title
Machine Learning Tutorial Python - 17: L1 and L2 Regularization | Lasso, Ridge Regression

description
In this python machine learning tutorial for beginners we will look into, 1) What is overfitting, underfitting 2) How to address overfitting using L1 and L2 regularization 3) Write code in python and sklearn for housing price prediction where we will see a model overfit when we use simple linear regression. Then we will use Lasso regression (L1 regularization) and ridge regression (L2 regression) to address this overfitting issue #MachineLearning #PythonMachineLearning #MachineLearningTutorial #Python #PythonTutorial #PythonTraining #MachineLearningCource #L1andL2Regularization #Regularization #sklearntutorials #scikitlearntutorials 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. Machine learning tutorials: https://www.youtube.com/watch?v=gmvvaobm7eQ&list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw Popular Playlists: Data Science Project: https://www.youtube.com/watch?v=rdfbcdP75KI&list=PLeo1K3hjS3uu7clOTtwsp94PcHbzqpAdg Data Science Full Course: https://www.youtube.com/playlist?list=PLeo1K3hjS3us_ELKYSj_Fth2tIEkdKXvV 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 Medias #️⃣ 🔗 Discord: https://discord.gg/r42Kbuk 📸 Dhaval's Personal Instagram: https://www.instagram.com/dhavalsays/ 📸 Instagram: https://www.instagram.com/codebasicshub/ 🌎 Website: https://codebasics.io/ 🔊 Facebook: https://www.facebook.com/codebasicshub 📱 Twitter: https://twitter.com/codebasicshub 📝 Linkedin: https://www.linkedin.com/company/codebasics/ ❗❗ DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers'.

detail
{'title': 'Machine Learning Tutorial Python - 17: L1 and L2 Regularization | Lasso, Ridge Regression', 'heatmap': [{'end': 430.958, 'start': 340.172, 'weight': 0.982}], 'summary': 'This tutorial covers l1 and l2 regularization techniques in machine learning with python, addressing overfitting and improving accuracy on a housing price dataset. it discusses reducing overfitting in linear regression and building a model with 34857 records and 21 columns, achieving significant accuracy improvement.', 'chapters': [{'end': 216.413, 'segs': [{'end': 66.864, 'src': 'embed', 'start': 22.664, 'weight': 0, 'content': [{'end': 29.789, 'text': 'which is an overfit, can be addressed and the accuracy can be improved when you use L1 and L2 regularization.', 'start': 22.664, 'duration': 7.125}, {'end': 40.692, 'text': "We will be using a housing price data set from the city of Melbourne and we'll first build a model using a simple linear regression and we'll see it is overfitting the model.", 'start': 30.369, 'duration': 10.323}, {'end': 50.736, 'text': "and then we'll use L1 and L2 regularization and we'll see how we address that overfitting issue and how it improves the score on our test set.", 'start': 40.692, 'duration': 10.044}, {'end': 51.856, 'text': "So let's get started.", 'start': 50.936, 'duration': 0.92}, {'end': 57.322, 'text': "Let's say you're trying to predict number of matches won based on the age.", 'start': 52.981, 'duration': 4.341}, {'end': 66.864, 'text': 'Now usually when the player gets aged, any sports person or athlete gets aged, the matches won kind of reduces.', 'start': 57.782, 'duration': 9.082}], 'summary': 'Using l1 and l2 regularization improves accuracy in housing price prediction and addresses overfitting in the model.', 'duration': 44.2, 'max_score': 22.664, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VqKq78PVO9g/pics/VqKq78PVO9g22664.jpg'}, {'end': 216.413, 'src': 'embed', 'start': 171.947, 'weight': 2, 'content': [{'end': 180.973, 'text': 'the line will look like a curve and it can generalize your data really well so that tomorrow, if new data point comes in,', 'start': 171.947, 'duration': 9.026}, {'end': 184.895, 'text': 'this equation will be able to make a better prediction for you.', 'start': 180.973, 'duration': 3.922}, {'end': 190.92, 'text': 'so the first case is called underfitting, the second case is called overfitting and the third case is balance fit.', 'start': 184.895, 'duration': 6.025}, {'end': 202.422, 'text': 'so you kind of get an idea on overfitting here where If you try to run training too much and try to fit too much to your training data set,', 'start': 190.92, 'duration': 11.502}, {'end': 205.544, 'text': 'then you will have issues with testing data set.', 'start': 202.422, 'duration': 3.122}, {'end': 211.329, 'text': 'When you try to predict your new data points, it might not do a better prediction.', 'start': 205.564, 'duration': 5.765}, {'end': 216.413, 'text': 'So you always have to keep a balance between these two extreme cases.', 'start': 211.349, 'duration': 5.064}], 'summary': 'Balancing underfitting and overfitting for better prediction.', 'duration': 44.466, 'max_score': 171.947, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VqKq78PVO9g/pics/VqKq78PVO9g171947.jpg'}], 'start': 0.409, 'title': 'Regularization and balancing in ml', 'summary': 'Covers l1 and l2 regularization techniques in machine learning with a python code example, addressing overfitting and improving accuracy on a housing price dataset. it also explores the trade-off between underfitting and overfitting, emphasizing the need for balanced model building.', 'chapters': [{'end': 66.864, 'start': 0.409, 'title': 'L1 and l2 regularization in machine learning', 'summary': 'Discusses l1 and l2 regularization to address overfitting in machine learning, using a python code to improve the accuracy of a model on a housing price dataset, and also explains the impact of age on matches won in sports.', 'duration': 66.455, 'highlights': ['L1 and L2 regularization techniques used to address overfitting in machine learning, demonstrated through Python code on a housing price dataset.', 'Improvement in model accuracy achieved by applying L1 and L2 regularization techniques.', 'Impact of age on the number of matches won by sports persons or athletes.']}, {'end': 216.413, 'start': 67.404, 'title': 'Balancing underfitting and overfitting', 'summary': 'Discusses the trade-off between underfitting, overfitting, and balance fit in model building, emphasizing the need to find a balance to ensure accurate predictions for new data points.', 'duration': 149.009, 'highlights': ['The chapter explains the concept of underfitting, overfitting, and balance fit in model building, emphasizing the need to maintain a balance to ensure accurate predictions for new data points, with underfitting and overfitting being the extreme cases.', 'It illustrates the impact of underfitting, overfitting, and balance fit using examples of simple linear regression and higher order polynomial equations, highlighting the need to find a balance between simplicity and complexity in model building.', 'The speaker stresses the importance of maintaining a balance between underfitting and overfitting in model training, as excessive fitting to the training data can lead to poor predictions for new data points during testing.']}], 'duration': 216.004, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VqKq78PVO9g/pics/VqKq78PVO9g409.jpg', 'highlights': ['L1 and L2 regularization techniques used to address overfitting in machine learning, demonstrated through Python code on a housing price dataset.', 'Improvement in model accuracy achieved by applying L1 and L2 regularization techniques.', 'The chapter explains the concept of underfitting, overfitting, and balance fit in model building, emphasizing the need to maintain a balance to ensure accurate predictions for new data points, with underfitting and overfitting being the extreme cases.', 'It illustrates the impact of underfitting, overfitting, and balance fit using examples of simple linear regression and higher order polynomial equations, highlighting the need to find a balance between simplicity and complexity in model building.', 'The speaker stresses the importance of maintaining a balance between underfitting and overfitting in model training, as excessive fitting to the training data can lead to poor predictions for new data points during testing.', 'Impact of age on the number of matches won by sports persons or athletes.']}, {'end': 456.534, 'segs': [{'end': 292.154, 'src': 'embed', 'start': 262.757, 'weight': 1, 'content': [{'end': 271.203, 'text': 'so the idea here is to shrink your parameters, your parameters, which is theta zero, theta three, theta four, even theta two, theta one.', 'start': 262.757, 'duration': 8.446}, {'end': 282.872, 'text': 'if you can reduce this parameter, if you can keep these parameters smaller, then you can get a better equation for your prediction function.', 'start': 271.203, 'duration': 11.669}, {'end': 284.313, 'text': 'now, how do we do that?', 'start': 282.872, 'duration': 1.441}, {'end': 292.154, 'text': 'we earlier saw in our linear regression video that we calculate mean squared error.', 'start': 285.569, 'duration': 6.585}], 'summary': 'Shrink parameters to improve prediction function; use mean squared error.', 'duration': 29.397, 'max_score': 262.757, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VqKq78PVO9g/pics/VqKq78PVO9g262757.jpg'}, {'end': 436.702, 'src': 'heatmap', 'start': 324.093, 'weight': 4, 'content': [{'end': 326.735, 'text': 'So in our case it will be age of a person.', 'start': 324.093, 'duration': 2.642}, {'end': 331.458, 'text': 'If you are thinking about housing price prediction it will be the size of the house.', 'start': 326.775, 'duration': 4.683}, {'end': 340.052, 'text': 'Now just think that in your Mean square error function.', 'start': 333.159, 'duration': 6.893}, {'end': 348.857, 'text': 'So, by the way, this mean square error function We use during training and we want to minimize the value of this, this error, you know,', 'start': 340.172, 'duration': 8.685}, {'end': 350.258, 'text': 'on each iteration.', 'start': 348.857, 'duration': 1.401}, {'end': 355.26, 'text': 'so, in this Equation, what if I add this particular parameter??', 'start': 350.258, 'duration': 5.002}, {'end': 356.481, 'text': 'So what is this??', 'start': 355.901, 'duration': 0.58}, {'end': 359.163, 'text': 'So there is this lambda, which is a free parameter.', 'start': 357.101, 'duration': 2.062}, {'end': 359.963, 'text': 'You can control it.', 'start': 359.203, 'duration': 0.76}, {'end': 369.11, 'text': "It's like a tuning knob and and you are making a square of each of these theta parameters.", 'start': 359.983, 'duration': 9.127}, {'end': 374.891, 'text': 'so now, if your theta gets bigger, this value will get bigger.', 'start': 369.11, 'duration': 5.781}, {'end': 381.453, 'text': 'the error will be big, you know, and your model will not converge.', 'start': 374.891, 'duration': 6.562}, {'end': 387.095, 'text': "so essentially, what you're doing is you are penalizing higher values of theta here.", 'start': 381.453, 'duration': 5.642}, {'end': 392.523, 'text': 'So whenever model tries to make the theta value higher, you are adding a penalty here.', 'start': 388.001, 'duration': 4.522}, {'end': 399.327, 'text': "So by adding this penalty, you're making sure your theta value doesn't go too high.", 'start': 392.904, 'duration': 6.423}, {'end': 401.488, 'text': 'So they will remain very small.', 'start': 399.687, 'duration': 1.801}, {'end': 406.791, 'text': 'You can fine tune this thing using this parameter lambda here.', 'start': 402.609, 'duration': 4.182}, {'end': 411.594, 'text': 'And if you make this bigger, the theta value will even get smaller and smaller.', 'start': 407.592, 'duration': 4.002}, {'end': 415.216, 'text': 'And if you get this smaller, then theta value can be bigger.', 'start': 412.614, 'duration': 2.602}, {'end': 418.13, 'text': 'So this is called L2 regularization.', 'start': 416.209, 'duration': 1.921}, {'end': 424.374, 'text': 'It is called L2 because we are using the square and in L1 regularization you are using the absolute value.', 'start': 418.651, 'duration': 5.723}, {'end': 430.958, 'text': 'So that is the only difference between L1 and L2 that in L1 you are using absolute value of theta parameter.', 'start': 424.414, 'duration': 6.544}, {'end': 436.702, 'text': 'Here again if theta is bigger the error overall error is big.', 'start': 431.459, 'duration': 5.243}], 'summary': 'Using l2 regularization with lambda parameter to penalize higher theta values for model convergence.', 'duration': 112.609, 'max_score': 324.093, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VqKq78PVO9g/pics/VqKq78PVO9g324093.jpg'}, {'end': 430.958, 'src': 'embed', 'start': 381.453, 'weight': 0, 'content': [{'end': 387.095, 'text': "so essentially, what you're doing is you are penalizing higher values of theta here.", 'start': 381.453, 'duration': 5.642}, {'end': 392.523, 'text': 'So whenever model tries to make the theta value higher, you are adding a penalty here.', 'start': 388.001, 'duration': 4.522}, {'end': 399.327, 'text': "So by adding this penalty, you're making sure your theta value doesn't go too high.", 'start': 392.904, 'duration': 6.423}, {'end': 401.488, 'text': 'So they will remain very small.', 'start': 399.687, 'duration': 1.801}, {'end': 406.791, 'text': 'You can fine tune this thing using this parameter lambda here.', 'start': 402.609, 'duration': 4.182}, {'end': 411.594, 'text': 'And if you make this bigger, the theta value will even get smaller and smaller.', 'start': 407.592, 'duration': 4.002}, {'end': 415.216, 'text': 'And if you get this smaller, then theta value can be bigger.', 'start': 412.614, 'duration': 2.602}, {'end': 418.13, 'text': 'So this is called L2 regularization.', 'start': 416.209, 'duration': 1.921}, {'end': 424.374, 'text': 'It is called L2 because we are using the square and in L1 regularization you are using the absolute value.', 'start': 418.651, 'duration': 5.723}, {'end': 430.958, 'text': 'So that is the only difference between L1 and L2 that in L1 you are using absolute value of theta parameter.', 'start': 424.414, 'duration': 6.544}], 'summary': 'L2 regularization penalizes higher theta values to keep them small, using lambda to fine-tune and control the shrinkage of theta values.', 'duration': 49.505, 'max_score': 381.453, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VqKq78PVO9g/pics/VqKq78PVO9g381453.jpg'}], 'start': 218.075, 'title': 'Reducing overfitting and l2 regularization', 'summary': 'Discusses reducing overfitting in linear regression by shrinking parameters to minimize mean square error, and explains l2 regularization using a tuning parameter lambda to penalize higher theta values for simpler equations and model convergence.', 'chapters': [{'end': 350.258, 'start': 218.075, 'title': 'Reducing overfitting in linear regression', 'summary': 'Discusses reducing overfitting in linear regression by shrinking parameters, such as theta values, to obtain a better prediction equation, ultimately minimizing mean square error during training.', 'duration': 132.183, 'highlights': ['Shrinking parameters, such as theta values, can lead to a better prediction equation.', 'The importance of keeping parameters smaller for a better prediction function is emphasized.', 'Explanation of mean square error and its role in training to minimize error.']}, {'end': 456.534, 'start': 350.258, 'title': 'L2 regularization in machine learning', 'summary': 'Explains l2 regularization in machine learning, highlighting the use of a tuning parameter lambda to penalize higher values of theta and ensure smaller theta values, ultimately leading to simpler equations and convergence of the model.', 'duration': 106.276, 'highlights': ['L2 regularization uses a tuning parameter lambda to penalize higher values of theta, ensuring smaller theta values and smaller overall error, ultimately leading to simpler equations and model convergence.', 'The lambda parameter acts as a tuning knob, controlling the penalty for higher theta values, with larger lambda values resulting in smaller theta values, and vice versa.', 'L2 regularization is distinguished by its use of the square of theta parameters, in contrast to L1 regularization which uses the absolute value of theta parameters.']}], 'duration': 238.459, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VqKq78PVO9g/pics/VqKq78PVO9g218075.jpg', 'highlights': ['L2 regularization uses lambda to penalize higher theta values for simpler equations and model convergence.', 'Shrinking parameters leads to a better prediction equation.', 'Importance of keeping parameters smaller for a better prediction function is emphasized.', 'Lambda parameter acts as a tuning knob, controlling the penalty for higher theta values.', 'Explanation of mean square error and its role in training to minimize error.']}, {'end': 1160.036, 'segs': [{'end': 483.741, 'src': 'embed', 'start': 456.855, 'weight': 0, 'content': [{'end': 463.056, 'text': 'And simpler equations are the best to represent the generic case of your prediction.', 'start': 456.855, 'duration': 6.201}, {'end': 465.157, 'text': "All right, so let's get into coding now.", 'start': 463.516, 'duration': 1.641}, {'end': 470.738, 'text': "For the coding, I'm using housing price data set for Melbourne city.", 'start': 465.577, 'duration': 5.161}, {'end': 472.838, 'text': 'I got this data set from Kaggle.', 'start': 471.058, 'duration': 1.78}, {'end': 476.259, 'text': 'And we are going to build a linear regression model.', 'start': 473.579, 'duration': 2.68}, {'end': 483.741, 'text': 'So you can see that there are different features such as room, the distance, postal code, bathroom card, and so on.', 'start': 476.399, 'duration': 7.342}], 'summary': 'Using melbourne housing price data from kaggle, a linear regression model is being built with various features.', 'duration': 26.886, 'max_score': 456.855, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VqKq78PVO9g/pics/VqKq78PVO9g456855.jpg'}, {'end': 566.799, 'src': 'embed', 'start': 507.085, 'weight': 1, 'content': [{'end': 516.61, 'text': "I'm going to do some exploration of my data frame now and kind of print of unique values in data set.", 'start': 507.085, 'duration': 9.525}, {'end': 521.033, 'text': 'So you see like there are 351 suburbs, these many addresses and so on.', 'start': 516.63, 'duration': 4.403}, {'end': 525.395, 'text': 'Also, if you look at the shape of the data set.', 'start': 521.673, 'duration': 3.722}, {'end': 540.013, 'text': "there are three four eight five seven uh records and 21 columns in total Now I'm going to discard certain columns.", 'start': 528.487, 'duration': 11.526}, {'end': 549.462, 'text': "I just did visual observation and discarded certain columns, which I don't think are very useful.", 'start': 540.533, 'duration': 8.929}, {'end': 555.788, 'text': "So for example, date, right? So date is, it's not useful.", 'start': 550.043, 'duration': 5.745}, {'end': 561.714, 'text': 'So I will just say, okay, here are the columns that are useful.', 'start': 556.048, 'duration': 5.666}, {'end': 566.799, 'text': "I'm just doing copy paste to save the time on recording.", 'start': 563.698, 'duration': 3.101}], 'summary': 'Exploration of data frame: 351 suburbs, 34857 records, 21 columns. discarded non-useful columns like date.', 'duration': 59.714, 'max_score': 507.085, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VqKq78PVO9g/pics/VqKq78PVO9g507085.jpg'}, {'end': 708.004, 'src': 'embed', 'start': 672.613, 'weight': 2, 'content': [{'end': 676.436, 'text': 'With NA value now, I made it 0.', 'start': 672.613, 'duration': 3.823}, {'end': 684.962, 'text': "Now, what we're going to do is we'll look at certain other columns, such as land size and building area.", 'start': 676.436, 'duration': 8.526}, {'end': 694.378, 'text': 'And we will like these two and we will calculate the mean and will fill those with the mean value.', 'start': 686.594, 'duration': 7.784}, {'end': 698.119, 'text': 'So the way you do that is using this function.', 'start': 695.698, 'duration': 2.421}, {'end': 704.182, 'text': "So you're doing length size dot fill NA with the mean of the same column.", 'start': 698.88, 'duration': 5.302}, {'end': 708.004, 'text': 'OK, so this is a safe safe assumption.', 'start': 705.003, 'duration': 3.001}], 'summary': 'Filled na values with mean for land size and building area.', 'duration': 35.391, 'max_score': 672.613, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VqKq78PVO9g/pics/VqKq78PVO9g672613.jpg'}, {'end': 778.704, 'src': 'embed', 'start': 745.778, 'weight': 3, 'content': [{'end': 750.742, 'text': 'So if you have a couple of random columns with any value, you can drop it.', 'start': 745.778, 'duration': 4.964}, {'end': 756.847, 'text': "You know our data set is huge and if you drop like these three and three six rows, it's not a big deal.", 'start': 750.922, 'duration': 5.925}, {'end': 760.891, 'text': 'So I will just drop them and run.', 'start': 757.588, 'duration': 3.303}, {'end': 763.313, 'text': 'The same function again.', 'start': 762.272, 'duration': 1.041}, {'end': 769.157, 'text': 'And you see like none of the columns have any values now.', 'start': 764.634, 'duration': 4.523}, {'end': 778.704, 'text': 'so now, uh, i have some categorical features which i want to convert into dummies.', 'start': 770.379, 'duration': 8.325}], 'summary': 'Data columns with any value dropped, no columns have any values now. categorical features to be converted into dummies.', 'duration': 32.926, 'max_score': 745.778, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VqKq78PVO9g/pics/VqKq78PVO9g745778.jpg'}, {'end': 1135.497, 'src': 'embed', 'start': 1107.913, 'weight': 5, 'content': [{'end': 1110.555, 'text': "you know it's pretty, pretty good.", 'start': 1107.913, 'duration': 2.642}, {'end': 1114.84, 'text': 'so you saw that by using ridge regression and lasso regression.', 'start': 1110.555, 'duration': 4.285}, {'end': 1117.843, 'text': 'so ridge is l2, lasso is one.', 'start': 1114.84, 'duration': 3.003}, {'end': 1126.671, 'text': 'your accuracy for your not seen data samples, which is your taste sample, improved a whole lot.', 'start': 1117.843, 'duration': 8.828}, {'end': 1131.955, 'text': 'if you are trying to learn machine learning, you can just in YouTube.', 'start': 1127.692, 'duration': 4.263}, {'end': 1135.497, 'text': 'you can just search core basics machine learning tutorials.', 'start': 1131.955, 'duration': 3.542}], 'summary': 'Ridge and lasso regression improved accuracy for not seen data samples.', 'duration': 27.584, 'max_score': 1107.913, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VqKq78PVO9g/pics/VqKq78PVO9g1107913.jpg'}], 'start': 456.855, 'title': 'Linear regression model and data handling', 'summary': 'Covers building a linear regression model with 34857 records and 21 columns, handling na values, overfitting, and achieving a significant accuracy improvement.', 'chapters': [{'end': 566.799, 'start': 456.855, 'title': 'Linear regression model on melbourne housing price data', 'summary': 'Covers building a linear regression model using housing price data for melbourne city from kaggle, with 34857 records and 21 columns, exploring and discarding certain columns based on visual observation.', 'duration': 109.944, 'highlights': ['Building a linear regression model using housing price data for Melbourne city obtained from Kaggle.', 'The dataset consists of 34857 records and 21 columns in total.', 'Exploring the dataset by printing unique values, including 351 suburbs and addresses.', 'Discarding certain columns based on visual observation to focus on useful features for the model.']}, {'end': 1160.036, 'start': 566.799, 'title': 'Handling na values and regularization', 'summary': 'Covers handling na values in a dataset by filling them with zero and mean, dropping irrelevant columns, converting categorical features to dummies, creating x and y for linear regression, and addressing overfitting by using lasso and ridge regression, resulting in a significant improvement in accuracy.', 'duration': 593.237, 'highlights': ['Handling NA values by filling with zero and mean', 'Converting categorical features to dummies', 'Addressing overfitting with lasso and ridge regression']}], 'duration': 703.181, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VqKq78PVO9g/pics/VqKq78PVO9g456855.jpg', 'highlights': ['Building a linear regression model using housing price data for Melbourne city obtained from Kaggle.', 'The dataset consists of 34857 records and 21 columns in total.', 'Handling NA values by filling with zero and mean', 'Converting categorical features to dummies', 'Exploring the dataset by printing unique values, including 351 suburbs and addresses.', 'Addressing overfitting with lasso and ridge regression', 'Discarding certain columns based on visual observation to focus on useful features for the model.']}], 'highlights': ['Building a linear regression model using housing price data for Melbourne city obtained from Kaggle.', 'The dataset consists of 34857 records and 21 columns in total.', 'L1 and L2 regularization techniques used to address overfitting in machine learning, demonstrated through Python code on a housing price dataset.', 'Improvement in model accuracy achieved by applying L1 and L2 regularization techniques.', 'L2 regularization uses lambda to penalize higher theta values for simpler equations and model convergence.', 'Handling NA values by filling with zero and mean', 'Exploring the dataset by printing unique values, including 351 suburbs and addresses.', 'The chapter explains the concept of underfitting, overfitting, and balance fit in model building, emphasizing the need to maintain a balance to ensure accurate predictions for new data points, with underfitting and overfitting being the extreme cases.', 'The speaker stresses the importance of maintaining a balance between underfitting and overfitting in model training, as excessive fitting to the training data can lead to poor predictions for new data points during testing.', 'Explanation of mean square error and its role in training to minimize error.']}