title
Machine Learning Tutorial Python - 2: Linear Regression Single Variable

description
In this tutorial we will predict home prices using linear regression. We use training data that has home areas in square feet and corresponding prices and train a linear regression model using sklearn linear regression class. Later on predict method is used on linear regression object to make actual forecast. Exercise CSV file is here: https://github.com/codebasics/py/tree/master/ML/1_linear_reg/Exercise Code in this tutorial is here: https://github.com/codebasics/py/tree/master/ML/1_linear_reg (check the .ipynb file) To download csv and code for all tutorials: go to https://github.com/codebasics/py, click on a green button to clone or download the entire repository and then go to relevant folder to get access to that specific file. #MachineLearning #PythonMachineLearning #MachineLearningTutorial #Python #PythonTutorial #PythonTraining #MachineLearningCource #LinearRegression #sklearntutorials #scikitlearntutorials Topics that are covered in this Machine Learning Video: 0:00 Simple linear regression 1:59 Linear equation 2:22 Import data in dataframe 2:43 Import sklearn library 3:52 Plot scatter plot 5:26 Create Linear Regression object 13:35 Exercise at the end to predict canada's per capita income Topic Highlights: 1) What is linear regression 2) Mean squared error 3) Predict home prices by minimizing mean squared error (or MSE) 4) Exercise at the end to predict canada's per capita income Do you want to learn technology from me? Check https://codebasics.io/?utm_source=description&utm_medium=yt&utm_campaign=description&utm_id=description for my affordable video courses. Next Video: Machine Learning Tutorial Python - 3: Linear Regression Multiple Variables: https://www.youtube.com/watch?v=J_LnPL3Qg70&list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw&index=3 Very Simple Explanation Of Neural Network: https://www.youtube.com/watch?v=ER2It2mIagI Code: https://github.com/codebasics/py/tree/master/ML/1_linear_reg Correction: at 6:53, use reg.predict([[3300]]) instead of reg.predict(3300) as api specification has changed. Exercise solution: https://github.com/codebasics/py/blob/master/ML/1_linear_reg/1_linear_regression.ipynb Populor Playlist: Data Science Full Course: https://www.youtube.com/playlist?list=PLeo1K3hjS3us_ELKYSj_Fth2tIEkdKXvV Data Science Project: https://www.youtube.com/watch?v=rdfbcdP75KI&list=PLeo1K3hjS3uu7clOTtwsp94PcHbzqpAdg Machine learning tutorials: https://www.youtube.com/watch?v=gmvvaobm7eQ&list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw Pandas: https://www.youtube.com/watch?v=CmorAWRsCAw&list=PLeo1K3hjS3uuASpe-1LjfG5f14Bnozjwy matplotlib: https://www.youtube.com/watch?v=qqwf4Vuj8oM&list=PLeo1K3hjS3uu4Lr8_kro2AqaO6CFYgKOl Python: https://www.youtube.com/watch?v=eykoKxsYtow&list=PLeo1K3hjS3uv5U-Lmlnucd7gqF-3ehIh0&index=1 Jupyter Notebook: https://www.youtube.com/watch?v=q_BzsPxwLOE&list=PLeo1K3hjS3uuZPwzACannnFSn9qHn8to8 Tools and Libraries: Scikit learn tutorials Sklearn tutorials Machine learning with scikit learn tutorials Machine learning with sklearn tutorials 🌎 My Website For Video Courses: https://codebasics.io/?utm_source=description&utm_medium=yt&utm_campaign=description&utm_id=description Need help building software or data analytics and AI solutions? My company https://www.atliq.com/ can help. Click on the Contact button on that website. #️⃣ Social Media #️⃣ 🔗 Discord: https://discord.gg/r42Kbuk 📸 Dhaval's Personal Instagram: https://www.instagram.com/dhavalsays/ 📸 Codebasics Instagram: https://www.instagram.com/codebasicshub/ 🔊 Facebook: https://www.facebook.com/codebasicshub 📱 Twitter: https://twitter.com/codebasicshub 📝 Linkedin (Personal): https://www.linkedin.com/in/dhavalsays/ 📝 Linkedin (Codebasics): https://www.linkedin.com/company/codebasics/ 🔗 Patreon: https://www.patreon.com/codebasics?fan_landing=true

detail
{'title': 'Machine Learning Tutorial Python - 2: Linear Regression Single Variable', 'heatmap': [{'end': 413.706, 'start': 333.518, 'weight': 0.727}, {'end': 673.369, 'start': 648.067, 'weight': 0.723}], 'summary': "Learn to predict home prices using simple linear regression in python, building a machine learning model to predict home prices for areas of 3300 and 5000 square feet while minimizing error. the training includes 2d array data frame, price prediction, coefficients, intercepts, and predictive modeling for home prices and canada's income using regression models.", 'chapters': [{'end': 311.742, 'segs': [{'end': 44.714, 'src': 'embed', 'start': 18.423, 'weight': 0, 'content': [{'end': 27.03, 'text': "using this data, we'll build a machine learning model that can tell me the prices of the homes whose area is 3300 square feet and 5000 square feet.", 'start': 18.423, 'duration': 8.607}, {'end': 38.468, 'text': 'You can plot available prices and areas in form of a scatter plot like this, where this red marker shows the available data points.', 'start': 29.54, 'duration': 8.928}, {'end': 44.714, 'text': 'Now we can draw this blue line, which best fits these data points.', 'start': 39.409, 'duration': 5.305}], 'summary': 'Building a machine learning model to predict home prices for areas of 3300 and 5000 square feet.', 'duration': 26.291, 'max_score': 18.423, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8jazNUpO3lQ/pics/8jazNUpO3lQ18423.jpg'}, {'end': 104.387, 'src': 'embed', 'start': 72.533, 'weight': 1, 'content': [{'end': 74.835, 'text': 'okay?. So why did I choose this blue line?', 'start': 72.533, 'duration': 2.302}, {'end': 88.147, 'text': 'What we do is we calculate this delta, which is an error between the actual data point and the data point which is predicted by your linear equation.', 'start': 75.395, 'duration': 12.752}, {'end': 96.522, 'text': 'We square individual errors and we sum them up and we try to minimize those.', 'start': 89.238, 'duration': 7.284}, {'end': 100.184, 'text': 'So we do this procedure for all these lines.', 'start': 97.363, 'duration': 2.821}, {'end': 104.387, 'text': 'So for orange, red and blue line, I repeated that procedure.', 'start': 100.325, 'duration': 4.062}], 'summary': 'Calculating errors and minimizing them for different lines.', 'duration': 31.854, 'max_score': 72.533, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8jazNUpO3lQ/pics/8jazNUpO3lQ72533.jpg'}, {'end': 206.145, 'src': 'embed', 'start': 142.449, 'weight': 2, 'content': [{'end': 153.972, 'text': 'area is called an independent variable, whereas price is called a dependent variable because we are calculating price based on this area.', 'start': 142.449, 'duration': 11.523}, {'end': 158.853, 'text': 'now we are going to write python code for doing home price predictions.', 'start': 153.972, 'duration': 4.881}, {'end': 164.355, 'text': 'here i have launched my jupyter notebook and i have imported some useful libraries.', 'start': 158.853, 'duration': 5.502}, {'end': 168.451, 'text': 'The most important library here is from sklearn.', 'start': 165.39, 'duration': 3.061}, {'end': 169.831, 'text': 'import linear model.', 'start': 168.451, 'duration': 1.38}, {'end': 171.771, 'text': 'So sklearn is the library.', 'start': 170.271, 'duration': 1.5}, {'end': 173.552, 'text': 'It is also called scikit-learn.', 'start': 171.951, 'duration': 1.601}, {'end': 187.575, 'text': 'So if you Google it, you will find that this is the library we are using and it comes with anaconda installation.', 'start': 173.892, 'duration': 13.683}, {'end': 194.456, 'text': 'So once you have installed anaconda, you should have this library available for import.', 'start': 188.915, 'duration': 5.541}, {'end': 201.121, 'text': 'I have the prices available in form of this CSV file.', 'start': 196.497, 'duration': 4.624}, {'end': 206.145, 'text': "So the first thing I'm going to do is load this prices in pandas data frame.", 'start': 201.141, 'duration': 5.004}], 'summary': 'Using sklearn library in python to make home price predictions based on area.', 'duration': 63.696, 'max_score': 142.449, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8jazNUpO3lQ/pics/8jazNUpO3lQ142449.jpg'}, {'end': 270.767, 'src': 'embed', 'start': 235.315, 'weight': 3, 'content': [{'end': 244.359, 'text': "next thing that I'm going to do is plot a scatter plot, just to get an idea on the distribution of my data point.", 'start': 235.315, 'duration': 9.044}, {'end': 259.046, 'text': 'okay and you all know if you have used Jupyter notebook before is that you have to use matplotlib, inline magic, in order to draw the plots,', 'start': 244.359, 'duration': 14.687}, {'end': 265.944, 'text': "and first I'm going to plot.scatter.", 'start': 259.046, 'duration': 6.898}, {'end': 270.767, 'text': "So I'm going to plot area versus price.", 'start': 266.584, 'duration': 4.183}], 'summary': 'Plotting scatter plot to visualize data distribution.', 'duration': 35.452, 'max_score': 235.315, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8jazNUpO3lQ/pics/8jazNUpO3lQ235315.jpg'}], 'start': 0.109, 'title': 'Predicting home prices with python', 'summary': 'Discusses using simple linear regression to predict home prices based on the area of the home using python, where a machine learning model is built to predict home prices for areas of 3300 and 5000 square feet, and the best fit line is determined by minimizing the error between the actual and predicted data points.', 'chapters': [{'end': 311.742, 'start': 0.109, 'title': 'Predicting home prices with python', 'summary': 'Discusses using simple linear regression to predict home prices based on the area of the home using python, where a machine learning model is built to predict home prices for areas of 3300 and 5000 square feet, and the best fit line is determined by minimizing the error between the actual and predicted data points.', 'duration': 311.633, 'highlights': ['A machine learning model is built to predict home prices for areas of 3300 and 5000 square feet.', 'Determining the best fit line by minimizing the error between the actual and predicted data points.', 'Usage of scikit-learn library for machine learning model implementation.', 'Plotting a scatter plot to visualize the distribution of data points for area versus price.', 'Introduction to the concept of dependent and independent variables in the context of home price prediction.']}], 'duration': 311.633, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8jazNUpO3lQ/pics/8jazNUpO3lQ109.jpg', 'highlights': ['A machine learning model is built to predict home prices for areas of 3300 and 5000 square feet.', 'Determining the best fit line by minimizing the error between the actual and predicted data points.', 'Usage of scikit-learn library for machine learning model implementation.', 'Plotting a scatter plot to visualize the distribution of data points for area versus price.', 'Introduction to the concept of dependent and independent variables in the context of home price prediction.']}, {'end': 480.044, 'segs': [{'end': 373.601, 'src': 'embed', 'start': 349.284, 'weight': 2, 'content': [{'end': 356.949, 'text': 'So I will create an object for linear regression, and then I will fit my data.', 'start': 349.284, 'duration': 7.665}, {'end': 366.436, 'text': 'So fitting the data means you are training the linear regression model using the available data points.', 'start': 357.229, 'duration': 9.207}, {'end': 373.601, 'text': 'okay?. Now the first argument has to be like a 2D array, so you can supply your data frame here.', 'start': 366.436, 'duration': 7.165}], 'summary': 'Creating a linear regression object and fitting data for training.', 'duration': 24.317, 'max_score': 349.284, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8jazNUpO3lQ/pics/8jazNUpO3lQ349284.jpg'}, {'end': 480.044, 'src': 'heatmap', 'start': 333.518, 'weight': 0, 'content': [{'end': 338.898, 'text': 'So you can see that from sklearn Python module.', 'start': 333.518, 'duration': 5.38}, {'end': 347.351, 'text': 'I have already imported linear model and here linear regression.', 'start': 339.018, 'duration': 8.333}, {'end': 356.949, 'text': 'So I will create an object for linear regression, and then I will fit my data.', 'start': 349.284, 'duration': 7.665}, {'end': 366.436, 'text': 'So fitting the data means you are training the linear regression model using the available data points.', 'start': 357.229, 'duration': 9.207}, {'end': 373.601, 'text': 'okay?. Now the first argument has to be like a 2D array, so you can supply your data frame here.', 'start': 366.436, 'duration': 7.165}, {'end': 387.093, 'text': 'so i am going to supply a data frame which only contains area, okay, and the second argument would be uh, your y-axis on your plot,', 'start': 374.341, 'duration': 12.752}, {'end': 388.495, 'text': 'which is your price.', 'start': 387.093, 'duration': 1.402}, {'end': 402.561, 'text': 'So when I execute this, it worked okay, without any error, which means now this linear regression model is ready to predict the prices.', 'start': 393.237, 'duration': 9.324}, {'end': 405.062, 'text': "So let's do a prediction.", 'start': 402.981, 'duration': 2.081}, {'end': 413.706, 'text': 'So what we wanted to predict was the price of a home whose area is 3, 300 square feet.', 'start': 405.362, 'duration': 8.344}, {'end': 418.788, 'text': 'And you can see that it predicted this price.', 'start': 415.306, 'duration': 3.482}, {'end': 424.326, 'text': 'Now you might be wondering why it came up with this price.', 'start': 419.764, 'duration': 4.562}, {'end': 426.747, 'text': "So let's look at some of the internal details.", 'start': 424.366, 'duration': 2.381}, {'end': 440.913, 'text': 'So when I train my linear regression object using this available data, what happened is it calculated the coefficient and intercept.', 'start': 428.228, 'duration': 12.685}, {'end': 451.179, 'text': 'So, if you go back to our mathematical equation, you know that for any linear equation there is a slope and intercept.', 'start': 441.233, 'duration': 9.946}, {'end': 453.541, 'text': 'this is also called a coefficient.', 'start': 451.179, 'duration': 2.362}, {'end': 457.403, 'text': 'so my model calculated the value for m and b.', 'start': 453.541, 'duration': 3.862}, {'end': 459.745, 'text': "so let's see what are the values.", 'start': 457.403, 'duration': 2.342}, {'end': 475.317, 'text': 'okay, so when you do reg.coef, it will show the value of coefficient, which is this m, and when you do intercept,', 'start': 459.745, 'duration': 15.572}, {'end': 480.044, 'text': 'This is showing you the value for this B.', 'start': 476.482, 'duration': 3.562}], 'summary': 'Trained a linear regression model to predict home prices with sklearn, obtaining coefficients and intercepts.', 'duration': 74.682, 'max_score': 333.518, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8jazNUpO3lQ/pics/8jazNUpO3lQ333518.jpg'}], 'start': 311.742, 'title': 'Linear regression model training', 'summary': 'Covers training a linear regression model using a 2d array data frame, price prediction, and calculation of coefficients and intercepts.', 'chapters': [{'end': 480.044, 'start': 311.742, 'title': 'Linear regression model training and prediction', 'summary': 'Discusses training a linear regression model using a 2d array data frame and predicting prices, with a demonstration of coefficient and intercept calculation for the model.', 'duration': 168.302, 'highlights': ['The model is trained using a 2D array data frame for area and price, and successfully predicts the price of a home with an area of 3,300 square feet.', 'Demonstration of coefficient and intercept calculation for the linear regression model through the values of m and b.', 'Creation of a linear regression object and fitting the data, training the model using available data points.']}], 'duration': 168.302, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8jazNUpO3lQ/pics/8jazNUpO3lQ311742.jpg', 'highlights': ['Demonstration of coefficient and intercept calculation for the linear regression model through the values of m and b.', 'The model is trained using a 2D array data frame for area and price, and successfully predicts the price of a home with an area of 3,300 square feet.', 'Creation of a linear regression object and fitting the data, training the model using available data points.']}, {'end': 913.54, 'segs': [{'end': 636.179, 'src': 'embed', 'start': 555.946, 'weight': 0, 'content': [{'end': 566.272, 'text': 'uh, you might have a csv file like this where you have list of prices, a list of areas available, and you want to predict, uh,', 'start': 555.946, 'duration': 10.326}, {'end': 567.833, 'text': 'the prices for these homes.', 'start': 566.272, 'duration': 1.561}, {'end': 574.698, 'text': 'right until now, what we did is We were individually giving the area and we are predicting the prices.', 'start': 567.833, 'duration': 6.865}, {'end': 585.86, 'text': 'So what I want to do now is generate another CSV file where I have these list of areas and their corresponding prediction on prices.', 'start': 575.178, 'duration': 10.682}, {'end': 594.502, 'text': 'So for this, I will create a data frame first using read CSV.', 'start': 586.961, 'duration': 7.541}, {'end': 596.543, 'text': 'So here.', 'start': 595.863, 'duration': 0.68}, {'end': 601.469, 'text': 'first i created a data frame.', 'start': 599.608, 'duration': 1.861}, {'end': 607.994, 'text': 'i have list of areas available.', 'start': 601.469, 'duration': 6.525}, {'end': 617.48, 'text': 'now i will use regression model to actually predict the prices.', 'start': 607.994, 'duration': 9.486}, {'end': 620.342, 'text': "okay, so i'll just supply d here.", 'start': 617.48, 'duration': 2.862}, {'end': 624.031, 'text': 'that will return me the prices.', 'start': 622.089, 'duration': 1.942}, {'end': 636.179, 'text': 'so you can store the prices in variable P and then what you can do is in your original data frame you can create a new column.', 'start': 624.031, 'duration': 12.148}], 'summary': 'Using a regression model to predict home prices based on areas and creating a new csv file with the predictions.', 'duration': 80.233, 'max_score': 555.946, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8jazNUpO3lQ/pics/8jazNUpO3lQ555946.jpg'}, {'end': 673.369, 'src': 'heatmap', 'start': 648.067, 'weight': 0.723, 'content': [{'end': 662.915, 'text': 'you can see the prices are available and then you can just use to csv method to export the value to prediction.csv.', 'start': 648.067, 'duration': 14.848}, {'end': 673.369, 'text': 'so if i open my prediction.csv, uh, you will now find that i have area and prices.', 'start': 662.915, 'duration': 10.454}], 'summary': 'Csv method used to export prices for area prediction', 'duration': 25.302, 'max_score': 648.067, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8jazNUpO3lQ/pics/8jazNUpO3lQ648067.jpg'}, {'end': 740.369, 'src': 'embed', 'start': 698.203, 'weight': 4, 'content': [{'end': 712.326, 'text': 'okay, so once you have this model built in, you can apply this model on a huge csv file and come up with a list of predictions.', 'start': 698.203, 'duration': 14.123}, {'end': 730.045, 'text': 'now, going back to our original example, uh, So let me go back again here and read my original prices and do a fitting on that.', 'start': 712.326, 'duration': 17.719}, {'end': 740.369, 'text': 'What I want to show you is how does my linear equation line look?', 'start': 730.926, 'duration': 9.443}], 'summary': 'Build model to make predictions on large csv file', 'duration': 42.166, 'max_score': 698.203, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8jazNUpO3lQ/pics/8jazNUpO3lQ698203.jpg'}, {'end': 820.29, 'src': 'embed', 'start': 775.207, 'weight': 2, 'content': [{'end': 783.252, 'text': "okay, so let's go step by step.", 'start': 775.207, 'duration': 8.045}, {'end': 790.917, 'text': "so here i have my scatter plot and what i'm doing is plt dot plot on my x-axis.", 'start': 783.252, 'duration': 7.665}, {'end': 795.758, 'text': 'i want df dot area on my y-axis.', 'start': 790.917, 'duration': 4.841}, {'end': 799.439, 'text': 'I want to.', 'start': 795.758, 'duration': 3.681}, {'end': 807.981, 'text': 'I Want to predict area like this.', 'start': 799.439, 'duration': 8.542}, {'end': 816.128, 'text': 'Okay, so it shows the visual representation of my linear equation here and All right.', 'start': 807.981, 'duration': 8.147}, {'end': 818.329, 'text': "So that's all I had for this tutorial.", 'start': 816.448, 'duration': 1.881}, {'end': 820.29, 'text': 'I have an exercise for you guys.', 'start': 818.529, 'duration': 1.761}], 'summary': 'Creating a scatter plot to predict area with linear equation.', 'duration': 45.083, 'max_score': 775.207, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8jazNUpO3lQ/pics/8jazNUpO3lQ775207.jpg'}, {'end': 899.318, 'src': 'embed', 'start': 844.789, 'weight': 1, 'content': [{'end': 854.638, 'text': 'the link of Jupyter notebook available in the video description below so download the notebook, study it first and then download the exercise folder.', 'start': 844.789, 'duration': 9.849}, {'end': 865.139, 'text': "in the exercise folder, you will find this CSV file, which has Canada's per capita income for the year 1970 to 2016,", 'start': 854.638, 'duration': 10.501}, {'end': 877.006, 'text': 'and your job is to find out the predicted uh income in year 2020..', 'start': 865.139, 'duration': 11.867}, {'end': 884.232, 'text': 'i highly recommend that you do the exercise, because just by watching the video, you are not probably going to learn that much.', 'start': 877.006, 'duration': 7.226}, {'end': 886.774, 'text': "i mean you learn something, but it's not very effective.", 'start': 884.232, 'duration': 2.542}, {'end': 889.976, 'text': "so it's it's better that you do some practice as well.", 'start': 886.774, 'duration': 3.202}, {'end': 899.318, 'text': "uh, as we go through these tutorials and i'll make sure i provide simple exercises at the end of every tutorial okay, so again, just to summarize,", 'start': 889.976, 'duration': 9.342}], 'summary': "Find canada's per capita income for 1970-2016 and predict 2020 income.", 'duration': 54.529, 'max_score': 844.789, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8jazNUpO3lQ/pics/8jazNUpO3lQ844789.jpg'}], 'start': 480.044, 'title': 'Predictive modeling with regression', 'summary': "Covers predictive modeling for home prices and canada's income using regression models, including calculations for specific area sizes, visualization of linear equations, and utilizing historical data for prediction, emphasizing practical learning exercises.", 'chapters': [{'end': 698.203, 'start': 480.044, 'title': 'Predicting home prices with regression model', 'summary': 'Discusses using a regression model to predict home prices based on area, demonstrating the process by calculating prices for areas of 3300 and 5000 square feet and exporting the predictions to a csv file.', 'duration': 218.159, 'highlights': ['Using regression model to predict home prices based on area', 'Exporting predictions to a CSV file', 'Creating a data frame and new column for predicted prices']}, {'end': 820.29, 'start': 698.203, 'title': 'Applying linear model for predictions', 'summary': 'Demonstrates building a model to make predictions from a csv file, visualizing a linear equation through scatter plots, and identifying areas for improvement in the code.', 'duration': 122.087, 'highlights': ['The chapter demonstrates building a model to make predictions from a CSV file, illustrating practical application of the model on a large dataset.', "Visualizing a linear equation through scatter plots, providing a clear understanding of the model's performance.", 'Identifying errors in the code and providing guidance for improvement, enhancing the learning experience.']}, {'end': 913.54, 'start': 821.471, 'title': "Predicting canada's income 2020", 'summary': "Involves predicting canada's net income in 2020 using a simple linear regression model with provided per capita income data from 1970 to 2016, emphasizing the importance of practical exercises for effective learning.", 'duration': 92.069, 'highlights': ["Provided a CSV file with Canada's per capita income data from 1970 to 2016 for predicting the net income in 2020.", 'Emphasized the importance of practical exercises for effective learning alongside video tutorials.', 'Stressed the significance of practicing alongside video tutorials for enhanced learning outcomes.']}], 'duration': 433.496, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8jazNUpO3lQ/pics/8jazNUpO3lQ480044.jpg', 'highlights': ['Using regression model to predict home prices based on area', "Provided a CSV file with Canada's per capita income data from 1970 to 2016 for predicting the net income in 2020", "Visualizing a linear equation through scatter plots, providing a clear understanding of the model's performance", 'Creating a data frame and new column for predicted prices', 'The chapter demonstrates building a model to make predictions from a CSV file, illustrating practical application of the model on a large dataset', 'Identifying errors in the code and providing guidance for improvement, enhancing the learning experience', 'Emphasized the importance of practical exercises for effective learning alongside video tutorials', 'Stressed the significance of practicing alongside video tutorials for enhanced learning outcomes', 'Exporting predictions to a CSV file']}], 'highlights': ['A machine learning model is built to predict home prices for areas of 3300 and 5000 square feet.', 'Demonstration of coefficient and intercept calculation for the linear regression model through the values of m and b.', 'Determining the best fit line by minimizing the error between the actual and predicted data points.', 'Usage of scikit-learn library for machine learning model implementation.', 'The model is trained using a 2D array data frame for area and price, and successfully predicts the price of a home with an area of 3,300 square feet.', 'Using regression model to predict home prices based on area', "Provided a CSV file with Canada's per capita income data from 1970 to 2016 for predicting the net income in 2020", "Visualizing a linear equation through scatter plots, providing a clear understanding of the model's performance", 'Creating a data frame and new column for predicted prices', 'The chapter demonstrates building a model to make predictions from a CSV file, illustrating practical application of the model on a large dataset', 'Identifying errors in the code and providing guidance for improvement, enhancing the learning experience', 'Emphasized the importance of practical exercises for effective learning alongside video tutorials', 'Stressed the significance of practicing alongside video tutorials for enhanced learning outcomes', 'Exporting predictions to a CSV file', 'Plotting a scatter plot to visualize the distribution of data points for area versus price.', 'Introduction to the concept of dependent and independent variables in the context of home price prediction.']}