.", 'start': 254.189, 'duration': 3.681}, {'end': 262.494, 'text': 'In supervised data, we have our data already there and our answers for a group.', 'start': 258.091, 'duration': 4.403}, {'end': 266.236, 'text': 'and then we use that to program our model and come up with an answer.', 'start': 262.734, 'duration': 3.502}, {'end': 270.518, 'text': 'The two most common uses for that is through the regression and classification.', 'start': 266.496, 'duration': 4.022}, {'end': 275.3, 'text': "Now we're doing linear regression, so we're just going to focus on the regression side.", 'start': 270.778, 'duration': 4.522}, {'end': 283.329, 'text': 'And in the regression, we have simple linear regression, we have multiple linear regression, and we have polynomial linear regression.', 'start': 275.561, 'duration': 7.768}], 'summary': 'Supervised learning focuses on linear regression and classification, using answers to program the model.', 'duration': 47.512, 'max_score': 235.817, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE235817.jpg'}, {'end': 387.626, 'src': 'embed', 'start': 346.583, 'weight': 2, 'content': [{'end': 351.605, 'text': 'Housing sales, to estimate the number of houses a builder would sell and what price in the coming months.', 'start': 346.583, 'duration': 5.022}, {'end': 359.149, 'text': 'Score predictions, cricket fever, to predict the number of runs a player would score in the coming matches based on the previous performance.', 'start': 352.226, 'duration': 6.923}, {'end': 364.071, 'text': "I'm sure you can figure out other applications you could use linear regression for.", 'start': 359.749, 'duration': 4.322}, {'end': 369.133, 'text': "So let's jump in and let's understand linear regression and dig into the theory.", 'start': 364.251, 'duration': 4.882}, {'end': 371.434, 'text': 'Understanding linear regression.', 'start': 369.833, 'duration': 1.601}, {'end': 380.961, 'text': 'Linear regression is the statistical model used to predict the relationship between independent and dependent variables by examining two factors.', 'start': 372.055, 'duration': 8.906}, {'end': 387.626, 'text': 'The first important one is which variables in particular are significant predictors of the outcome variable.', 'start': 381.662, 'duration': 5.964}], 'summary': 'Linear regression predicts housing sales and cricket runs based on previous performance.', 'duration': 41.043, 'max_score': 346.583, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE346583.jpg'}, {'end': 608.007, 'src': 'heatmap', 'start': 577.642, 'weight': 0.914, 'content': [{'end': 579.822, 'text': "The means doesn't mean anything other than the average.", 'start': 577.642, 'duration': 2.18}, {'end': 582.783, 'text': 'So we add up all the numbers and divide by the total.', 'start': 580.222, 'duration': 2.561}, {'end': 587.885, 'text': 'So 1 plus 2 plus 3 plus 4 plus 5 over 5 equals 3.', 'start': 583.003, 'duration': 4.882}, {'end': 589.887, 'text': 'And the same for y, we get 4.', 'start': 587.885, 'duration': 2.002}, {'end': 596.372, 'text': "If we go ahead and plot the means on the graph, we'll see we get 3, 4, which draws a nice line down the middle.", 'start': 589.887, 'duration': 6.485}, {'end': 597.593, 'text': 'A good estimate.', 'start': 596.792, 'duration': 0.801}, {'end': 601.762, 'text': "Here we're going to dig deeper into the math behind the regression line.", 'start': 598.3, 'duration': 3.462}, {'end': 608.007, 'text': "Now remember before I said you don't have to have all these formulas memorized or fully understand them,", 'start': 602.183, 'duration': 5.824}], 'summary': 'Calculating means and plotting regression line for data points yields 3 and 4, providing a good estimate.', 'duration': 30.365, 'max_score': 577.642, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE577642.jpg'}, {'end': 755.797, 'src': 'heatmap', 'start': 726.646, 'weight': 0.701, 'content': [{'end': 733.029, 'text': 'And we end up, as we come across with our formula, You can plug in all those numbers, which is very easy to do on the computer.', 'start': 726.646, 'duration': 6.383}, {'end': 736.17, 'text': "You don't have to do the math on a piece of paper or calculator.", 'start': 733.169, 'duration': 3.001}, {'end': 738.231, 'text': "And you'll get a slope of 0.6.", 'start': 736.35, 'duration': 1.881}, {'end': 739.872, 'text': "And you'll get your c coefficient.", 'start': 738.231, 'duration': 1.641}, {'end': 744.253, 'text': "If you continue to follow through that formula, you'll see it comes out as equal to 2.2.", 'start': 740.052, 'duration': 4.201}, {'end': 747.734, 'text': "Continuing deeper into what's going behind the scenes,", 'start': 744.253, 'duration': 3.481}, {'end': 755.797, 'text': "let's find out the predicted values of y for corresponding values of x using the linear equation, where m equals 0.6 and c equals 2.2..", 'start': 747.734, 'duration': 8.063}], 'summary': 'Using the formula, the slope is 0.6, and the c coefficient is 2.2.', 'duration': 29.151, 'max_score': 726.646, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE726646.jpg'}, {'end': 796.763, 'src': 'heatmap', 'start': 767.626, 'weight': 0.759, 'content': [{'end': 776.354, 'text': 'And here the blue points represent the actual y values and the brown points represent the predicted y values based on the model we created.', 'start': 767.626, 'duration': 8.728}, {'end': 781.879, 'text': 'The distance between the actual and predicted values is known as residuals or errors.', 'start': 776.554, 'duration': 5.325}, {'end': 788.521, 'text': 'the best fit line should have the least sum of squares of these errors, also known as e-square.', 'start': 782.639, 'duration': 5.882}, {'end': 796.763, 'text': 'If we put these into a nice chart where you can see x and you can see y, what the actual values were, and you can see y predicted,', 'start': 788.721, 'duration': 8.042}], 'summary': 'The best fit line should minimize the sum of squares of errors, known as e-square.', 'duration': 29.137, 'max_score': 767.626, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE767626.jpg'}, {'end': 956.398, 'src': 'embed', 'start': 930.31, 'weight': 3, 'content': [{'end': 935.252, 'text': 'so on all the way to m to the nth, x to the nth, and then you add your coefficient on there.', 'start': 930.31, 'duration': 4.942}, {'end': 937.712, 'text': 'Implementation of linear regression.', 'start': 935.452, 'duration': 2.26}, {'end': 939.453, 'text': 'Now we get into my favorite part.', 'start': 937.872, 'duration': 1.581}, {'end': 944.734, 'text': "Let's understand how multiple linear regression works by implementing it in Python.", 'start': 939.813, 'duration': 4.921}, {'end': 951.577, 'text': 'If you remember before, we were looking at a company and just based on its R&D trying to figure out its profit.', 'start': 944.975, 'duration': 6.602}, {'end': 953.837, 'text': "We're going to start looking at the expenditure of the company.", 'start': 951.757, 'duration': 2.08}, {'end': 955.018, 'text': "We're going to go back to that.", 'start': 953.937, 'duration': 1.081}, {'end': 956.398, 'text': "We're going to predict its profit.", 'start': 955.158, 'duration': 1.24}], 'summary': 'Implementing multiple linear regression in python to predict company profit based on expenditure.', 'duration': 26.088, 'max_score': 930.31, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE930310.jpg'}], 'start': 97.903, 'title': 'Introduction to linear regression in machine learning', 'summary': 'Covers an introduction to machine learning with a focus on linear regression, including a practical use case for profit estimation of companies and an example of machine learning. it also explains the concepts of independent and dependent variables in the context of crop yield prediction, delves into the theory and definitions of machine learning, explores the applications and mathematical calculations behind linear regression, and provides an overview of multiple linear regression and its implementation in python for profit prediction.', 'chapters': [{'end': 132.374, 'start': 97.903, 'title': 'Introduction to linear regression in machine learning', 'summary': 'Covers an introduction to machine learning, specific to linear regression and its applications, followed by a practical use case for profit estimation of companies, and an example of machine learning.', 'duration': 34.471, 'highlights': ['The chapter covers an introduction to machine learning, specific to linear regression and its applications.', 'It includes a practical use case for profit estimation of companies.', 'The chapter also provides an example of machine learning.']}, {'end': 968.795, 'start': 132.514, 'title': 'Linear regression in machine learning', 'summary': 'Explains the concepts of independent and dependent variables in the context of crop yield prediction based on rainfall, delves into the theory and definitions of machine learning, particularly focusing on linear regression, and explores the applications and mathematical calculations behind linear regression, including the intuition, slope, coefficient, and predicted values, and provides an overview of multiple linear regression and its implementation in python for profit prediction.', 'duration': 836.281, 'highlights': ['Linear regression is used to predict the relationship between independent and dependent variables, with a significant focus on the example of crop yield prediction based on rainfall, demonstrating the fundamental concepts of independent and dependent variables. The discussion starts with an example of using rainfall as the independent variable and crop yield as the dependent variable to illustrate the concept and importance of independent and dependent variables in predicting crop yield.', 'The chapter covers the theory and definitions of machine learning, particularly focusing on supervised learning, linear regression, and its different types, including simple linear regression, multiple linear regression, and polynomial linear regression. It delves into the theory and definitions of machine learning, focusing on supervised learning and exploring the different types of linear regression, providing a comprehensive understanding of the concepts and their application in predictive modeling.', 'The applications of linear regression, such as predicting economic growth, product prices, housing sales, and score predictions, are discussed, emphasizing the versatility and practical use of linear regression in various domains. The chapter explores the practical applications of linear regression, highlighting its use in predicting economic growth, product prices, housing sales, and score predictions, showcasing the diverse real-world applications of linear regression for predictive analysis.', 'The mathematical calculations behind linear regression, including the intuition, slope, coefficient, predicted values, and the concept of minimizing the distance between the regression line and the data points, are explained in detail, providing a comprehensive understanding of the key mathematical concepts. It provides a detailed explanation of the mathematical calculations behind linear regression, covering the intuition, slope, coefficient, predicted values, and the concept of minimizing the distance between the regression line and the data points, offering a thorough understanding of the mathematical principles underlying linear regression.', 'An overview of multiple linear regression and its implementation in Python for profit prediction is provided, demonstrating the extension of linear regression to accommodate multiple independent variables and its practical application in predictive modeling. The chapter concludes with an overview of multiple linear regression and its implementation in Python for profit prediction, showcasing the extension of linear regression to accommodate multiple independent variables and its practical application in predictive modeling.']}], 'duration': 870.892, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE97903.jpg', 'highlights': ['The chapter covers an introduction to machine learning, specific to linear regression and its applications.', 'The chapter also provides an example of machine learning.', 'The applications of linear regression, such as predicting economic growth, product prices, housing sales, and score predictions, are discussed, emphasizing the versatility and practical use of linear regression in various domains.', 'An overview of multiple linear regression and its implementation in Python for profit prediction is provided, demonstrating the extension of linear regression to accommodate multiple independent variables and its practical application in predictive modeling.', 'The mathematical calculations behind linear regression, including the intuition, slope, coefficient, predicted values, and the concept of minimizing the distance between the regression line and the data points, are explained in detail, providing a comprehensive understanding of the key mathematical concepts.', 'Linear regression is used to predict the relationship between independent and dependent variables, with a significant focus on the example of crop yield prediction based on rainfall, demonstrating the fundamental concepts of independent and dependent variables.']}, {'end': 1280.421, 'segs': [{'end': 998.447, 'src': 'embed', 'start': 969.036, 'weight': 0, 'content': [{'end': 973.449, 'text': "To start our coding, We're going to begin by importing some basic libraries.", 'start': 969.036, 'duration': 4.413}, {'end': 977.311, 'text': "And we're going to be looking through the data before we do any kind of linear regression.", 'start': 973.889, 'duration': 3.422}, {'end': 979.753, 'text': "We're going to take a look at the data to see what we're playing with.", 'start': 977.551, 'duration': 2.202}, {'end': 985.696, 'text': "Then we'll go ahead and format the data to the format we need to be able to run it in the linear regression model.", 'start': 980.433, 'duration': 5.263}, {'end': 990.339, 'text': "And then from there we'll go ahead and solve it and just see how valid our solution is.", 'start': 985.857, 'duration': 4.482}, {'end': 992.741, 'text': "So let's start with importing the basic libraries.", 'start': 990.7, 'duration': 2.041}, {'end': 998.447, 'text': "Now I'm going to be doing this in Anaconda Jupyter Notebook, a very popular IDE.", 'start': 993.061, 'duration': 5.386}], 'summary': 'Preparing and formatting data for linear regression in anaconda jupyter notebook.', 'duration': 29.411, 'max_score': 969.036, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE969036.jpg'}, {'end': 1081.237, 'src': 'embed', 'start': 1026.288, 'weight': 1, 'content': [{'end': 1033.27, 'text': 'The numpy, which stands for number Python, is usually denoted as np, and you have to almost have that for your sklearn toolbox.', 'start': 1026.288, 'duration': 6.982}, {'end': 1034.931, 'text': 'You always import that right off the beginning.', 'start': 1033.31, 'duration': 1.621}, {'end': 1038.854, 'text': "Pandas, although you don't have to have it for your sklearn libraries.", 'start': 1035.111, 'duration': 3.743}, {'end': 1045.137, 'text': 'it does such a wonderful job of importing data, setting it up into a data frame so we can manipulate it rather easily,', 'start': 1038.854, 'duration': 6.283}, {'end': 1047.46, 'text': 'and it has a lot of tools also in addition to that.', 'start': 1045.137, 'duration': 2.323}, {'end': 1051.142, 'text': "So we usually like to use the pandas when we can, and I'll show you what that looks like.", 'start': 1047.66, 'duration': 3.482}, {'end': 1055.866, 'text': 'The other three lines are for us to get a visual of this data and take a look at it.', 'start': 1051.463, 'duration': 4.403}, {'end': 1063.109, 'text': "So we're going to import matplotlibrary.pyplot as plt and then seaborn as sns.", 'start': 1056.306, 'duration': 6.803}, {'end': 1069.372, 'text': 'Seaborn works with the matplotlibrary, so you have to always import matplotlibrary and then seaborn sits on top of it.', 'start': 1063.529, 'duration': 5.843}, {'end': 1071.232, 'text': "And we'll take a look at what that looks like.", 'start': 1069.632, 'duration': 1.6}, {'end': 1074.374, 'text': 'You could use any of your own plotting libraries you want.', 'start': 1071.573, 'duration': 2.801}, {'end': 1076.095, 'text': "There's all kinds of ways to look at the data.", 'start': 1074.534, 'duration': 1.561}, {'end': 1081.237, 'text': 'These are just very common ones and the seaborn is so easy to use that it just looks beautiful.', 'start': 1076.415, 'duration': 4.822}], 'summary': 'Numpy and pandas are essential for sklearn; seaborn and matplotlib are commonly used for data visualization.', 'duration': 54.949, 'max_score': 1026.288, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE1026288.jpg'}, {'end': 1204.586, 'src': 'heatmap', 'start': 1092.12, 'weight': 0.773, 'content': [{'end': 1099.081, 'text': "My interface in the Anaconda Jupyter notebook requires I put that in there or you're not going to see the graph when it comes up.", 'start': 1092.12, 'duration': 6.961}, {'end': 1100.381, 'text': "Let's go ahead and run this.", 'start': 1099.301, 'duration': 1.08}, {'end': 1103.042, 'text': "It's not going to be that interesting because we're just setting up variables.", 'start': 1100.501, 'duration': 2.541}, {'end': 1108.723, 'text': "In fact it's not going to do anything that we can see but it is importing these different libraries and setup.", 'start': 1103.322, 'duration': 5.401}, {'end': 1114.283, 'text': 'The next step is load the data set and extract independent and dependent variables.', 'start': 1108.963, 'duration': 5.32}, {'end': 1124.083, 'text': "Here in the slide you'll see companies equals pd.read.csv and it has a long line there with the file at the end, 1000companies.csv.", 'start': 1115.379, 'duration': 8.704}, {'end': 1127.304, 'text': "You're going to have to change this to fit whatever setup you have.", 'start': 1124.263, 'duration': 3.041}, {'end': 1130.165, 'text': 'And the file itself you can request.', 'start': 1127.784, 'duration': 2.381}, {'end': 1134.387, 'text': 'Just go down to the commentary below this video and put a note in there,', 'start': 1130.605, 'duration': 3.782}, {'end': 1139.289, 'text': 'and Simply Learn will try to get in contact with you and supply you with that file, so you can try this coding yourself.', 'start': 1134.387, 'duration': 4.902}, {'end': 1141.35, 'text': "So we're going to add this code in here.", 'start': 1139.649, 'duration': 1.701}, {'end': 1146.095, 'text': "And we're going to see that I have companies equals pd.reader underscore csv.", 'start': 1141.61, 'duration': 4.485}, {'end': 1148.557, 'text': "And I've changed this path to match my computer.", 'start': 1146.275, 'duration': 2.282}, {'end': 1153.202, 'text': 'C colon slash simply learn slash 1000 underscore companies dot csv.', 'start': 1148.738, 'duration': 4.464}, {'end': 1158.127, 'text': "And then below there we're going to set the x equals to companies under the i location.", 'start': 1153.382, 'duration': 4.745}, {'end': 1161.03, 'text': 'And because this is companies is a pd data set.', 'start': 1158.268, 'duration': 2.762}, {'end': 1164.294, 'text': 'I can use this nice notation that says take every row.', 'start': 1161.271, 'duration': 3.023}, {'end': 1168.677, 'text': "That's what the first colon is, comma, except for the last column.", 'start': 1164.594, 'duration': 4.083}, {'end': 1173.581, 'text': "That's what the second part is, where we have a colon minus 1, and we want the values set into there.", 'start': 1168.778, 'duration': 4.803}, {'end': 1180.747, 'text': 'So x is no longer a data set, a Pandas data set, but we can easily extract the data from our Pandas data set with this notation.', 'start': 1173.922, 'duration': 6.825}, {'end': 1184.19, 'text': "And then y we're going to set equal to the last row.", 'start': 1180.987, 'duration': 3.203}, {'end': 1189.274, 'text': "Well, the question is going to be, what are we actually looking at? So let's go ahead and take a look at that.", 'start': 1184.37, 'duration': 4.904}, {'end': 1193.917, 'text': "And we're going to look at the companies.head, which lists the first five rows of data.", 'start': 1189.614, 'duration': 4.303}, {'end': 1197.38, 'text': "And I'll open up the file in just a second so you can see where that's coming from.", 'start': 1194.078, 'duration': 3.302}, {'end': 1201.283, 'text': "But let's look at the data in here as far as the way the pandas sees it.", 'start': 1197.54, 'duration': 3.743}, {'end': 1204.586, 'text': "When I hit run, you'll see it breaks it out into a nice setup.", 'start': 1201.383, 'duration': 3.203}], 'summary': 'Setting up and importing data in jupyter notebook using python and pandas for analysis and visualization.', 'duration': 112.466, 'max_score': 1092.12, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE1092120.jpg'}, {'end': 1130.165, 'src': 'embed', 'start': 1100.501, 'weight': 3, 'content': [{'end': 1103.042, 'text': "It's not going to be that interesting because we're just setting up variables.", 'start': 1100.501, 'duration': 2.541}, {'end': 1108.723, 'text': "In fact it's not going to do anything that we can see but it is importing these different libraries and setup.", 'start': 1103.322, 'duration': 5.401}, {'end': 1114.283, 'text': 'The next step is load the data set and extract independent and dependent variables.', 'start': 1108.963, 'duration': 5.32}, {'end': 1124.083, 'text': "Here in the slide you'll see companies equals pd.read.csv and it has a long line there with the file at the end, 1000companies.csv.", 'start': 1115.379, 'duration': 8.704}, {'end': 1127.304, 'text': "You're going to have to change this to fit whatever setup you have.", 'start': 1124.263, 'duration': 3.041}, {'end': 1130.165, 'text': 'And the file itself you can request.', 'start': 1127.784, 'duration': 2.381}], 'summary': 'Setting up variables and loading dataset for analysis.', 'duration': 29.664, 'max_score': 1100.501, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE1100501.jpg'}], 'start': 969.036, 'title': 'Linear regression data analysis', 'summary': 'Covers the process of importing basic libraries, formatting data, and visualizing it in anaconda jupyter notebook before running a linear regression model, using numpy, pandas, matplotlibrary, and seaborn, essential tools in data analysis and visualization.', 'chapters': [{'end': 1100.381, 'start': 969.036, 'title': 'Linear regression data analysis', 'summary': 'Discusses the process of importing basic libraries, formatting the data, and visualizing it in anaconda jupyter notebook before running a linear regression model, using numpy, pandas, matplotlibrary, and seaborn, all essential tools in data analysis and visualization.', 'duration': 131.345, 'highlights': ['The chapter discusses the process of importing basic libraries, formatting the data, and visualizing it in Anaconda Jupyter Notebook before running a linear regression model. The chapter emphasizes the importance of importing basic libraries, formatting the data, and visualizing it in Anaconda Jupyter Notebook before running a linear regression model.', "The numpy, which stands for number Python, is usually denoted as np, and you have to almost have that for your sklearn toolbox. Pandas, although you don't have to have it for your sklearn libraries, does a wonderful job of importing data and setting it up into a data frame. The use of numpy and pandas is highlighted, emphasizing the importance of numpy for the sklearn toolbox and the benefits of using pandas for importing and setting up data.", 'The chapter also covers importing matplotlibrary and seaborn for visualizing the data, with seaborn providing an easy-to-use and visually appealing representation of the data. The chapter explains the import of matplotlibrary and seaborn for data visualization, emphasizing the ease of use and visual appeal of seaborn.']}, {'end': 1280.421, 'start': 1100.501, 'title': 'Setting up variables for data analysis', 'summary': 'Introduces the process of setting up variables and loading a dataset for data analysis, demonstrating the use of pandas to import and manipulate the data, leading to a clear understanding of the dataset structure and content.', 'duration': 179.92, 'highlights': ['Demonstrating the process of setting up variables and loading a dataset for data analysis. The chapter focuses on the initial steps of setting up variables and loading a dataset for data analysis, providing a foundational understanding for further analysis.', 'Importing different libraries and setting up the data. The process involves importing different libraries and setting up the data, establishing the necessary environment for data analysis.', 'Utilizing Pandas to import and manipulate the dataset for analysis. The demonstration showcases the use of Pandas to import and manipulate the dataset, enabling efficient data analysis and manipulation.', 'Illustrating the structure of the dataset and its content using Pandas. The chapter provides a clear demonstration of using Pandas to understand the structure and content of the dataset, enhancing the comprehension of the data.']}], 'duration': 311.385, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE969036.jpg', 'highlights': ['The chapter emphasizes the importance of importing basic libraries, formatting the data, and visualizing it in Anaconda Jupyter Notebook before running a linear regression model.', 'The use of numpy and pandas is highlighted, emphasizing the importance of numpy for the sklearn toolbox and the benefits of using pandas for importing and setting up data.', 'The chapter explains the import of matplotlibrary and seaborn for data visualization, emphasizing the ease of use and visual appeal of seaborn.', 'The chapter focuses on the initial steps of setting up variables and loading a dataset for data analysis, providing a foundational understanding for further analysis.', 'The process involves importing different libraries and setting up the data, establishing the necessary environment for data analysis.', 'The demonstration showcases the use of Pandas to import and manipulate the dataset, enabling efficient data analysis and manipulation.', 'The chapter provides a clear demonstration of using Pandas to understand the structure and content of the dataset, enhancing the comprehension of the data.']}, {'end': 1738.394, 'segs': [{'end': 1373.903, 'src': 'heatmap', 'start': 1295.416, 'weight': 0, 'content': [{'end': 1299.102, 'text': "So let's flip back here and take a look at our next set of code, where we're going to graph it,", 'start': 1295.416, 'duration': 3.686}, {'end': 1301.385, 'text': 'so we can get a better understanding of our data and what it means.', 'start': 1299.102, 'duration': 2.283}, {'end': 1308.996, 'text': "So at this point we're going to use a single line of code to get a lot of information so we can see where we're going with this.", 'start': 1301.926, 'duration': 7.07}, {'end': 1313.541, 'text': "Let's go ahead and paste that into our notebook and see what we got going.", 'start': 1309.336, 'duration': 4.205}, {'end': 1318.005, 'text': "And so we have the visualization and again we're using SNS which is pandas.", 'start': 1313.761, 'duration': 4.244}, {'end': 1327.252, 'text': 'As you can see we imported the matplotlibrary.pyplot as plt which then the seaborn uses and we imported the seaborn as sns.', 'start': 1318.585, 'duration': 8.667}, {'end': 1332.816, 'text': 'And then that final line of code helps us show this in our inline coding.', 'start': 1327.572, 'duration': 5.244}, {'end': 1336.378, 'text': "Without this it wouldn't display and you could display it to a file and other means.", 'start': 1333.096, 'duration': 3.282}, {'end': 1339.641, 'text': "And that's the matplotlibrary inline with the amber sign at the beginning.", 'start': 1336.519, 'duration': 3.122}, {'end': 1341.803, 'text': 'So here we come down to the single line of code.', 'start': 1339.741, 'duration': 2.062}, {'end': 1345.706, 'text': 'Seaborn is great because it actually recognizes the panda data frame.', 'start': 1342.183, 'duration': 3.523}, {'end': 1351.971, 'text': 'So I can just take the companies.core for coordinates, and I can put that right into the Seaborn.', 'start': 1345.966, 'duration': 6.005}, {'end': 1355.294, 'text': 'And when we run this, we get this beautiful plot.', 'start': 1352.271, 'duration': 3.023}, {'end': 1357.575, 'text': "And let's just take a look at what this plot means.", 'start': 1355.654, 'duration': 1.921}, {'end': 1363.08, 'text': 'If you look at this plot on mine, the colors are probably a little bit more purplish and blue than the original one.', 'start': 1357.896, 'duration': 5.184}, {'end': 1365.721, 'text': 'We have the columns and the rows.', 'start': 1363.58, 'duration': 2.141}, {'end': 1369.702, 'text': 'We have R&D spending, we have administration, we have marketing spending, and profit.', 'start': 1365.761, 'duration': 3.941}, {'end': 1373.903, 'text': "And if you cross index any two of these, since we're interested in profit.", 'start': 1370.062, 'duration': 3.841}], 'summary': 'Code creates visualization of data using seaborn, showing r&d, administration, marketing spending, and profit.', 'duration': 62.159, 'max_score': 1295.416, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE1295416.jpg'}, {'end': 1461.146, 'src': 'heatmap', 'start': 1434.83, 'weight': 0.767, 'content': [{'end': 1440.275, 'text': 'Instead of just having a pretty picture, we need to generate some hard data, some hard values.', 'start': 1434.83, 'duration': 5.445}, {'end': 1442.096, 'text': "So let's see what that looks like.", 'start': 1440.715, 'duration': 1.381}, {'end': 1445.519, 'text': "We're going to set up our linear regression model in two steps.", 'start': 1442.316, 'duration': 3.203}, {'end': 1450.002, 'text': 'The first one is we need to prepare some of our data so it fits correctly.', 'start': 1445.839, 'duration': 4.163}, {'end': 1452.744, 'text': "Let's go ahead and paste this code into our Jupyter notebook.", 'start': 1450.362, 'duration': 2.382}, {'end': 1461.146, 'text': "And what we're bringing in is we're going to bring in the sklearn preprocessing where we're going to import the label encoder and the one hot encoder.", 'start': 1453.004, 'duration': 8.142}], 'summary': 'Generating hard data values using linear regression model setup in two steps.', 'duration': 26.316, 'max_score': 1434.83, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE1434830.jpg'}, {'end': 1658.639, 'src': 'heatmap', 'start': 1500.484, 'weight': 4, 'content': [{'end': 1504.628, 'text': "Now, to do a linear regression model, it doesn't know how to process New York.", 'start': 1500.484, 'duration': 4.144}, {'end': 1506.409, 'text': 'It knows how to process a number.', 'start': 1505.028, 'duration': 1.381}, {'end': 1512.254, 'text': "So the first thing we're going to do is we're going to change that New York, California, and Florida, and we're going to change those to numbers.", 'start': 1506.589, 'duration': 5.665}, {'end': 1514.416, 'text': "That's what this line of code does here.", 'start': 1512.595, 'duration': 1.821}, {'end': 1518.6, 'text': 'X equals, and then it has the colon comma three in brackets.', 'start': 1514.757, 'duration': 3.843}, {'end': 1523.244, 'text': "The first part, the colon comma, means that we're going to look at all the different rows.", 'start': 1518.86, 'duration': 4.384}, {'end': 1524.886, 'text': "So we're going to keep them all together.", 'start': 1523.605, 'duration': 1.281}, {'end': 1527.508, 'text': "But the only row we're going to edit is the third row.", 'start': 1525.006, 'duration': 2.502}, {'end': 1533.95, 'text': "And in there we're going to take the label coder and we're going to fit and transform the x also the third row.", 'start': 1527.887, 'duration': 6.063}, {'end': 1537.352, 'text': "So we're going to take that third row and we're going to set it equal to a transformation.", 'start': 1533.97, 'duration': 3.382}, {'end': 1544.496, 'text': 'And that transformation basically tells it that instead of having a New York it has a 0 or a 1 or a 2.', 'start': 1537.512, 'duration': 6.984}, {'end': 1547.098, 'text': 'And then finally we need to do a one hot encoder.', 'start': 1544.496, 'duration': 2.602}, {'end': 1551.202, 'text': 'which equals one hot encoder categorical features equals three.', 'start': 1547.438, 'duration': 3.764}, {'end': 1557.808, 'text': 'And then we take the x and we go ahead and do that equal to one hot encoder fit transform x to array.', 'start': 1551.422, 'duration': 6.386}, {'end': 1564.134, 'text': "This final transformation preps our data for us so it's completely set the way we need it as just a row of numbers.", 'start': 1558.028, 'duration': 6.106}, {'end': 1569.518, 'text': "Even though it's not in here, let's go ahead and print x and just take a look at what this data is doing.", 'start': 1564.534, 'duration': 4.984}, {'end': 1573.622, 'text': "You'll see you have an array of arrays and then each array is a row of numbers.", 'start': 1569.638, 'duration': 3.984}, {'end': 1580.447, 'text': "And if I go ahead and just do row 0, you'll see I have a nice organized row of numbers that the computer now understands.", 'start': 1574.022, 'duration': 6.425}, {'end': 1584.691, 'text': "We'll go ahead and take this out there because it doesn't mean a whole lot to us, it's just a row of numbers.", 'start': 1580.807, 'duration': 3.884}, {'end': 1590.746, 'text': 'Next, on setting up our data, we have avoiding dummy variable trap.', 'start': 1585.763, 'duration': 4.983}, {'end': 1592.447, 'text': 'This is very important.', 'start': 1591.186, 'duration': 1.261}, {'end': 1600.571, 'text': "Why? Because the computer has automatically transformed our header into the setup, and it's automatically transferred all these different variables.", 'start': 1592.847, 'duration': 7.724}, {'end': 1610.394, 'text': 'So when we did the encoder, The encoder created two columns and what we need to do is just have the one because it has both the variable and the name.', 'start': 1601.031, 'duration': 9.363}, {'end': 1612.374, 'text': "That's what this piece of code does here.", 'start': 1610.614, 'duration': 1.76}, {'end': 1618.095, 'text': "Let's go ahead and paste this in here and we have x equals x colon comma one colon.", 'start': 1612.534, 'duration': 5.561}, {'end': 1625.317, 'text': 'All this is doing is removing that one extra column we put in there when we did our one hot encoder and our label encoding.', 'start': 1618.315, 'duration': 7.002}, {'end': 1626.497, 'text': "Let's go ahead and run that.", 'start': 1625.437, 'duration': 1.06}, {'end': 1629.639, 'text': 'And now we get to create our linear regression model.', 'start': 1626.957, 'duration': 2.682}, {'end': 1631.52, 'text': "And let's see what that looks like here.", 'start': 1629.859, 'duration': 1.661}, {'end': 1633.462, 'text': "And we're going to do that in two steps.", 'start': 1631.54, 'duration': 1.922}, {'end': 1637.504, 'text': 'The first step is going to be in splitting the data.', 'start': 1633.942, 'duration': 3.562}, {'end': 1645.75, 'text': 'Now whenever we create a predictive model of data, we always want to split it up so we have a training set and we have a testing set.', 'start': 1638.105, 'duration': 7.645}, {'end': 1647.091, 'text': "That's very important.", 'start': 1646.15, 'duration': 0.941}, {'end': 1651.013, 'text': "Otherwise we'd be very unethical without testing it to see how good our fit is.", 'start': 1647.231, 'duration': 3.782}, {'end': 1656.157, 'text': "And then we'll go ahead and create our multiple linear regression model and train it and set it up.", 'start': 1651.374, 'duration': 4.783}, {'end': 1658.639, 'text': "Let's go ahead and paste this next piece of code in here.", 'start': 1656.457, 'duration': 2.182}], 'summary': 'The transcript explains the process of preparing data for a linear regression model, including transforming categorical variables to numerical values and avoiding dummy variable trap.', 'duration': 40.324, 'max_score': 1500.484, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE1500484.jpg'}], 'start': 1295.416, 'title': 'Graphing data and linear regression', 'summary': 'Covers graphing data with seaborn, emphasizing the ease of visualization and understanding using a single line of code. it also discusses linear regression modeling, highlighting the importance of data preparation, and the process of splitting data into training and testing sets to avoid the dummy variable trap.', 'chapters': [{'end': 1357.575, 'start': 1295.416, 'title': 'Graphing data with seaborn', 'summary': 'Discusses using a single line of code to visualize and understand data using seaborn, which recognizes the pandas data frame, ultimately producing a beautiful plot.', 'duration': 62.159, 'highlights': ['Seaborn recognizes the panda data frame, allowing for easy visualization of data.', 'Using a single line of code, a lot of information can be obtained to understand the data better.', 'The matplotlibrary and seaborn libraries are used for graphing the data, providing a better understanding of the data.', 'The final line of code enables the visualization to be displayed inline, enhancing the data exploration process.']}, {'end': 1738.394, 'start': 1357.896, 'title': 'Linear regression modeling', 'summary': 'Discusses the visualization of data and the process of preparing and setting up the data for linear regression modeling, emphasizing the importance of splitting the data into training and testing sets and avoiding the dummy variable trap.', 'duration': 380.498, 'highlights': ['The chapter discusses the visualization of data and the process of preparing and setting up the data for linear regression modeling. Visualization of data, preparing and setting up data for linear regression modeling', 'Emphasizes the importance of splitting the data into training and testing sets. Importance of splitting data into training and testing sets', 'Explains the process of avoiding the dummy variable trap by removing the extra column created during one hot encoding and label encoding. Process of avoiding the dummy variable trap by removing extra column']}], 'duration': 442.978, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE1295416.jpg', 'highlights': ['Seaborn recognizes the panda data frame, allowing for easy visualization of data.', 'Using a single line of code, a lot of information can be obtained to understand the data better.', 'The matplotlibrary and seaborn libraries are used for graphing the data, providing a better understanding of the data.', 'The final line of code enables the visualization to be displayed inline, enhancing the data exploration process.', 'Emphasizes the importance of splitting the data into training and testing sets.', 'Explains the process of avoiding the dummy variable trap by removing the extra column created during one hot encoding and label encoding.']}, {'end': 2142.988, 'segs': [{'end': 1828.479, 'src': 'embed', 'start': 1803.378, 'weight': 0, 'content': [{'end': 1811.304, 'text': "In this case, we do X train and Y train because we're using the training data, X being the data in and Y being profit, what we're looking at.", 'start': 1803.378, 'duration': 7.926}, {'end': 1813.245, 'text': 'And this does all that math for us.', 'start': 1811.604, 'duration': 1.641}, {'end': 1817.608, 'text': "So within one click and one line, we've created the whole linear regression model.", 'start': 1813.565, 'duration': 4.043}, {'end': 1820.47, 'text': 'And we fit the data to the linear regression model.', 'start': 1817.868, 'duration': 2.602}, {'end': 1824.934, 'text': 'And you can see that when I run the regressor It gives an output linear regression.', 'start': 1820.65, 'duration': 4.284}, {'end': 1828.479, 'text': 'it says copy x equals true, fit intercept equals true.', 'start': 1824.934, 'duration': 3.545}], 'summary': 'Using x train and y train, a linear regression model is created with one click, fitting the data and generating an output.', 'duration': 25.101, 'max_score': 1803.378, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE1803378.jpg'}, {'end': 1926.99, 'src': 'embed', 'start': 1900.541, 'weight': 1, 'content': [{'end': 1905.426, 'text': "We're going to take a short detour here and we're going to be calculating the coefficient and intercepts.", 'start': 1900.541, 'duration': 4.885}, {'end': 1907.028, 'text': 'So you can see what those look like.', 'start': 1905.707, 'duration': 1.321}, {'end': 1912.56, 'text': "What's really nice about our regressor we created is it already has a coefficient for us.", 'start': 1907.537, 'duration': 5.023}, {'end': 1916.723, 'text': 'We can simply just print regressor.coefficient underscore.', 'start': 1912.821, 'duration': 3.902}, {'end': 1919.365, 'text': "When I run this, you'll see our coefficients here.", 'start': 1916.944, 'duration': 2.421}, {'end': 1924.829, 'text': 'And if we can do the regressor coefficient, we can also do the regressor intercept.', 'start': 1919.585, 'duration': 5.244}, {'end': 1926.99, 'text': "Let's run that and take a look at that.", 'start': 1925.389, 'duration': 1.601}], 'summary': 'Calculating the coefficient and intercepts for the regressor.', 'duration': 26.449, 'max_score': 1900.541, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE1900541.jpg'}, {'end': 2041.875, 'src': 'embed', 'start': 1999.389, 'weight': 2, 'content': [{'end': 2002.59, 'text': "And so we're going to use this from sklearn.metrics.", 'start': 1999.389, 'duration': 3.201}, {'end': 2004.151, 'text': "We're going to import R2 score.", 'start': 2002.81, 'duration': 1.341}, {'end': 2006.752, 'text': "That's the R squared value.", 'start': 2005.171, 'duration': 1.581}, {'end': 2008.093, 'text': "We're looking at the error.", 'start': 2006.772, 'duration': 1.321}, {'end': 2013.876, 'text': 'So in the R2 score, we take our y test versus our y predict.', 'start': 2008.293, 'duration': 5.583}, {'end': 2016.677, 'text': "y test is the actual values we're testing.", 'start': 2014.596, 'duration': 2.081}, {'end': 2018.918, 'text': 'That was the one that was given to us that we know are true.', 'start': 2016.697, 'duration': 2.221}, {'end': 2022.961, 'text': 'The y predict of those 200 values is what we think it was true.', 'start': 2019.079, 'duration': 3.882}, {'end': 2025.562, 'text': 'And when we go ahead and run this, we see we get a .', 'start': 2023.141, 'duration': 2.421}, {'end': 2026.262, 'text': "9352 That's the R2 score.", 'start': 2025.562, 'duration': 0.7}, {'end': 2036.631, 'text': "Now, it's not exactly a straight percentage, so it's not saying it's 93% correct, but you do want that in the upper 90s.", 'start': 2029.384, 'duration': 7.247}, {'end': 2041.875, 'text': 'O and higher shows that this is a very valid prediction based on the R2 score.', 'start': 2037.211, 'duration': 4.664}], 'summary': 'Using sklearn.metrics, r2 score of .9352 indicates valid prediction.', 'duration': 42.486, 'max_score': 1999.389, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE1999389.jpg'}], 'start': 1738.774, 'title': 'Linear regression model creation and validation', 'summary': 'Details the creation of a linear regression model using sklearn.linear_model to fit and predict data, displaying coefficients and intercepts, thereby simplifying the calculation process and producing 200 profit predictions. it also presents the process of validating a linear regression model using r squared value, achieving an r2 score of .9352 indicating a successful model, and concluding with key takeaways on machine learning, linear regression application, and prediction.', 'chapters': [{'end': 1981.763, 'start': 1738.774, 'title': 'Creating linear regression model', 'summary': 'Details the creation of a linear regression model using sklearn.linear_model to fit and predict data, displaying coefficients and intercepts, thereby simplifying the calculation process and producing 200 profit predictions.', 'duration': 242.989, 'highlights': ['The creation of a linear regression model using sklearn.linear_model and fitting the training data simplifies the calculation process and enables the prediction of 200 profit values. 200 profit predictions', 'Displaying the coefficients and intercepts provides insight into the variables and their impact on the linear regression model. Insight into the impact of variables', 'Utilizing the regressor.coefficient_ and regressor.intercept_ simplifies the calculation of coefficients and intercepts. Simplified calculation process']}, {'end': 2142.988, 'start': 1982.043, 'title': 'Validating linear regression model', 'summary': 'Presents the process of validating a linear regression model using r squared value, achieving an r2 score of .9352 indicating a successful model, and concluding with key takeaways on machine learning, linear regression application, and prediction.', 'duration': 160.945, 'highlights': ['The R2 score of .9352 indicates a successful linear regression model, suggesting a high level of accuracy in predicting the outcome (93.52%).', 'The chapter outlines key takeaways on machine learning, linear regression application, and prediction, providing a comprehensive overview of the covered topics.', 'The process involves importing R2 score from sklearn.metrics, comparing y test versus y predict, and achieving an R2 score of .9352, demonstrating the effectiveness of the model in predicting outcomes.']}], 'duration': 404.214, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NUXdtN1W1FE/pics/NUXdtN1W1FE1738774.jpg', 'highlights': ['The creation of a linear regression model using sklearn.linear_model simplifies the calculation process and enables the prediction of 200 profit values.', 'Displaying the coefficients and intercepts provides insight into the variables and their impact on the linear regression model.', 'The R2 score of .9352 indicates a successful linear regression model, suggesting a high level of accuracy in predicting the outcome (93.52%).', 'The process involves importing R2 score from sklearn.metrics, comparing y test versus y predict, and achieving an R2 score of .9352, demonstrating the effectiveness of the model in predicting outcomes.']}], 'highlights': ['Using linear regression, we can predict the profit of a company based on its expenses.', 'Companies spending more on R&D tend to have higher profits.', "R&D expenditure is a good indicator of a company's potential profit.", 'The R2 score of .9352 indicates a successful linear regression model, suggesting a high level of accuracy in predicting the outcome (93.52%).', 'The creation of a linear regression model using sklearn.linear_model simplifies the calculation process and enables the prediction of 200 profit values.', 'The process involves importing R2 score from sklearn.metrics, comparing y test versus y predict, and achieving an R2 score of .9352, demonstrating the effectiveness of the model in predicting outcomes.', 'The chapter covers an introduction to machine learning, specific to linear regression and its applications.', 'The applications of linear regression, such as predicting economic growth, product prices, housing sales, and score predictions, are discussed, emphasizing the versatility and practical use of linear regression in various domains.', 'An overview of multiple linear regression and its implementation in Python for profit prediction is provided, demonstrating the extension of linear regression to accommodate multiple independent variables and its practical application in predictive modeling.', 'The mathematical calculations behind linear regression, including the intuition, slope, coefficient, predicted values, and the concept of minimizing the distance between the regression line and the data points, are explained in detail, providing a comprehensive understanding of the key mathematical concepts.', 'Linear regression is used to predict the relationship between independent and dependent variables, with a significant focus on the example of crop yield prediction based on rainfall, demonstrating the fundamental concepts of independent and dependent variables.', 'The chapter emphasizes the importance of importing basic libraries, formatting the data, and visualizing it in Anaconda Jupyter Notebook before running a linear regression model.', 'The use of numpy and pandas is highlighted, emphasizing the importance of numpy for the sklearn toolbox and the benefits of using pandas for importing and setting up data.', 'The chapter explains the import of matplotlibrary and seaborn for data visualization, emphasizing the ease of use and visual appeal of seaborn.', 'The chapter focuses on the initial steps of setting up variables and loading a dataset for data analysis, providing a foundational understanding for further analysis.', 'Seaborn recognizes the panda data frame, allowing for easy visualization of data.', 'Using a single line of code, a lot of information can be obtained to understand the data better.', 'The matplotlibrary and seaborn libraries are used for graphing the data, providing a better understanding of the data.', 'The final line of code enables the visualization to be displayed inline, enhancing the data exploration process.', 'Emphasizes the importance of splitting the data into training and testing sets.', 'Explains the process of avoiding the dummy variable trap by removing the extra column created during one hot encoding and label encoding.']}