title
Logistic Regression in Python | Logistic Regression Example | Machine Learning Algorithms | Edureka
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This Edureka Video on Logistic Regression in Python will give you basic understanding of Logistic Regression Machine Learning Algorithm with examples. In this video, you will also get to see demo on Logistic Regression using Python. Below are the topics covered in this tutorial:
1:10 What is Regression?
3:22 What is Logistic Regression: What & Why?
8:43 Linear Vs Logistic Regression
10:13 Logistic Regression Use Cases
12:14 Logistic Regression Example Demo in Python
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{'title': 'Logistic Regression in Python | Logistic Regression Example | Machine Learning Algorithms | Edureka', 'heatmap': [{'end': 1675.121, 'start': 1642.45, 'weight': 0.797}, {'end': 2226.003, 'start': 2186.721, 'weight': 1}], 'summary': 'Tutorial series on logistic regression in python covers the significance of logistic regression in predicting relationships, including an introduction to regression, different types of regression, real-life use cases, practical implementation, predictive modeling techniques, data analysis, visualization using python libraries, titanic dataset analysis, data preprocessing, and model training, achieving an accuracy score of 78% and 89% for suv prediction.', 'chapters': [{'end': 59.624, 'segs': [{'end': 54.619, 'src': 'embed', 'start': 27.088, 'weight': 0, 'content': [{'end': 31.01, 'text': "So we'll start off the session by getting a quick introduction to what is regression.", 'start': 27.088, 'duration': 3.922}, {'end': 35.812, 'text': "then we'll see the different types of regression and we'll be discussing the what and why of logistic regression.", 'start': 31.01, 'duration': 4.802}, {'end': 42.215, 'text': "So in this part we'll discuss what exactly it is, where it is used, why it is used and all those things.", 'start': 36.272, 'duration': 5.943}, {'end': 49.078, 'text': 'moving ahead will compare linear regression versus logistic regression, along with the various real-life use cases and finally, towards the end.', 'start': 42.215, 'duration': 6.863}, {'end': 51.999, 'text': "I'll be practically implementing logistic regression algorithm.", 'start': 49.198, 'duration': 2.801}, {'end': 54.619, 'text': 'So I hope you guys are clear with this agenda.', 'start': 52.717, 'duration': 1.902}], 'summary': 'Introduction to regression, types and application of logistic regression, comparison with linear regression, and practical implementation of logistic regression algorithm.', 'duration': 27.531, 'max_score': 27.088, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ27088.jpg'}], 'start': 7.915, 'title': 'Logistic regression for predicting relationships', 'summary': 'Discusses the significance of logistic regression in predicting relationships, including an introduction to regression, different types of regression, comparison of linear regression versus logistic regression, real-life use cases, and practical implementation of logistic regression algorithm.', 'chapters': [{'end': 59.624, 'start': 7.915, 'title': 'Logistic regression for predicting relationships', 'summary': 'Discusses the significance of logistic regression in predicting relationships, covering an introduction to regression, different types of regression, comparison of linear regression versus logistic regression, real-life use cases, and practical implementation of logistic regression algorithm.', 'duration': 51.709, 'highlights': ['The chapter covers the significance of logistic regression in predicting relationships, including the what and why of logistic regression, real-life use cases, and practical implementation of the algorithm.', 'The session provides a comparison between linear regression and logistic regression, highlighting the differences and real-life use cases.', 'The training includes an introduction to regression and different types of regression, providing a comprehensive understanding of the topic.']}], 'duration': 51.709, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ7915.jpg', 'highlights': ['The chapter covers the significance of logistic regression in predicting relationships, including the what and why of logistic regression, real-life use cases, and practical implementation of the algorithm.', 'The session provides a comparison between linear regression and logistic regression, highlighting the differences and real-life use cases.', 'The training includes an introduction to regression and different types of regression, providing a comprehensive understanding of the topic.']}, {'end': 934.022, 'segs': [{'end': 243.063, 'src': 'embed', 'start': 212.232, 'weight': 0, 'content': [{'end': 215.873, 'text': 'So here you need to predict the outcome of a categorical dependent variable.', 'start': 212.232, 'duration': 3.641}, {'end': 219.814, 'text': 'So the outcome should be always discrete or categorical in nature.', 'start': 216.293, 'duration': 3.521}, {'end': 224.755, 'text': 'Now by discrete I mean the value should be binary or you can say you just have two values.', 'start': 220.174, 'duration': 4.581}, {'end': 226.715, 'text': 'It can either be 0 or 1.', 'start': 224.875, 'duration': 1.84}, {'end': 230.836, 'text': 'It can either be yes or no either be true or false or high or low.', 'start': 226.715, 'duration': 4.121}, {'end': 232.937, 'text': 'So only these can be the outcomes.', 'start': 231.196, 'duration': 1.741}, {'end': 237.88, 'text': 'So the value which you need to predict should be discrete or you can say categorical in nature.', 'start': 233.497, 'duration': 4.383}, {'end': 243.063, 'text': 'Whereas in linear regression, we have the value of Y or you can say the value need to predict is in a range.', 'start': 238.44, 'duration': 4.623}], 'summary': 'Predict categorical outcome with binary values like 0 or 1, yes or no, true or false.', 'duration': 30.831, 'max_score': 212.232, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ212232.jpg'}, {'end': 345.436, 'src': 'embed', 'start': 322.993, 'weight': 1, 'content': [{'end': 331.097, 'text': 'So this sigmoid function basically converts any value from minus infinity to infinity, to your discrete values which a logistic regression wants.', 'start': 322.993, 'duration': 8.104}, {'end': 335.059, 'text': 'or you can say the values which are in binary format either 0 or 1..', 'start': 331.097, 'duration': 3.962}, {'end': 341.902, 'text': "So if you see here the values as either 0 or 1 and this is nothing but just a transition of it, but guys there's a catch over here.", 'start': 335.059, 'duration': 6.843}, {'end': 345.436, 'text': "So let's say I have a data point that is 0.8.", 'start': 342.182, 'duration': 3.254}], 'summary': 'Sigmoid function transforms values to binary (0 or 1) for logistic regression.', 'duration': 22.443, 'max_score': 322.993, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ322993.jpg'}, {'end': 478.359, 'src': 'embed', 'start': 451.637, 'weight': 2, 'content': [{'end': 457.081, 'text': 'but in our case, or you can say logistic equation, the value which we need to predict, or you can say the Y value.', 'start': 451.637, 'duration': 5.444}, {'end': 459.863, 'text': 'It can have the range only from 0 to 1.', 'start': 457.201, 'duration': 2.662}, {'end': 461.945, 'text': 'So in that case, we need to transform this equation.', 'start': 459.863, 'duration': 2.082}, {'end': 467.254, 'text': 'So to do that what we had done, we have just divide the equation by 1 minus y.', 'start': 462.612, 'duration': 4.642}, {'end': 469.015, 'text': 'so now y is equals to 0.', 'start': 467.254, 'duration': 1.761}, {'end': 471.816, 'text': 'so 0 over 1 minus 0, which is equals to 1.', 'start': 469.015, 'duration': 2.801}, {'end': 474.238, 'text': 'so 0 over 1 is again 0.', 'start': 471.816, 'duration': 2.422}, {'end': 478.359, 'text': 'and if we take y is equals to 1, then 1 over 1 minus 1, which is 0.', 'start': 474.238, 'duration': 4.121}], 'summary': 'Logistic equation predicts y value between 0 to 1.', 'duration': 26.722, 'max_score': 451.637, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ451637.jpg'}, {'end': 639.204, 'src': 'embed', 'start': 613.276, 'weight': 3, 'content': [{'end': 618.139, 'text': 'Moving ahead let us see the various use cases wherein logistic regression is implemented in real life.', 'start': 613.276, 'duration': 4.863}, {'end': 620.921, 'text': 'So the very first is weather prediction.', 'start': 619.32, 'duration': 1.601}, {'end': 624.017, 'text': 'Now logistic regression helps you to predict your weather.', 'start': 621.696, 'duration': 2.321}, {'end': 628.559, 'text': 'For example, it is used to predict whether it is raining or not whether it is sunny.', 'start': 624.477, 'duration': 4.082}, {'end': 633.281, 'text': 'Is it cloudy or not? So all these things can be predicted using logistic regression.', 'start': 628.899, 'duration': 4.382}, {'end': 639.204, 'text': 'Whereas you need to keep in mind that both linear regression and logistic regression can be used in predicting the weather.', 'start': 633.681, 'duration': 5.523}], 'summary': 'Logistic regression is used for weather prediction, including rain, sun, and cloud forecasts.', 'duration': 25.928, 'max_score': 613.276, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ613276.jpg'}], 'start': 61.405, 'title': 'Regression and logistic regression', 'summary': 'Discusses regression analysis and logistic regression, emphasizing predictive modeling techniques, the relationship between variables, and practical applications such as sales prediction, binary outcome prediction, and use cases in survival and purchase interest prediction.', 'chapters': [{'end': 133.951, 'start': 61.405, 'title': 'Regression analysis overview', 'summary': 'Discusses regression analysis as a predictive modeling technique, explaining the relationship between dependent and independent variables, emphasizing the importance of predictive analysis over prescriptive analysis, and the factors affecting the dependent variable, with a focus on sales prediction.', 'duration': 72.546, 'highlights': ['The importance of predictive analysis over prescriptive analysis, as it forms the base for understanding relationship between dependent and independent variables.', 'Explanation of dependent variable as the target variable to be predicted, such as sales, based on factors like number of products sold, season, product availability, and quality.', 'Definition of regression analysis as a predictive modeling technique involving predictions, with a focus on estimating the relationship between dependent and independent variables.']}, {'end': 410.028, 'start': 134.331, 'title': 'Understanding logistic regression', 'summary': 'Explains the concept of logistic regression, its application in predicting binary outcomes, the difference between linear and logistic regression, and the use of the sigmoid function to convert continuous values to discrete binary outcomes.', 'duration': 275.697, 'highlights': ['Logistic regression is used to predict binary outcomes, such as 0 or 1, true or false, high or low. It is most widely used when the dependent variable or output is in the binary format, and the outcome should be discrete or categorical in nature.', 'Difference between linear and logistic regression lies in the type of outcome to be predicted. In logistic regression, the outcome is discrete and binary, while in linear regression, the outcome is in a range.', 'The sigmoid function is used in logistic regression to convert continuous values to discrete binary outcomes. The sigmoid function converts any value from minus infinity to infinity to discrete binary values, either 0 or 1, and is used to define the threshold for rounding off the output.']}, {'end': 934.022, 'start': 411.129, 'title': 'Logistic regression and its applications', 'summary': 'Explains the transformation of linear regression equation into logistic regression to predict discrete values from 0 to 1, the differences and use cases of linear and logistic regression, and its practical implementation in predicting survival on the titanic and purchase interest in suv cars using a dataset.', 'duration': 522.893, 'highlights': ['The equation transformation from linear regression to logistic regression is explained, where the equation is divided by 1 minus y to restrict the range of predicted values from 0 to 1, then further transformed using logarithmic function to achieve a range between minus infinity to infinity. The transformation of the linear regression equation into logistic regression is demonstrated by dividing the equation by 1 minus y, restricting the predicted values range from 0 to 1, and further transforming it using a logarithmic function to achieve a range between minus infinity to infinity.', "The differences between linear and logistic regression are highlighted, focusing on linear regression solving regression problems with continuous dependent variables while logistic regression addresses classification problems with discrete values, and the graphical differences are explained. The differences between linear and logistic regression are delineated, emphasizing linear regression's role in solving regression problems with continuous dependent variables, and logistic regression's function in addressing classification problems with discrete values, with the graphical distinctions elucidated.", 'The practical applications of logistic regression in predicting survival on the Titanic and purchase interest in SUV cars are discussed, showcasing its use in classifying discrete values and its role in multi-class classification for weather prediction, illness determination, and multi-class classification. The practical applications of logistic regression in predicting survival on the Titanic and purchase interest in SUV cars are presented, highlighting its use in classifying discrete values and its function in multi-class classification for weather prediction, illness determination, and multi-class classification.']}], 'duration': 872.617, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ61405.jpg', 'highlights': ['Logistic regression predicts binary outcomes, widely used for discrete or categorical results.', 'Logistic regression uses the sigmoid function to convert continuous values to discrete binary outcomes.', 'The equation transformation from linear to logistic regression restricts predicted values range from 0 to 1 and transforms using a logarithmic function.', "Logistic regression's practical applications include predicting survival on the Titanic and purchase interest in SUV cars."]}, {'end': 1442.813, 'segs': [{'end': 1000.349, 'src': 'embed', 'start': 969.808, 'weight': 0, 'content': [{'end': 973.789, 'text': 'you will check the accuracy so as to ensure how much accurate your values are.', 'start': 969.808, 'duration': 3.981}, {'end': 978.33, 'text': "So I hope you guys got these five steps that you're going to implement in logistic regression.", 'start': 974.109, 'duration': 4.221}, {'end': 981.07, 'text': "So now let's go into all these steps in detail.", 'start': 978.79, 'duration': 2.28}, {'end': 984.951, 'text': 'So number one, we have to collect your data or you can say import the libraries.', 'start': 981.39, 'duration': 3.561}, {'end': 987.351, 'text': 'So let me show you the implementation part as well.', 'start': 985.331, 'duration': 2.02}, {'end': 991.752, 'text': 'So I just open my Jupiter notebook and I just implement all of these steps side by side.', 'start': 987.731, 'duration': 4.021}, {'end': 995.501, 'text': 'So guys, this is my Jupyter notebook.', 'start': 993.939, 'duration': 1.562}, {'end': 1000.349, 'text': "So first let me just rename Jupyter notebook to let's say Titanic data analysis.", 'start': 995.922, 'duration': 4.427}], 'summary': 'The speaker outlines five steps for logistic regression implementation and demonstrates in jupyter notebook.', 'duration': 30.541, 'max_score': 969.808, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ969808.jpg'}, {'end': 1040.147, 'src': 'embed', 'start': 1011.945, 'weight': 2, 'content': [{'end': 1014.087, 'text': 'So pandas is used for data analysis.', 'start': 1011.945, 'duration': 2.142}, {'end': 1018.069, 'text': "So I'll say import pandas as PD then I'll be importing numpy.", 'start': 1014.527, 'duration': 3.542}, {'end': 1020.33, 'text': "So I'll say import numpy as NP.", 'start': 1018.229, 'duration': 2.101}, {'end': 1027.914, 'text': 'So numpy is a library in Python which basically stands for numerical Python and it is widely used to perform any scientific computation.', 'start': 1020.83, 'duration': 7.084}, {'end': 1030.018, 'text': "Next we'll be importing seaborn.", 'start': 1028.476, 'duration': 1.542}, {'end': 1032.72, 'text': 'So seaborn is a library for statistical plotting.', 'start': 1030.438, 'duration': 2.282}, {'end': 1035.323, 'text': "So I'll say import seaborn as SNS.", 'start': 1033.121, 'duration': 2.202}, {'end': 1037.185, 'text': "I'll also import matplotlib.", 'start': 1035.684, 'duration': 1.501}, {'end': 1040.147, 'text': 'So matplotlib library is again for plotting.', 'start': 1037.806, 'duration': 2.341}], 'summary': 'Python libraries pandas, numpy, seaborn, and matplotlib are used for data analysis and plotting.', 'duration': 28.202, 'max_score': 1011.945, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ1011945.jpg'}, {'end': 1172.816, 'src': 'embed', 'start': 1141.657, 'weight': 1, 'content': [{'end': 1146.801, 'text': 'So here the number of passengers which are there in the original data set we have is 891.', 'start': 1141.657, 'duration': 5.144}, {'end': 1149.503, 'text': 'So around this number were traveling in the Titanic ship.', 'start': 1146.801, 'duration': 2.702}, {'end': 1150.798, 'text': 'So over here.', 'start': 1150.197, 'duration': 0.601}, {'end': 1153.462, 'text': 'my first step is done, where you have just collected data,', 'start': 1150.798, 'duration': 2.664}, {'end': 1157.968, 'text': 'imported all the libraries and find out the total number of passengers which are traveling in Titanic.', 'start': 1153.462, 'duration': 4.506}, {'end': 1161.373, 'text': "So now let me just go back to presentation and let's see what is my next step.", 'start': 1158.589, 'duration': 2.784}, {'end': 1165.614, 'text': "So we're done with the collecting data next step is to analyze your data.", 'start': 1162.133, 'duration': 3.481}, {'end': 1172.816, 'text': 'So over here will be creating different plots to check the relationship between variables, as in how one variable is affecting the other.', 'start': 1165.974, 'duration': 6.842}], 'summary': 'The original dataset contains 891 passengers. next step: analyzing data and creating plots to check variable relationships.', 'duration': 31.159, 'max_score': 1141.657, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ1141657.jpg'}, {'end': 1323.013, 'src': 'embed', 'start': 1295.487, 'weight': 3, 'content': [{'end': 1299.89, 'text': 'and if we see the people who survived here, we can see the majority of females survive.', 'start': 1295.487, 'duration': 4.403}, {'end': 1302.732, 'text': 'So this basically concludes the gender of the survival rate.', 'start': 1300.231, 'duration': 2.501}, {'end': 1308.596, 'text': 'So it appears on average women were more than three times more likely to survive than men next.', 'start': 1303.053, 'duration': 5.543}, {'end': 1312.299, 'text': 'Let us plot another plot where we have the hue as the passenger class.', 'start': 1308.616, 'duration': 3.683}, {'end': 1318.683, 'text': 'So over here we can see which class at the passenger was traveling in whether it was traveling in class 1 2 or 3.', 'start': 1312.399, 'duration': 6.284}, {'end': 1320.705, 'text': 'So for that I just write the same command.', 'start': 1318.683, 'duration': 2.022}, {'end': 1323.013, 'text': "I'll say sns.com plot.", 'start': 1320.825, 'duration': 2.188}], 'summary': 'Females had over three times higher survival rate compared to males. passenger class distribution also analyzed.', 'duration': 27.526, 'max_score': 1295.487, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ1295487.jpg'}, {'end': 1364.158, 'src': 'embed', 'start': 1337.65, 'weight': 5, 'content': [{'end': 1343.136, 'text': 'So over here you can see I have blue for first class orange for second class and green for the third class.', 'start': 1337.65, 'duration': 5.486}, {'end': 1347.933, 'text': 'So here the passengers who did not survive were majorly of the third class, or you can say,', 'start': 1343.812, 'duration': 4.121}, {'end': 1354.315, 'text': 'the lowest class or the cheapest class to get into the Titanic, and the people who did survive majorly belong to the higher classes.', 'start': 1347.933, 'duration': 6.382}, {'end': 1358.657, 'text': 'So here one and two has more rise than the passenger who were traveling in the third class.', 'start': 1354.715, 'duration': 3.942}, {'end': 1364.158, 'text': 'So here we have concluded that the passengers who did not survive a majorly of third class or, you can say,', 'start': 1359.177, 'duration': 4.981}], 'summary': 'Most non-survivors were from third class on the titanic.', 'duration': 26.508, 'max_score': 1337.65, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ1337650.jpg'}, {'end': 1431.445, 'src': 'embed', 'start': 1399.524, 'weight': 7, 'content': [{'end': 1401.665, 'text': 'So this is the analysis on the age column.', 'start': 1399.524, 'duration': 2.141}, {'end': 1407.409, 'text': 'So we saw that we have more young passengers and more mediocre age passengers which are traveling in the Titanic.', 'start': 1401.705, 'duration': 5.704}, {'end': 1410.412, 'text': 'So next let me plot a graph of air as well.', 'start': 1408.17, 'duration': 2.242}, {'end': 1412.153, 'text': "So I'll say Titanic data.", 'start': 1410.692, 'duration': 1.461}, {'end': 1416.556, 'text': "I'll say fair and again I'll plot a histogram.", 'start': 1413.574, 'duration': 2.982}, {'end': 1417.217, 'text': "So I'll say hist.", 'start': 1416.596, 'duration': 0.621}, {'end': 1422.939, 'text': 'So here you can see the fair size is between 0 to 100.', 'start': 1419.457, 'duration': 3.482}, {'end': 1426.862, 'text': 'Now, let me add the bin size so as to make it more clear over here.', 'start': 1422.939, 'duration': 3.923}, {'end': 1431.445, 'text': "I'll say bin is equals to let's say 20 and I'll increase the figure size as well.", 'start': 1426.942, 'duration': 4.503}], 'summary': 'Analysis shows more young and mediocre age passengers on titanic, with fair size mostly between 0 to 100.', 'duration': 31.921, 'max_score': 1399.524, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ1399524.jpg'}], 'start': 934.523, 'title': 'Logistic regression and data analysis', 'summary': 'Covers logistic regression with 5 steps, including data analysis and wrangling, model building and testing, and importing libraries. it also focuses on data analysis and visualization using python libraries like seaborn and pandas, with examples from the titanic dataset.', 'chapters': [{'end': 1157.968, 'start': 934.523, 'title': 'Logistic regression data analysis', 'summary': 'Covers the 5 steps in logistic regression, including analyzing and wrangling data, building and testing the model, and importing libraries, with a focus on titanic data analysis using python libraries like pandas, numpy, seaborn, matplotlib, and math.', 'duration': 223.445, 'highlights': ['The chapter covers the 5 steps in logistic regression, including analyzing and wrangling data, building and testing the model, and importing libraries, with a focus on Titanic data analysis using Python libraries like pandas, numpy, seaborn, matplotlib, and math.', 'The process involves importing libraries such as pandas for data analysis, numpy for scientific computation, seaborn for statistical plotting, matplotlib for plotting, and math for basic mathematical functions.', 'The original data set for Titanic analysis contains 891 passengers.']}, {'end': 1442.813, 'start': 1158.589, 'title': 'Data analysis and visualization', 'summary': 'Covers the process of analyzing and visualizing data using various plots and libraries such as seaborn and pandas, with examples including comparing survival rates based on gender and passenger class, and analyzing age and fare distribution in the titanic dataset.', 'duration': 284.224, 'highlights': ['Majority of males did not survive, while majority of females did survive, indicating that women were more than three times more likely to survive than men. The count plot analysis of survival rates based on gender shows that around 550 of the passengers did not survive, while around 350 passengers survived, indicating that women were more than three times more likely to survive than men.', 'Passengers of the third class had a lower survival rate compared to those traveling in higher classes, with first and second class passengers tending to survive more. The analysis based on the passenger class shows that the passengers who did not survive were majorly of the third class, while the people who did survive majorly belong to the higher classes, indicating that first and second class passengers tended to survive more.', 'The age distribution analysis shows that there were more young and average age passengers traveling in the Titanic. The age distribution analysis indicates that there were more young passengers and more mediocre age passengers traveling in the Titanic.']}], 'duration': 508.29, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ934523.jpg', 'highlights': ['The chapter covers the 5 steps in logistic regression, including analyzing and wrangling data, building and testing the model, and importing libraries, with a focus on Titanic data analysis using Python libraries like pandas, numpy, seaborn, matplotlib, and math.', 'The original data set for Titanic analysis contains 891 passengers.', 'The process involves importing libraries such as pandas for data analysis, numpy for scientific computation, seaborn for statistical plotting, matplotlib for plotting, and math for basic mathematical functions.', 'Majority of males did not survive, while majority of females did survive, indicating that women were more than three times more likely to survive than men.', 'The count plot analysis of survival rates based on gender shows that around 550 of the passengers did not survive, while around 350 passengers survived, indicating that women were more than three times more likely to survive than men.', 'Passengers of the third class had a lower survival rate compared to those traveling in higher classes, with first and second class passengers tending to survive more.', 'The analysis based on the passenger class shows that the passengers who did not survive were majorly of the third class, while the people who did survive majorly belong to the higher classes, indicating that first and second class passengers tended to survive more.', 'The age distribution analysis shows that there were more young and average age passengers traveling in the Titanic.', 'The age distribution analysis indicates that there were more young passengers and more mediocre age passengers traveling in the Titanic.']}, {'end': 1904.225, 'segs': [{'end': 1491.631, 'src': 'embed', 'start': 1463.397, 'weight': 0, 'content': [{'end': 1468.561, 'text': 'then we saw the passenger class, where the passenger is traveling in the first class, second class or third class.', 'start': 1463.397, 'duration': 5.164}, {'end': 1469.342, 'text': 'then we have the name.', 'start': 1468.561, 'duration': 0.781}, {'end': 1471.443, 'text': 'So in name we cannot do any analysis.', 'start': 1469.462, 'duration': 1.981}, {'end': 1473.885, 'text': 'We saw the sex we saw the age as well.', 'start': 1471.844, 'duration': 2.041}, {'end': 1480.186, 'text': 'Then we have SibSP So this stands for the number of siblings or the spouses which are aboard the Titanic.', 'start': 1474.426, 'duration': 5.76}, {'end': 1481.687, 'text': 'So let us do this as well.', 'start': 1480.587, 'duration': 1.1}, {'end': 1483.748, 'text': "So I'll say sns.com plot.", 'start': 1482.007, 'duration': 1.741}, {'end': 1491.631, 'text': "I'll mention X as SIP SP and I'll be using the Titanic data.", 'start': 1486.609, 'duration': 5.022}], 'summary': 'Analyzing passenger data including class, sex, age, and sibsp aboard titanic.', 'duration': 28.234, 'max_score': 1463.397, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ1463397.jpg'}, {'end': 1675.121, 'src': 'heatmap', 'start': 1642.45, 'weight': 0.797, 'content': [{'end': 1643.871, 'text': 'That is 177.', 'start': 1642.45, 'duration': 1.421}, {'end': 1648.574, 'text': 'Then we have the maximum value in the cabin column and we have very less in the impact column.', 'start': 1643.871, 'duration': 4.703}, {'end': 1649.873, 'text': 'That is 2.', 'start': 1648.614, 'duration': 1.259}, {'end': 1655.296, 'text': "So here if you don't want to see this numbers, you can also plot a heat map and then you can visually analyze it.", 'start': 1649.873, 'duration': 5.423}, {'end': 1656.737, 'text': 'So let me just do that as well.', 'start': 1655.416, 'duration': 1.321}, {'end': 1658.879, 'text': "So I'll say SNS dot heat map.", 'start': 1656.777, 'duration': 2.102}, {'end': 1664.382, 'text': "I'll say y tick labels.", 'start': 1663.101, 'duration': 1.281}, {'end': 1673.739, 'text': "Falls so I'll just run this so as we have already seen that there were three columns in which missing data value was present.", 'start': 1667.572, 'duration': 6.167}, {'end': 1675.121, 'text': 'So this might be age.', 'start': 1674.14, 'duration': 0.981}], 'summary': 'Data analysis reveals maximum value in cabin column and very few in impact column, with 3 columns containing missing data, possibly including age.', 'duration': 32.671, 'max_score': 1642.45, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ1642450.jpg'}, {'end': 1759.95, 'src': 'embed', 'start': 1733.608, 'weight': 1, 'content': [{'end': 1738.21, 'text': "I'll say y is equals to age and the data set that I'll be using is Titanic set.", 'start': 1733.608, 'duration': 4.602}, {'end': 1740.551, 'text': "So I'll say data is equals to Titanic data.", 'start': 1738.55, 'duration': 2.001}, {'end': 1747.061, 'text': 'You can see the age in first class and second class tends to be more older rather than we have it in the third class.', 'start': 1741.377, 'duration': 5.684}, {'end': 1751.664, 'text': 'Well that depends on the experience how much you own or might be the n number of reasons.', 'start': 1747.501, 'duration': 4.163}, {'end': 1759.95, 'text': 'So here we concluded that passengers who were traveling in class 1 and class 2 attend to be older than what we have in the class 3.', 'start': 1752.385, 'duration': 7.565}], 'summary': 'Titanic dataset shows older passengers in class 1 and 2 compared to class 3.', 'duration': 26.342, 'max_score': 1733.608, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ1733608.jpg'}, {'end': 1818.68, 'src': 'embed', 'start': 1792.666, 'weight': 2, 'content': [{'end': 1797.188, 'text': 'We have the name then we have ticket number fare keeping so over here.', 'start': 1792.666, 'duration': 4.522}, {'end': 1798.448, 'text': 'We have seen that in cabin.', 'start': 1797.208, 'duration': 1.24}, {'end': 1802.629, 'text': 'We have a lot of null values or you can say the nan values is quite visible as well.', 'start': 1798.468, 'duration': 4.161}, {'end': 1804.93, 'text': "So first of all, we'll just drop this column.", 'start': 1803.109, 'duration': 1.821}, {'end': 1805.99, 'text': 'So dropping it.', 'start': 1805.19, 'duration': 0.8}, {'end': 1807.871, 'text': "I'll just say Titanic underscore data.", 'start': 1806.07, 'duration': 1.801}, {'end': 1813.816, 'text': "and I'll simply type and drop and the column which I need to drop so I have to drop the cabin column.", 'start': 1808.431, 'duration': 5.385}, {'end': 1818.68, 'text': "I'll mention the axis equals to 1 and I'll say in place also to true.", 'start': 1814.637, 'duration': 4.043}], 'summary': "Dropping the 'cabin' column with null values from the titanic data.", 'duration': 26.014, 'max_score': 1792.666, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ1792666.jpg'}], 'start': 1443.493, 'title': 'Titanic data analysis and data wrangling', 'summary': "Covers the analysis of titanic data including the number of passengers who survived, gender-based analysis, passenger class distribution, and the analysis of the number of siblings or spouses aboard the titanic. additionally, it includes data wrangling on the titanic dataset, focusing on identifying and removing null values, analyzing missing data using heat maps, and performing imputation to clean the dataset, with the primary focus on the 'cabin' column and the 'age' column.", 'chapters': [{'end': 1526.404, 'start': 1443.493, 'title': 'Titanic data analysis', 'summary': 'Covers the analysis of titanic data including the number of passengers who survived, gender-based analysis, passenger class distribution, and the analysis of the number of siblings or spouses aboard the titanic.', 'duration': 82.911, 'highlights': ['The chapter discusses the number of passengers who survived and those who did not, providing insights into the survival rate.', 'It also covers the analysis of gender-based survival rates, comparing the survival rates of men and women.', 'The analysis includes the distribution of passengers across first, second, and third class, providing an understanding of the class distribution on the Titanic.', 'The chapter involves the analysis of the number of siblings or spouses aboard the Titanic, with a focus on the distribution of these relationships among passengers.']}, {'end': 1904.225, 'start': 1526.404, 'title': 'Data wrangling in titanic dataset', 'summary': "Covers data wrangling on the titanic dataset, including identifying and removing null values, analyzing missing data using heat maps, and performing imputation to clean the dataset, with the primary focus on the 'cabin' column and the 'age' column.", 'duration': 377.821, 'highlights': ['Passengers traveling in first and second class tend to be older than those in third class. Passenger class 1 and class 2 tend to be older than class 3, as observed in the box plot analysis of age data.', "177 passengers have missing values in the dataset, with the 'cabin' column having the highest count of null values. The 'cabin' column has the highest count of null values, with 177 passengers having missing values in the dataset, as identified using the sum function.", "The 'cabin' column is dropped from the dataset to remove null values, resulting in a cleaner dataset. The 'cabin' column is dropped from the dataset using the drop method, resulting in the removal of the column and a cleaner dataset."]}], 'duration': 460.732, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ1443493.jpg', 'highlights': ['The chapter involves the analysis of the number of siblings or spouses aboard the Titanic, with a focus on the distribution of these relationships among passengers.', 'Passengers traveling in first and second class tend to be older than those in third class. Passenger class 1 and class 2 tend to be older than class 3, as observed in the box plot analysis of age data.', "The 'cabin' column is dropped from the dataset to remove null values, resulting in a cleaner dataset.", 'The analysis includes the distribution of passengers across first, second, and third class, providing an understanding of the class distribution on the Titanic.']}, {'end': 2410.579, 'segs': [{'end': 1951.914, 'src': 'embed', 'start': 1924.717, 'weight': 0, 'content': [{'end': 1930.505, 'text': 'we will convert this to categorical variable, into some dummy variables, and this can be done using Pandas,', 'start': 1924.717, 'duration': 5.788}, {'end': 1932.809, 'text': 'because logistic regression just take two values.', 'start': 1930.505, 'duration': 2.304}, {'end': 1937.851, 'text': 'So whenever you apply machine learning, you need to make sure that there are no string values present,', 'start': 1933.51, 'duration': 4.341}, {'end': 1940.812, 'text': "because it won't be taking these as your input variables.", 'start': 1937.851, 'duration': 2.961}, {'end': 1945.653, 'text': "So using string you don't have to predict anything but in my case, I have the survive columns.", 'start': 1941.292, 'duration': 4.361}, {'end': 1951.914, 'text': 'So I need to predict how many people tend to survive and how many did not so zero stands for did not survive and one stands for survive.', 'start': 1945.773, 'duration': 6.141}], 'summary': 'Data converted to dummy variables for logistic regression in predicting survival outcomes.', 'duration': 27.197, 'max_score': 1924.717, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ1924717.jpg'}, {'end': 2211.594, 'src': 'embed', 'start': 2186.721, 'weight': 4, 'content': [{'end': 2192.226, 'text': "So I'll just type in Titanic data dot drop and mention the columns that I want to drop.", 'start': 2186.721, 'duration': 5.505}, {'end': 2203.469, 'text': "So I'll say I even delete the passenger ID because it's nothing but just the index value which is starting from one.", 'start': 2192.706, 'duration': 10.763}, {'end': 2205.17, 'text': "So I'll drop this as well.", 'start': 2204.049, 'duration': 1.121}, {'end': 2207.011, 'text': "Then I don't want name as well.", 'start': 2205.85, 'duration': 1.161}, {'end': 2208.212, 'text': "So I'll delete name as well.", 'start': 2207.051, 'duration': 1.161}, {'end': 2211.594, 'text': 'Then what else we can drop we can drop the ticket as well.', 'start': 2209.112, 'duration': 2.482}], 'summary': 'Dropped passenger id, name, and ticket columns from titanic dataset.', 'duration': 24.873, 'max_score': 2186.721, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ2186721.jpg'}, {'end': 2226.003, 'src': 'heatmap', 'start': 2186.721, 'weight': 1, 'content': [{'end': 2192.226, 'text': "So I'll just type in Titanic data dot drop and mention the columns that I want to drop.", 'start': 2186.721, 'duration': 5.505}, {'end': 2203.469, 'text': "So I'll say I even delete the passenger ID because it's nothing but just the index value which is starting from one.", 'start': 2192.706, 'duration': 10.763}, {'end': 2205.17, 'text': "So I'll drop this as well.", 'start': 2204.049, 'duration': 1.121}, {'end': 2207.011, 'text': "Then I don't want name as well.", 'start': 2205.85, 'duration': 1.161}, {'end': 2208.212, 'text': "So I'll delete name as well.", 'start': 2207.051, 'duration': 1.161}, {'end': 2211.594, 'text': 'Then what else we can drop we can drop the ticket as well.', 'start': 2209.112, 'duration': 2.482}, {'end': 2218.098, 'text': "And then I just mentioned the axis and I'll say in place is equals to true.", 'start': 2213.935, 'duration': 4.163}, {'end': 2222.301, 'text': 'Okay, so my column name starts from uppercase.', 'start': 2219.699, 'duration': 2.602}, {'end': 2226.003, 'text': 'So these has been dropped now, let me just print my data set again.', 'start': 2223.301, 'duration': 2.702}], 'summary': 'Dropped titanic dataset columns: passenger id, name, ticket.', 'duration': 39.282, 'max_score': 2186.721, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ2186721.jpg'}, {'end': 2357.221, 'src': 'embed', 'start': 2327.639, 'weight': 3, 'content': [{'end': 2330.36, 'text': 'So everything else are the features which leads to the survival rate.', 'start': 2327.639, 'duration': 2.721}, {'end': 2337.529, 'text': 'So once we have defined the independent variable and the dependent variable next step is to split your data into training and testing subset.', 'start': 2330.945, 'duration': 6.584}, {'end': 2343.052, 'text': "So for that we'll be using sklearn I just type in from sklearn dot cross validation.", 'start': 2338.069, 'duration': 4.983}, {'end': 2346.154, 'text': 'Import train test plate.', 'start': 2344.933, 'duration': 1.221}, {'end': 2357.221, 'text': 'Now here if you just click on shift and tab you can go to the documentation and you can just see the examples over here.', 'start': 2350.457, 'duration': 6.764}], 'summary': 'Features impact survival rate. data split for training/testing using sklearn.', 'duration': 29.582, 'max_score': 2327.639, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ2327639.jpg'}], 'start': 1904.225, 'title': 'Data preprocessing and logistic regression', 'summary': 'Covers converting string values to categorical variables using pandas, including converting sex into dummy variables, dropping unnecessary columns, and applying the same process to embarked and passenger class for logistic regression. it also includes data wrangling, model training, and splitting the data into a 70-30 ratio for prediction.', 'chapters': [{'end': 2145.675, 'start': 1904.225, 'title': 'Data preprocessing for logistic regression', 'summary': 'Covers the process of converting string values to categorical variables using pandas, including converting sex into dummy variables, dropping unnecessary columns, and applying the same process to embarked and passenger class, ultimately concatenating all the new rows into a data set for logistic regression.', 'duration': 241.45, 'highlights': ['The process of converting string values to categorical variables using Pandas is essential for logistic regression, as it ensures that machine learning models do not consider string values as input variables, ultimately requiring the conversion of string values to dummy variables. This process is essential for logistic regression as it ensures that machine learning models do not consider string values as input variables, ultimately requiring the conversion of string values to dummy variables.', 'The conversion of sex into dummy variables involves using Pandas to create dummy variables for sex, dropping unnecessary columns, and setting the remaining column as a variable, demonstrating the process of efficiently handling categorical variables for logistic regression. The conversion of sex into dummy variables involves using Pandas to create dummy variables for sex, dropping unnecessary columns, and setting the remaining column as a variable, demonstrating the process of efficiently handling categorical variables for logistic regression.', 'The same process of converting string values to categorical variables using Pandas is applied to embarked and passenger class, involving the creation of dummy variables, dropping unnecessary columns, and preparing the data for concatenation into a data set for logistic regression analysis. The same process of converting string values to categorical variables using Pandas is applied to embarked and passenger class, involving the creation of dummy variables, dropping unnecessary columns, and preparing the data for concatenation into a data set for logistic regression analysis.']}, {'end': 2410.579, 'start': 2146.915, 'title': 'Data wrangling and model training', 'summary': 'Covers data wrangling by dropping irrelevant columns and cleaning the data. it then progresses to training and testing the data set using sklearn to split the data into a 70-30 ratio for model building and prediction.', 'duration': 263.664, 'highlights': ['The chapter covers data wrangling by dropping irrelevant columns and cleaning the data. The speaker demonstrates dropping irrelevant columns like P class, embarked, sex, passenger ID, and name, followed by mentioning the action of dropping the ticket column and printing the final data set.', "Training and testing the data set using sklearn to split the data into a 70-30 ratio for model building and prediction. The speaker explains the process of defining dependent and independent variables, and then splitting the data into training and testing subsets using sklearn's train_test_split function, with a split size of 0.3 and a random state of 1 for reproducibility."]}], 'duration': 506.354, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ1904225.jpg', 'highlights': ['The process of converting string values to categorical variables using Pandas is essential for logistic regression, as it ensures that machine learning models do not consider string values as input variables, ultimately requiring the conversion of string values to dummy variables.', 'The same process of converting string values to categorical variables using Pandas is applied to embarked and passenger class, involving the creation of dummy variables, dropping unnecessary columns, and preparing the data for concatenation into a data set for logistic regression analysis.', 'The conversion of sex into dummy variables involves using Pandas to create dummy variables for sex, dropping unnecessary columns, and setting the remaining column as a variable, demonstrating the process of efficiently handling categorical variables for logistic regression.', "Training and testing the data set using sklearn to split the data into a 70-30 ratio for model building and prediction. The speaker explains the process of defining dependent and independent variables, and then splitting the data into training and testing subsets using sklearn's train_test_split function, with a split size of 0.3 and a random state of 1 for reproducibility.", 'The chapter covers data wrangling by dropping irrelevant columns and cleaning the data. The speaker demonstrates dropping irrelevant columns like P class, embarked, sex, passenger ID, and name, followed by mentioning the action of dropping the ticket column and printing the final data set.']}, {'end': 3220.323, 'segs': [{'end': 2590.1, 'src': 'embed', 'start': 2552.87, 'weight': 3, 'content': [{'end': 2554.231, 'text': 'Okay, Swati is not clear with this.', 'start': 2552.87, 'duration': 1.361}, {'end': 2557.253, 'text': "So I'll just tell you in a brief what confusion matrix is all about.", 'start': 2554.731, 'duration': 2.522}, {'end': 2562.876, 'text': 'So confusion matrix is nothing but a two by two matrix which has a four outcomes.', 'start': 2558.553, 'duration': 4.323}, {'end': 2565.878, 'text': 'This basically tells us that how accurate your values are.', 'start': 2563.356, 'duration': 2.522}, {'end': 2573.222, 'text': 'So here we have the column as predicted no predicted Y and we have actual no and an actual yes.', 'start': 2566.378, 'duration': 6.844}, {'end': 2577.265, 'text': 'So this is the concept of confusion matrix.', 'start': 2574.963, 'duration': 2.302}, {'end': 2580.747, 'text': 'So here let me just feed in these values which we have just calculated.', 'start': 2577.745, 'duration': 3.002}, {'end': 2590.1, 'text': 'So here we have 105 105, 21, 25 and 63.', 'start': 2581.267, 'duration': 8.833}], 'summary': 'Confusion matrix is a 2x2 matrix with 4 outcomes, showing accuracy with values: 105, 105, 21, 25, and 63.', 'duration': 37.23, 'max_score': 2552.87, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ2552870.jpg'}, {'end': 2713.935, 'src': 'embed', 'start': 2688.547, 'weight': 1, 'content': [{'end': 2695.309, 'text': 'Then we have build our model on the train data and then predicted the output on the test data set and then my fifth step is to check the accuracy.', 'start': 2688.547, 'duration': 6.762}, {'end': 2700.091, 'text': 'So here we have calculated accuracy to almost 78% which is quite good.', 'start': 2695.769, 'duration': 4.322}, {'end': 2702.371, 'text': 'You cannot say that accuracy is bad.', 'start': 2700.711, 'duration': 1.66}, {'end': 2705.312, 'text': 'So here it tells me how accurate your results are.', 'start': 2703.092, 'duration': 2.22}, {'end': 2709.574, 'text': 'So here my accuracy score defines that and hence we got a good accuracy.', 'start': 2705.692, 'duration': 3.882}, {'end': 2713.935, 'text': 'So now moving ahead later see the second project that is SUV data analysis.', 'start': 2710.474, 'duration': 3.461}], 'summary': 'Model accuracy reached 78%, indicating good results in data analysis.', 'duration': 25.388, 'max_score': 2688.547, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ2688547.jpg'}, {'end': 3122.346, 'src': 'embed', 'start': 3095.39, 'weight': 0, 'content': [{'end': 3100.591, 'text': 'All right, so over here I get the accuracy is 89% so we want to know the accuracy in percentage.', 'start': 3095.39, 'duration': 5.201}, {'end': 3108.253, 'text': 'So I just have to multiply it by 100 and if I run this so it gives me 89% so I hope you guys are clear with whatever I have taught you today.', 'start': 3100.611, 'duration': 7.642}, {'end': 3116.382, 'text': 'So here I have taken my independent variables as age and salary, and then we have calculated that how many people can purchase the SUV?', 'start': 3108.777, 'duration': 7.605}, {'end': 3119.244, 'text': 'and then we have calculated our model by checking the accuracy.', 'start': 3116.382, 'duration': 2.862}, {'end': 3122.346, 'text': 'So over here we get the accuracy is 89, which is great.', 'start': 3119.684, 'duration': 2.662}], 'summary': 'The accuracy of the model is 89% based on age and salary as independent variables.', 'duration': 26.956, 'max_score': 3095.39, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ3095390.jpg'}, {'end': 3157.069, 'src': 'embed', 'start': 3132.273, 'weight': 2, 'content': [{'end': 3137.897, 'text': 'then we have understood the types of regression and then got into the details of what and why of logistic regression.', 'start': 3132.273, 'duration': 5.624}, {'end': 3140.637, 'text': "I've compared linear versus logistic regression.", 'start': 3138.556, 'duration': 2.081}, {'end': 3146.121, 'text': 'We have also seen the various use cases where you can implement logistic regression in real life.', 'start': 3141.018, 'duration': 5.103}, {'end': 3151.345, 'text': 'and then we have picked up two projects, that is, Titanic data analysis and SUV prediction, over here.', 'start': 3146.121, 'duration': 5.224}, {'end': 3157.069, 'text': 'We have seen how you can collect your data, analyze your data, then, before modeling on that data, train the data,', 'start': 3151.385, 'duration': 5.684}], 'summary': 'Explored types of regression, compared linear vs. logistic regression, and discussed real-life use cases. also covered titanic data analysis and suv prediction projects.', 'duration': 24.796, 'max_score': 3132.273, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ3132273.jpg'}], 'start': 2411.658, 'title': 'Logistic regression for predictions', 'summary': 'Covers logistic regression, evaluating model performance with an accuracy score of 78%, and demonstrating prediction for suv with a model achieving an 89% accuracy. it also provides an overview of regression, types of regression, comparison between linear and logistic regression, and practical use cases.', 'chapters': [{'end': 2898.751, 'start': 2411.658, 'title': 'Logistic regression model and evaluation', 'summary': "Covers the process of training and predicting using logistic regression, evaluating the model's performance with an accuracy score of 78%, and a detailed explanation of confusion matrix and its application, followed by a demonstration of logistic regression for suv predictions.", 'duration': 487.093, 'highlights': ['The accuracy score of the logistic regression model is 78%, indicating a good level of accuracy in predicting outcomes.', 'A detailed explanation of the confusion matrix, including the four outcomes (true positive, true negative, false positive, and false negative) and its application in calculating accuracy.', 'Demonstration of training a logistic regression model for predicting SUV purchases using age and estimated salary as independent variables.', 'Explanation of the process of importing libraries, defining independent and dependent variables, and implementing logistic regression for the SUV prediction project.']}, {'end': 3220.323, 'start': 2899.091, 'title': 'Logistic regression for suv prediction', 'summary': 'Explains the process of performing logistic regression for suv prediction, including data preprocessing, model fitting, and accuracy calculation, achieving an 89% accuracy, and also covers an overview of regression, types of regression, comparison between linear and logistic regression, and practical use cases.', 'duration': 321.232, 'highlights': ['The accuracy achieved for the SUV prediction model is 89%. The accuracy of the logistic regression model for SUV prediction is highlighted as 89%, indicating a high level of predictive success.', 'The chapter provides an overview of regression, types of regression, and the comparison between linear and logistic regression. The chapter covers an overview of regression, types of regression, and a comparison between linear and logistic regression, providing a comprehensive understanding of the topic.', 'The process of logistic regression for SUV prediction includes data preprocessing, model fitting, and accuracy calculation. The detailed process of logistic regression for SUV prediction is explained, encompassing data preprocessing, model fitting, and accuracy calculation, demonstrating a structured approach to predictive modeling.']}], 'duration': 808.665, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VCJdg7YBbAQ/pics/VCJdg7YBbAQ2411658.jpg', 'highlights': ['The accuracy achieved for the SUV prediction model is 89%.', 'The accuracy score of the logistic regression model is 78%.', 'The chapter provides an overview of regression, types of regression, and the comparison between linear and logistic regression.', 'A detailed explanation of the confusion matrix, including the four outcomes (true positive, true negative, false positive, and false negative) and its application in calculating accuracy.']}], 'highlights': ['The process involves importing libraries such as pandas for data analysis, numpy for scientific computation, seaborn for statistical plotting, matplotlib for plotting, and math for basic mathematical functions.', 'The chapter covers the 5 steps in logistic regression, including analyzing and wrangling data, building and testing the model, and importing libraries, with a focus on Titanic data analysis using Python libraries like pandas, numpy, seaborn, matplotlib, and math.', 'The accuracy achieved for the SUV prediction model is 89%.', 'The accuracy score of the logistic regression model is 78%.', 'The training includes an introduction to regression and different types of regression, providing a comprehensive understanding of the topic.', 'The chapter covers the significance of logistic regression in predicting relationships, including the what and why of logistic regression, real-life use cases, and practical implementation of the algorithm.', 'The session provides a comparison between linear regression and logistic regression, highlighting the differences and real-life use cases.', 'Logistic regression predicts binary outcomes, widely used for discrete or categorical results.', 'Logistic regression uses the sigmoid function to convert continuous values to discrete binary outcomes.', 'The equation transformation from linear to logistic regression restricts predicted values range from 0 to 1 and transforms using a logarithmic function.', "Logistic regression's practical applications include predicting survival on the Titanic and purchase interest in SUV cars.", 'The original data set for Titanic analysis contains 891 passengers.', 'Majority of males did not survive, while majority of females did survive, indicating that women were more than three times more likely to survive than men.', 'The count plot analysis of survival rates based on gender shows that around 550 of the passengers did not survive, while around 350 passengers survived, indicating that women were more than three times more likely to survive than men.', 'Passengers of the third class had a lower survival rate compared to those traveling in higher classes, with first and second class passengers tending to survive more.', 'The analysis based on the passenger class shows that the passengers who did not survive were majorly of the third class, while the people who did survive majorly belong to the higher classes, indicating that first and second class passengers tended to survive more.', 'The age distribution analysis shows that there were more young and average age passengers traveling in the Titanic.', 'The age distribution analysis indicates that there were more young passengers and more mediocre age passengers traveling in the Titanic.', 'The chapter involves the analysis of the number of siblings or spouses aboard the Titanic, with a focus on the distribution of these relationships among passengers.', 'Passengers traveling in first and second class tend to be older than those in third class. Passenger class 1 and class 2 tend to be older than class 3, as observed in the box plot analysis of age data.', "The 'cabin' column is dropped from the dataset to remove null values, resulting in a cleaner dataset.", 'The analysis includes the distribution of passengers across first, second, and third class, providing an understanding of the class distribution on the Titanic.', 'The process of converting string values to categorical variables using Pandas is essential for logistic regression, as it ensures that machine learning models do not consider string values as input variables, ultimately requiring the conversion of string values to dummy variables.', 'The same process of converting string values to categorical variables using Pandas is applied to embarked and passenger class, involving the creation of dummy variables, dropping unnecessary columns, and preparing the data for concatenation into a data set for logistic regression analysis.', 'The conversion of sex into dummy variables involves using Pandas to create dummy variables for sex, dropping unnecessary columns, and setting the remaining column as a variable, demonstrating the process of efficiently handling categorical variables for logistic regression.', "Training and testing the data set using sklearn to split the data into a 70-30 ratio for model building and prediction. The speaker explains the process of defining dependent and independent variables, and then splitting the data into training and testing subsets using sklearn's train_test_split function, with a split size of 0.3 and a random state of 1 for reproducibility.", 'The chapter covers data wrangling by dropping irrelevant columns and cleaning the data. The speaker demonstrates dropping irrelevant columns like P class, embarked, sex, passenger ID, and name, followed by mentioning the action of dropping the ticket column and printing the final data set.', 'A detailed explanation of the confusion matrix, including the four outcomes (true positive, true negative, false positive, and false negative) and its application in calculating accuracy.']}