title
Beginner Kaggle Data Science Project Walk-Through (Titanic)

description
In this video I walk through an entire Kaggle data science project. I use the titanic kaggle competition to show you how I start thinking about the problems. I also show you the systematic approach that I use to explore the data, build the models, and submit the solution. Kaggle notebook: https://www.kaggle.com/kenjee/titanic-project-example My Kaggle Profile: https://www.kaggle.com/kenjee Feel free to follow along with the code! You don't need to understand everything that is going on under the hood of the algorithms, for a beginner, learning to implement them should be enough. This video covers - Project Planning - Data exploration - Data Visualization (light) - Replacing null values - Feature engineering - Data Cleaning - Model Production - Model Tuning - Kaggle model submission #DataScience #KenJee ⭕ Subscribe: https://www.youtube.com/c/kenjee1?sub_confirmation=1 🎙 Listen to My Podcast: https://www.youtube.com/c/KensNearestNeighborsPodcast 🕸 Check out My Website - https://kennethjee.com/ ✍️Sign up for My Newsletter - https://www.kennethjee.com/newsletter 📚 Books and Products I use - https://www.amazon.com/shop/kenjee (affiliate link) Partners & Affiliates 🌟 365 Data Science - Courses ( 57% Annual Discount): https://365datascience.pxf.io/P0jbBY 🌟 Interview Query - https://www.interviewquery.com/?ref=kenjee MORE DATA SCIENCE CONTENT HERE: 🐤My Twitter - https://twitter.com/KenJee_DS 👔 LinkedIn - https://www.linkedin.com/in/kenjee/ 📈 Kaggle - https://www.kaggle.com/kenjee 📑 Medium Articles - https://medium.com/@kenneth.b.jee 💻 Github - https://github.com/PlayingNumbers 🏀 My Sports Blog -https://www.playingnumbers.com Check These Videos Out Next! My Leaderboard Project: https://www.youtube.com/watch?v=myhoWUrSP7o&ab_channel=KenJee 66 Days of Data: https://www.youtube.com/watch?v=qV_AlRwhI3I&ab_channel=KenJee How I Would Learn Data Science in 2021: https://www.youtube.com/watch?v=41Clrh6nv1s&ab_channel=KenJee My Playlists Data Science Beginners: https://www.youtube.com/playlist?list=PL2zq7klxX5ATMsmyRazei7ZXkP1GHt-vs Project From Scratch: https://www.youtube.com/watch?v=MpF9HENQjDo&list=PL2zq7klxX5ASFejJj80ob9ZAnBHdz5O1t&ab_channel=KenJee Kaggle Projects: https://www.youtube.com/playlist?list=PL2zq7klxX5AQXzNSLtc_LEKFPh2mAvHIO

detail
{'title': 'Beginner Kaggle Data Science Project Walk-Through (Titanic)', 'heatmap': [{'end': 534.609, 'start': 501.431, 'weight': 0.721}, {'end': 598.886, 'start': 569.293, 'weight': 1}, {'end': 642.977, 'start': 618.667, 'weight': 0.901}], 'summary': 'Ken demonstrates accountability in engaging with kaggle community, analyzing titanic dataset, emphasizes citing sources in data analysis, focuses on feature engineering for 800 samples, covers data preprocessing and model training with various classifiers, and discusses voting classifier, model tuning, and performance improvement targeting top 10% on kaggle.', 'chapters': [{'end': 81.272, 'segs': [{'end': 40.761, 'src': 'embed', 'start': 8.045, 'weight': 1, 'content': [{'end': 10.609, 'text': 'Hello everyone, Ken here back with another video for you.', 'start': 8.045, 'duration': 2.564}, {'end': 12.511, 'text': "I've got a little confession to make.", 'start': 11.269, 'duration': 1.242}, {'end': 18.237, 'text': 'So I always tell you guys to use Kaggle to engage in the community, to do projects there.', 'start': 12.931, 'duration': 5.306}, {'end': 22.201, 'text': "And admittedly, I haven't really been that active on the platform.", 'start': 18.477, 'duration': 3.724}, {'end': 26.931, 'text': 'So I decided that it was time for me to be accountable to actually practice what I preach.', 'start': 22.748, 'duration': 4.183}, {'end': 32.755, 'text': 'And I recently started a new Kaggle profile specifically for this channel on the platform.', 'start': 27.451, 'duration': 5.304}, {'end': 35.377, 'text': "So that's going to be linked below in the description.", 'start': 33.236, 'duration': 2.141}, {'end': 40.761, 'text': "I'll also link to the actual analysis that I'm going to do in this video in the description below.", 'start': 35.978, 'duration': 4.783}], 'summary': 'Ken confesses lack of activity on kaggle, starts new profile for accountability.', 'duration': 32.716, 'max_score': 8.045, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg8045.jpg'}, {'end': 81.272, 'src': 'embed', 'start': 44.944, 'weight': 0, 'content': [{'end': 54.874, 'text': "So in the video today I'm going to actually go through the Titanic dataset and do some analysis and actually submit my results to the actual competition.", 'start': 44.944, 'duration': 9.93}, {'end': 61.139, 'text': "So you'll be able to see what the process is like of going through and trying to understand this problem,", 'start': 55.334, 'duration': 5.805}, {'end': 65.363, 'text': 'some of the techniques and algorithms that I use and then actually how to submit your work.', 'start': 61.139, 'duration': 4.224}, {'end': 71.628, 'text': 'I want to make sure that you guys know this video is more focused on how to think about a data science problem,', 'start': 66.184, 'duration': 5.444}, {'end': 74.871, 'text': 'how to think about one of these projects, than actually the implementation.', 'start': 71.628, 'duration': 3.243}, {'end': 81.272, 'text': "So, you know, a lot of people are like, where do I start? How do I know when I'm done? These things I'll cover in this video.", 'start': 75.45, 'duration': 5.822}], 'summary': 'Analyzing titanic dataset, submitting results to competition, focusing on data science problem-solving process.', 'duration': 36.328, 'max_score': 44.944, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg44944.jpg'}], 'start': 8.045, 'title': 'Kaggle accountability and titanic dataset analysis', 'summary': "Discusses ken's accountability in engaging with the kaggle community, starting a new kaggle profile, and analyzing the titanic dataset, emphasizing the problem-solving approach in data science.", 'chapters': [{'end': 81.272, 'start': 8.045, 'title': 'Kaggle accountability and titanic dataset analysis', 'summary': "Discusses ken's accountability in engaging with the kaggle community, starting a new kaggle profile, and analyzing the titanic dataset, emphasizing the focus on problem-solving approach in data science.", 'duration': 73.227, 'highlights': ['Ken starts a new Kaggle profile specifically for his channel to be more accountable and engage in the community.', 'The video focuses on analyzing the Titanic dataset and guides viewers through the process of understanding the problem, using techniques and algorithms, and submitting results to the competition.', "The chapter emphasizes the video's focus on problem-solving approach in data science and addresses common concerns like where to start and determining project completion."]}], 'duration': 73.227, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg8045.jpg', 'highlights': ['The video focuses on analyzing the Titanic dataset and guides viewers through the process of understanding the problem, using techniques and algorithms, and submitting results to the competition.', 'Ken starts a new Kaggle profile specifically for his channel to be more accountable and engage in the community.', "The chapter emphasizes the video's focus on problem-solving approach in data science and addresses common concerns like where to start and determining project completion."]}, {'end': 831.106, 'segs': [{'end': 130.286, 'src': 'embed', 'start': 81.892, 'weight': 0, 'content': [{'end': 86.134, 'text': "The last thing I want to say is it's totally fine to follow along as I'm going through this,", 'start': 81.892, 'duration': 4.242}, {'end': 90.435, 'text': "but you definitely want to make sure that you're citing your sources whenever you publish something.", 'start': 86.134, 'duration': 4.301}, {'end': 93.216, 'text': 'On Kaggle, you can also fork the notebook and experiment with it.', 'start': 90.615, 'duration': 2.601}, {'end': 101.999, 'text': "If you don't do that, you should go to the top of the notebook and use markdown and actually say where you're getting the analysis or the cells from.", 'start': 93.516, 'duration': 8.483}, {'end': 105.56, 'text': 'I tried to do this one without really looking at any additional notebooks.', 'start': 102.319, 'duration': 3.241}, {'end': 109.221, 'text': 'It was just what I was getting from the data and working through it on my own.', 'start': 105.76, 'duration': 3.461}, {'end': 117.503, 'text': "I'll probably go back and try to actually improve the results and I'm going to bring in other people's work there and I'll definitely cite that in the notebook itself.", 'start': 109.721, 'duration': 7.782}, {'end': 120.083, 'text': 'So that was just a quick warning.', 'start': 118.343, 'duration': 1.74}, {'end': 125.925, 'text': "You really want to be careful and you really want to make sure you give credit to other people's work that you're putting out.", 'start': 121.144, 'duration': 4.781}, {'end': 130.286, 'text': "So without further ado, let's jump into the actual analysis.", 'start': 127.045, 'duration': 3.241}], 'summary': "Cite sources when publishing, fork on kaggle, give credit to others' work.", 'duration': 48.394, 'max_score': 81.892, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg81892.jpg'}, {'end': 178.346, 'src': 'embed', 'start': 155.43, 'weight': 1, 'content': [{'end': 163.396, 'text': "So, before I enter in any competition, before I go through any workbook, I make sure to understand actually what's going on, like,", 'start': 155.43, 'duration': 7.966}, {'end': 169.44, 'text': "where the data is coming from, who, who, who it's going to be valuable for, and everything along those lines.", 'start': 163.396, 'duration': 6.044}, {'end': 170.921, 'text': 'So we can go through and read here.', 'start': 169.46, 'duration': 1.461}, {'end': 178.346, 'text': 'Basically the idea with this dataset is we want to predict who actually would survive or who survived this class.', 'start': 171.421, 'duration': 6.925}], 'summary': 'Before competing, understanding data origins and predicting survival in a class is crucial.', 'duration': 22.916, 'max_score': 155.43, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg155430.jpg'}, {'end': 365.78, 'src': 'embed', 'start': 337.603, 'weight': 3, 'content': [{'end': 340.043, 'text': 'So, like what data types are they?', 'start': 337.603, 'duration': 2.44}, {'end': 341.784, 'text': 'You know, are we working with numerics??', 'start': 340.063, 'duration': 1.721}, {'end': 343.565, 'text': 'Are we working with categoricals?', 'start': 341.864, 'duration': 1.701}, {'end': 345.485, 'text': 'What are some of the trends of the data?', 'start': 344.065, 'duration': 1.42}, {'end': 346.226, 'text': 'What are the averages?', 'start': 345.505, 'duration': 0.721}, {'end': 347.766, 'text': 'How many missing values do we have?', 'start': 346.246, 'duration': 1.52}, {'end': 350.827, 'text': 'Next, I look at the histograms and the box plots.', 'start': 348.626, 'duration': 2.201}, {'end': 354.508, 'text': 'This helps you understand the trends in the data.', 'start': 352.208, 'duration': 2.3}, {'end': 359.518, 'text': "So we might see that You know, for example, for FAIR, there's a lot of people that didn't pay anything.", 'start': 354.709, 'duration': 4.809}, {'end': 365.78, 'text': 'Is that something we have to dive into further? The following thing is I want to understand the value counts for the categoricals.', 'start': 359.898, 'duration': 5.882}], 'summary': 'Analyzing data types, trends, averages, and missing values, including histograms and box plots for further insights.', 'duration': 28.177, 'max_score': 337.603, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg337603.jpg'}, {'end': 534.609, 'src': 'heatmap', 'start': 501.431, 'weight': 0.721, 'content': [{'end': 505.412, 'text': 'to help us understand the shape of the data and these types of things.', 'start': 501.431, 'duration': 3.981}, {'end': 512.653, 'text': "So as I've mentioned before, we do training, which is our training data set, which we've isolated here.", 'start': 505.472, 'duration': 7.181}, {'end': 518.134, 'text': 'And we want to understand the data types and also the null values.', 'start': 513.712, 'duration': 4.422}, {'end': 523.174, 'text': 'So we can see age has quite a few null values, and cabin has quite a few null values.', 'start': 518.514, 'duration': 4.66}, {'end': 526.456, 'text': "So we're going to want to start thinking early about how we're going to manage that.", 'start': 523.515, 'duration': 2.941}, {'end': 534.609, 'text': "We're going to go through and actually look at the differences down here, but this is just a good starting point.", 'start': 526.956, 'duration': 7.653}], 'summary': "Analyzing training data to identify null values in 'age' and 'cabin' columns for early management planning.", 'duration': 33.178, 'max_score': 501.431, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg501431.jpg'}, {'end': 598.886, 'src': 'heatmap', 'start': 569.293, 'weight': 1, 'content': [{'end': 576.296, 'text': "When we do this, I think it's relevant to break this into numeric variables and categorical variables.", 'start': 569.293, 'duration': 7.003}, {'end': 583.3, 'text': 'So these are things that we want to understand with a histogram, and these are things that we want to understand with value counts.', 'start': 576.957, 'duration': 6.343}, {'end': 591.484, 'text': 'So this line of code here, I just make histograms for all of the numeric variables, and I just plot what they are on top.', 'start': 583.86, 'duration': 7.624}, {'end': 598.886, 'text': 'So age follows a fairly normal distribution, like the siblings do not, and neither does like the parents.', 'start': 591.943, 'duration': 6.943}], 'summary': 'Data analysis involves using histograms and value counts to understand numeric and categorical variables, with age demonstrating a normal distribution.', 'duration': 29.593, 'max_score': 569.293, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg569293.jpg'}, {'end': 642.977, 'src': 'heatmap', 'start': 618.667, 'weight': 0.901, 'content': [{'end': 624.83, 'text': "And age is already fairly normally distributed, so we don't really have to think about normalizing it.", 'start': 618.667, 'duration': 6.163}, {'end': 625.93, 'text': 'So I might have said scaling.', 'start': 625.05, 'duration': 0.88}, {'end': 629.151, 'text': "We'd want to normalize these and then scale them.", 'start': 626.13, 'duration': 3.021}, {'end': 633.133, 'text': "So let's now look at some correlations.", 'start': 630.012, 'duration': 3.121}, {'end': 642.977, 'text': 'So as we can see, you know, the number of parents and the number of siblings, so like families tend to travel together.', 'start': 633.673, 'duration': 9.304}], 'summary': 'Data shows age is normally distributed, and families tend to travel together.', 'duration': 24.31, 'max_score': 618.667, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg618667.jpg'}], 'start': 81.892, 'title': 'Citing sources in data analysis', 'summary': 'Emphasizes the importance of citing sources in data analysis for ethical reasons and mentions the option to fork notebooks on kaggle for experimentation.', 'chapters': [{'end': 130.286, 'start': 81.892, 'title': 'Citing sources in data analysis', 'summary': 'Emphasizes the importance of citing sources in data analysis for ethical reasons and mentions the option to fork notebooks on kaggle for experimentation.', 'duration': 48.394, 'highlights': ['The importance of citing sources in data analysis is emphasized for ethical reasons, and the option to fork notebooks on Kaggle for experimentation is mentioned.', 'It is stressed that citing sources is essential when publishing analysis, and using markdown to indicate the source of analysis or cells is recommended.', "The speaker mentions working through the data independently without referencing additional notebooks, but plans to improve the results by incorporating others' work and ensuring proper citation in the notebook.", "The speaker emphasizes the need to give credit to others' work in data analysis and highlights the ethical importance of doing so."]}, {'end': 831.106, 'start': 131.408, 'title': 'Kaggle titanic competition analysis', 'summary': 'Discusses the process of understanding and analyzing the kaggle titanic competition dataset to predict survival rates, including data exploration, feature engineering, model building, and insights into factors affecting survival, such as age, fare, and class.', 'duration': 699.698, 'highlights': ['The chapter discusses the process of understanding and analyzing the Kaggle Titanic competition dataset to predict survival rates, including data exploration, feature engineering, model building, and insights into factors affecting survival, such as age, fare, and class. Discussion of the process of understanding and analyzing the Kaggle Titanic competition dataset; Insights into factors affecting survival, such as age, fare, and class; Data exploration, feature engineering, and model building.', 'The data exploration involves understanding the data types, trends, averages, and missing values, as well as exploring histograms, box plots, and value counts for both numeric and categorical variables. Understanding data types, trends, averages, and missing values; Exploring histograms, box plots, and value counts for numeric and categorical variables.', 'Insights from the data exploration include understanding the distribution of variables, correlations between metrics, and consideration of factors like wealth, cabin location, and age affecting survival rates. Understanding variable distributions and correlations; Consideration of factors like wealth, cabin location, and age affecting survival rates.']}], 'duration': 749.214, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg81892.jpg', 'highlights': ['The importance of citing sources in data analysis is emphasized for ethical reasons, and the option to fork notebooks on Kaggle for experimentation is mentioned.', 'The chapter discusses the process of understanding and analyzing the Kaggle Titanic competition dataset to predict survival rates, including data exploration, feature engineering, model building, and insights into factors affecting survival, such as age, fare, and class.', 'It is stressed that citing sources is essential when publishing analysis, and using markdown to indicate the source of analysis or cells is recommended.', 'The data exploration involves understanding the data types, trends, averages, and missing values, as well as exploring histograms, box plots, and value counts for both numeric and categorical variables.', "The speaker mentions working through the data independently without referencing additional notebooks, but plans to improve the results by incorporating others' work and ensuring proper citation in the notebook."]}, {'end': 1215.682, 'segs': [{'end': 889.954, 'src': 'embed', 'start': 831.327, 'weight': 0, 'content': [{'end': 836.59, 'text': 'Maybe if they got on this location, they have a slightly higher chance of surviving.', 'start': 831.327, 'duration': 5.263}, {'end': 843.042, 'text': 'All right, now moving on to the feature engineering.', 'start': 840.441, 'duration': 2.601}, {'end': 854.686, 'text': "So we saw that ticket and cabin, there's just a ton of data and there's only, I think, 800 or so samples here in the training set.", 'start': 843.082, 'duration': 11.604}, {'end': 861.049, 'text': "So if we have too many columns, that really doesn't cooperate well with our data.", 'start': 855.127, 'duration': 5.922}, {'end': 864.43, 'text': 'So we wanna simplify some of this through some feature engineering.', 'start': 861.249, 'duration': 3.181}, {'end': 875.385, 'text': "So if we look at the actual cabin data, we see that there's basically a letter and then a number following it.", 'start': 865.31, 'duration': 10.075}, {'end': 876.465, 'text': "And that's what cabinet is.", 'start': 875.405, 'duration': 1.06}, {'end': 879.988, 'text': 'So I wanted to separate them into individual cabins.', 'start': 876.886, 'duration': 3.102}, {'end': 885.772, 'text': 'And to do that, I used a little bit of regex.', 'start': 881.068, 'duration': 4.704}, {'end': 889.954, 'text': 'So we just split here on spaces.', 'start': 885.952, 'duration': 4.002}], 'summary': 'Data analysis revealed 800 samples in the training set, prompting feature engineering to simplify and separate cabin data using regex.', 'duration': 58.627, 'max_score': 831.327, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg831327.jpg'}, {'end': 981.223, 'src': 'embed', 'start': 953.973, 'weight': 3, 'content': [{'end': 957.215, 'text': "It probably won't, but it's at least worth experimenting with.", 'start': 953.973, 'duration': 3.242}, {'end': 963.395, 'text': 'So as you can see, you know, a lot of the people in the null column did not survive.', 'start': 958.413, 'duration': 4.982}, {'end': 973.54, 'text': 'Um, people that actually, uh, did have a a a clear cabin, had a lot higher survival rate unless they were in a.', 'start': 964.096, 'duration': 9.444}, {'end': 978.882, 'text': 'so I think that we can comfortably use the column letter as a categorical variable here.', 'start': 973.54, 'duration': 5.342}, {'end': 981.223, 'text': 'And that might give us a little bit more insight.', 'start': 979.322, 'duration': 1.901}], 'summary': 'Experimenting with categorical variable column letter for more insight.', 'duration': 27.25, 'max_score': 953.973, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg953973.jpg'}, {'end': 1034.714, 'src': 'embed', 'start': 1004.188, 'weight': 2, 'content': [{'end': 1007.169, 'text': 'It probably is related to where they embarked from and things like that.', 'start': 1004.188, 'duration': 2.981}, {'end': 1009.23, 'text': 'But it was worth experimenting with.', 'start': 1007.269, 'duration': 1.961}, {'end': 1012.391, 'text': 'So I included those here.', 'start': 1009.31, 'duration': 3.081}, {'end': 1024.29, 'text': "Just as a variable, if they have a number, Yeah, if they have a number, then it'd be a one.", 'start': 1013.672, 'duration': 10.618}, {'end': 1027.692, 'text': 'If there is some text involved, then it would be a zero.', 'start': 1024.491, 'duration': 3.201}, {'end': 1034.714, 'text': 'So as you can see, I just kind of explore all of the different ticket lettering conventions.', 'start': 1029.132, 'duration': 5.582}], 'summary': 'Experimenting with ticket lettering conventions to determine numerical value.', 'duration': 30.526, 'max_score': 1004.188, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg1004188.jpg'}, {'end': 1136.953, 'src': 'embed', 'start': 1112.742, 'weight': 4, 'content': [{'end': 1118.805, 'text': "So the next thing we do is I wanted to look at the individual people's names.", 'start': 1112.742, 'duration': 6.063}, {'end': 1124.168, 'text': 'So I thought that this might give us a little bit more data than just if they were male or female.', 'start': 1119.325, 'duration': 4.843}, {'end': 1133.693, 'text': "As you see, there's doctors on board, reverends, majors, there's a lady, a countess, and These are things that might you know.", 'start': 1124.628, 'duration': 9.065}, {'end': 1136.953, 'text': "if someone's royalty or something like that, they might have a higher chance of surviving.", 'start': 1133.693, 'duration': 3.26}], 'summary': 'Analyzing individual passenger data to assess survival likelihood based on titles and roles.', 'duration': 24.211, 'max_score': 1112.742, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg1112742.jpg'}], 'start': 831.327, 'title': 'Feature engineering and data analysis', 'summary': 'Focuses on feature engineering for the titanic dataset with 800 samples, including separating cabin data using regex and analyzing survival rates. it also delves into the analysis of cabin, ticket, and name data, highlighting the experimental nature of the process and the importance of extensive data exploration.', 'chapters': [{'end': 914.184, 'start': 831.327, 'title': 'Feature engineering for titanic dataset', 'summary': 'Discusses feature engineering to simplify the data with 800 samples, focusing on separating cabin data into individual cabins using regex and analyzing survival rates across different cabin features.', 'duration': 82.857, 'highlights': ['The chapter emphasizes the need for feature engineering due to the large amount of data and the limited number of samples in the training set, around 800, to ensure better data cooperation.', 'The speaker uses regex to separate the cabin data into individual cabins, highlighting the process of identifying multiple cabins and analyzing the survival rate across different cabin features.', 'The chapter mentions the importance of looking at the letter of the cabin they were in, indicating a detailed analysis of specific cabin features for the dataset.']}, {'end': 1215.682, 'start': 914.484, 'title': 'Analysis of cabin, ticket, and name data', 'summary': "Explores the usage of null values as categorical variables in the 'cabin' column, the significance of ticket numbers, and the potential insights from individual names to improve model building. it emphasizes the experimental nature of the analysis and the need for extensive data exploration.", 'duration': 301.198, 'highlights': ["Exploring null values as categorical variables in the 'cabin' column, reducing unique cabins to less than 10, and emphasizing the experimental nature of analysis. The chapter explores using null values as categorical variables in the 'cabin' column, reducing unique cabins to less than 10, and highlights the experimental nature of the analysis.", 'Investigating the significance of ticket numbers in relation to embarkation locations, categorizing tickets with numbers and text, and simplifying variables based on ticket lettering conventions. The chapter investigates the significance of ticket numbers in relation to embarkation locations, categorizes tickets with numbers and text, and simplifies variables based on ticket lettering conventions.', 'Exploring the potential insights from individual names, including titles such as doctors, reverends, and military personnel, and considering the impact of royalty on survival rates. The chapter explores potential insights from individual names, including titles such as doctors, reverends, and military personnel, and considers the impact of royalty on survival rates.']}], 'duration': 384.355, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg831327.jpg', 'highlights': ['The chapter emphasizes the need for feature engineering due to the large amount of data and the limited number of samples in the training set, around 800, to ensure better data cooperation.', 'The speaker uses regex to separate the cabin data into individual cabins, highlighting the process of identifying multiple cabins and analyzing the survival rate across different cabin features.', 'Investigating the significance of ticket numbers in relation to embarkation locations, categorizing tickets with numbers and text, and simplifying variables based on ticket lettering conventions.', "Exploring null values as categorical variables in the 'cabin' column, reducing unique cabins to less than 10, and emphasizing the experimental nature of analysis.", 'Exploring the potential insights from individual names, including titles such as doctors, reverends, and military personnel, and considering the impact of royalty on survival rates.']}, {'end': 1613.392, 'segs': [{'end': 1262.708, 'src': 'embed', 'start': 1215.862, 'weight': 2, 'content': [{'end': 1220.665, 'text': 'I just wanted to go through kind of a whole pipeline to get you guys familiar with the process here.', 'start': 1215.862, 'duration': 4.803}, {'end': 1225.388, 'text': 'So next we go into actually pre-processing for the model.', 'start': 1221.485, 'duration': 3.903}, {'end': 1227.869, 'text': 'So none of these models handle null data well.', 'start': 1225.588, 'duration': 2.281}, {'end': 1231.372, 'text': 'So we want to drop the null values from embarked.', 'start': 1228.29, 'duration': 3.082}, {'end': 1234.534, 'text': 'We also only want to include relevant data.', 'start': 1231.852, 'duration': 2.682}, {'end': 1241.039, 'text': 'So I only included ones that we had featured engineered or that were like fine as they were.', 'start': 1235.054, 'duration': 5.985}, {'end': 1246.202, 'text': 'Next, we have to actually transform all this data.', 'start': 1242.82, 'duration': 3.382}, {'end': 1249.985, 'text': 'And I used pandas get dummies.', 'start': 1246.362, 'duration': 3.623}, {'end': 1254.726, 'text': 'What that means is when you have multiple categorical variables.', 'start': 1251.245, 'duration': 3.481}, {'end': 1258.467, 'text': "so let's say, you know, like the class, the cabin right?", 'start': 1254.726, 'duration': 3.741}, {'end': 1259.908, 'text': 'We have seven different things.', 'start': 1258.787, 'duration': 1.121}, {'end': 1261.468, 'text': 'I think it was seven.', 'start': 1260.768, 'duration': 0.7}, {'end': 1262.708, 'text': 'Maybe it was ten different cabins.', 'start': 1261.608, 'duration': 1.1}], 'summary': 'Pipeline process: preprocessing, handling null data, relevant data inclusion, and data transformation using pandas get dummies.', 'duration': 46.846, 'max_score': 1215.862, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg1215862.jpg'}, {'end': 1317.883, 'src': 'embed', 'start': 1286.227, 'weight': 0, 'content': [{'end': 1290.969, 'text': "You can't just have them as one chunk in most cases or one column in most cases.", 'start': 1286.227, 'duration': 4.742}, {'end': 1295.651, 'text': 'So, you know, basically what I do here is I take all of the data.', 'start': 1291.669, 'duration': 3.982}, {'end': 1301.034, 'text': 'So I joined the training and the test sets,', 'start': 1297.192, 'duration': 3.842}, {'end': 1308.557, 'text': "because it's easier to make sure that the training data has the same columns as the test data if I do it this way.", 'start': 1301.034, 'duration': 7.523}, {'end': 1317.883, 'text': 'It also, for the case of a Kaggle competition, might give us a little bit more information in the training set about the distribution of the test set.', 'start': 1309.136, 'duration': 8.747}], 'summary': 'Joining training and test sets provides consistency and additional information for analysis.', 'duration': 31.656, 'max_score': 1286.227, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg1286227.jpg'}, {'end': 1399.917, 'src': 'embed', 'start': 1374.668, 'weight': 3, 'content': [{'end': 1381.571, 'text': 'We, in theory, probably should have used the median for fair because it was not normally distributed.', 'start': 1374.668, 'duration': 6.903}, {'end': 1388.531, 'text': "So that is something that I'll take into account and I might, when I go back through this experiment and see if that actually helps.", 'start': 1382.251, 'duration': 6.28}, {'end': 1391.753, 'text': "I don't think that there were that many missing fair values.", 'start': 1389.232, 'duration': 2.521}, {'end': 1392.974, 'text': "I'd have to look up top.", 'start': 1391.913, 'duration': 1.061}, {'end': 1394.474, 'text': 'We can do that pretty quickly.', 'start': 1392.994, 'duration': 1.48}, {'end': 1397.436, 'text': "This notebook wasn't as short as I thought.", 'start': 1395.235, 'duration': 2.201}, {'end': 1399.917, 'text': 'So we can see fair.', 'start': 1397.656, 'duration': 2.261}], 'summary': 'Consider using median for fair due to non-normal distribution. few missing fair values.', 'duration': 25.249, 'max_score': 1374.668, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg1374668.jpg'}, {'end': 1461.659, 'src': 'embed', 'start': 1436.056, 'weight': 5, 'content': [{'end': 1443.652, 'text': 'It just looks really wonky, but we normalize the fair and we get closer to normal distribution here, which I think is good.', 'start': 1436.056, 'duration': 7.596}, {'end': 1448.154, 'text': 'So that to me made sense to use instead of the traditional FAIR data.', 'start': 1444.152, 'duration': 4.002}, {'end': 1461.659, 'text': "Yeah, so after that we create our dummies down here, we split it back into our train and test sets, and then we're actually ready to get going here.", 'start': 1448.174, 'duration': 13.485}], 'summary': 'Data normalization improves distribution for better analysis and model training.', 'duration': 25.603, 'max_score': 1436.056, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg1436056.jpg'}, {'end': 1606.507, 'src': 'embed', 'start': 1571.953, 'weight': 1, 'content': [{'end': 1576.376, 'text': 'You know, you do have to understand a little bit more of the math when you start getting to feature tuning,', 'start': 1571.953, 'duration': 4.423}, {'end': 1579.078, 'text': "but that's also an experimental process as well.", 'start': 1576.376, 'duration': 2.702}, {'end': 1586.184, 'text': "So we import the cross-validation and that's how we're going to evaluate the success of these models with the cross-val score here.", 'start': 1579.841, 'duration': 6.343}, {'end': 1596.57, 'text': 'So to run any of these, you just import it from sklearn, you create an instance of it, and then you can fit it to the data down here.', 'start': 1586.745, 'duration': 9.825}, {'end': 1606.507, 'text': "I'll show you how to do this without cross-validation, when we're actually predicting for the results on the test set,", 'start': 1596.99, 'duration': 9.517}], 'summary': 'Feature tuning involves experimental process, evaluating models with cross-validation and predicting test results.', 'duration': 34.554, 'max_score': 1571.953, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg1571953.jpg'}], 'start': 1215.862, 'title': 'Data preprocessing and model training', 'summary': 'Covers data pre-processing including dropping null values, relevant data inclusion, and categorical variable transformation using pandas get dummies. it also highlights joining training and test sets, filling missing values, dropping null values, normalizing features, creating dummy variables, and evaluating various models using cross-validation, including naive bayes, logistic regression, decision tree, k-nearest neighbor, random forest, support vector classifier, xgboost, and a voting classifier.', 'chapters': [{'end': 1286.047, 'start': 1215.862, 'title': 'Data pre-processing for model training', 'summary': 'Covers the process of data pre-processing for model training, including dropping null values, including relevant data, and transforming categorical variables using pandas get dummies.', 'duration': 70.185, 'highlights': ["The process involves dropping null values from the 'embarked' column and including only relevant data for model training.", 'Pandas get dummies is used to transform multiple categorical variables into individual columns with binary values (0 and 1) for model integration.', "The transformation of categorical variables involves creating individual columns for each category, allocating '0' for absence and '1' for presence within the category."]}, {'end': 1613.392, 'start': 1286.227, 'title': 'Data preprocessing and model evaluation', 'summary': 'Highlights the process of joining the training and test sets, filling missing values with means, dropping null values, normalizing features, creating dummy variables, and evaluating various models using cross-validation, including naive bayes, logistic regression, decision tree, k-nearest neighbor, random forest, support vector classifier, xgboost, and a voting classifier.', 'duration': 327.165, 'highlights': ['Joining Training and Test Sets The process of joining the training and test sets is highlighted, which allows for ensuring that the training data has the same columns as the test data and provides more information in the training set about the distribution of the test set, particularly beneficial in the context of a Kaggle competition.', 'Filling Missing Values The practice of filling missing values with means for both age and fair is emphasized, with a note about the possibility of using the median for fair due to its non-normal distribution, and the observation that there were no missing fair values found.', 'Normalizing Features The process of normalizing the fair to achieve a closer to normal distribution is mentioned, which is deemed beneficial as it made sense to use instead of the traditional FAIR data.', 'Model Evaluation using Cross-Validation The importance of model evaluation using cross-validation is highlighted, which involves training and predicting on held-out data to provide a better estimation of real-world performance, followed by the initial evaluation of basic models including Naive Bayes, logistic regression, decision tree, k-nearest neighbor, random forest, support vector classifier, XGBoost, and a voting classifier.']}], 'duration': 397.53, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg1215862.jpg', 'highlights': ['Joining training and test sets to ensure consistent columns and provide more information in the training set about the distribution of the test set.', 'Model evaluation using cross-validation to provide a better estimation of real-world performance.', 'Pandas get dummies used to transform multiple categorical variables into individual columns with binary values (0 and 1) for model integration.', 'Filling missing values with means for age and fair, and the possibility of using the median for fair due to its non-normal distribution.', "The process involves dropping null values from the 'embarked' column and including only relevant data for model training.", 'Normalizing fair to achieve a closer to normal distribution, which is beneficial for model integration.']}, {'end': 1888.784, 'segs': [{'end': 1666.092, 'src': 'embed', 'start': 1637.957, 'weight': 2, 'content': [{'end': 1644.262, 'text': "In what's known as a hard voting classifier, then the model would spit out that they believe that that person survived.", 'start': 1637.957, 'duration': 6.305}, {'end': 1648.177, 'text': "If we're using a soft voting classifier,", 'start': 1645.515, 'duration': 2.662}, {'end': 1654.943, 'text': 'that means that the models are sending forward their confidence or the probability that they think this person survived.', 'start': 1648.177, 'duration': 6.766}, {'end': 1660.187, 'text': "So let's say the logistic, let's just use a two voting classifier that is soft.", 'start': 1655.423, 'duration': 4.764}, {'end': 1666.092, 'text': "Let's say the logistic regression said that it was 100% chance that this person survived.", 'start': 1660.587, 'duration': 5.505}], 'summary': 'In a soft voting classifier, models send confidence levels - e.g., 100% survival chance from logistic regression.', 'duration': 28.135, 'max_score': 1637.957, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg1637957.jpg'}, {'end': 1720.841, 'src': 'embed', 'start': 1681.034, 'weight': 0, 'content': [{'end': 1687.619, 'text': 'So that would be over 50%, and it would still say that the voting classifier would believe that they survived.', 'start': 1681.034, 'duration': 6.585}, {'end': 1688.901, 'text': "So that's how voting works.", 'start': 1687.94, 'duration': 0.961}, {'end': 1691.763, 'text': 'Generally, if you have some breadth of models,', 'start': 1689.121, 'duration': 2.642}, {'end': 1697.648, 'text': 'voting classifiers work very well because they help to normalize your results and generalize the data a little bit.', 'start': 1691.763, 'duration': 5.885}, {'end': 1707.213, 'text': 'So in most cases, ensemble approaches, which are already Random Forests, XGBoost are ready ensemble approaches.', 'start': 1698.028, 'duration': 9.185}, {'end': 1710.855, 'text': "They're really powerful techniques for solving problems.", 'start': 1707.733, 'duration': 3.122}, {'end': 1714.978, 'text': "And they are generally a best practice when you're not using deep learning.", 'start': 1711.816, 'duration': 3.162}, {'end': 1717.579, 'text': 'In this notebook, I chose not to use deep learning.', 'start': 1715.598, 'duration': 1.981}, {'end': 1720.841, 'text': "The size of the data is very small, so it probably wouldn't be optimal.", 'start': 1717.919, 'duration': 2.922}], 'summary': 'Voting classifiers, ensemble approaches, like random forests and xgboost, are effective for solving problems and normalizing results with over 50% accuracy.', 'duration': 39.807, 'max_score': 1681.034, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg1681034.jpg'}, {'end': 1827.086, 'src': 'embed', 'start': 1803.39, 'weight': 3, 'content': [{'end': 1811.376, 'text': "GridSearch allows you to just put in a bunch of parameters and try them all, and it'll spit out what parameters have the best results.", 'start': 1803.39, 'duration': 7.986}, {'end': 1816.402, 'text': "So that's what we do here, is we actually go through, we try all these parameters,", 'start': 1812.641, 'duration': 3.761}, {'end': 1821.044, 'text': 'and these are the parameters that ended up having the best performance for us.', 'start': 1816.402, 'duration': 4.642}, {'end': 1823.205, 'text': 'And this is the score that it had.', 'start': 1821.344, 'duration': 1.861}, {'end': 1827.086, 'text': 'So we do that for all of these different classifiers.', 'start': 1823.785, 'duration': 3.301}], 'summary': 'Gridsearch optimizes parameters for best performance across classifiers.', 'duration': 23.696, 'max_score': 1803.39, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg1803390.jpg'}, {'end': 1883.661, 'src': 'embed', 'start': 1849.54, 'weight': 4, 'content': [{'end': 1853.342, 'text': 'And if I tried them all, it would take days, months, years, whatever it would be.', 'start': 1849.54, 'duration': 3.802}, {'end': 1858.966, 'text': 'So I used a kind of funnel approach to find parameters that work well for me.', 'start': 1853.863, 'duration': 5.103}, {'end': 1864.67, 'text': 'So I did a very broad, uh, random, uh, randomized classifier.', 'start': 1859.506, 'duration': 5.164}, {'end': 1866.131, 'text': 'So a randomized search.', 'start': 1865.19, 'duration': 0.941}, {'end': 1869.911, 'text': "And what that does is it doesn't try everything in this grid.", 'start': 1866.829, 'duration': 3.082}, {'end': 1871.112, 'text': "It doesn't try all of the options.", 'start': 1869.951, 'duration': 1.161}, {'end': 1875.495, 'text': 'It randomly samples from it and it gives you what the best results were.', 'start': 1871.172, 'duration': 4.323}, {'end': 1883.661, 'text': 'And then after you have the best results, you can tune it a little bit more and find something that does just a little bit better.', 'start': 1876.155, 'duration': 7.506}], 'summary': 'Used randomized search to find best parameters for classifier, saving time and effort.', 'duration': 34.121, 'max_score': 1849.54, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg1849540.jpg'}], 'start': 1614.032, 'title': 'Voting classifier and model tuning', 'summary': 'Provides an overview of the voting classifier, explaining hard and soft voting, the benefits of ensemble approaches, and the limitations of deep learning, as well as discusses the use of gridsearch and randomizedsearch for model tuning, and the funnel method for improved performance.', 'chapters': [{'end': 1739.172, 'start': 1614.032, 'title': 'Voting classifier in machine learning', 'summary': 'Provides an overview of the voting classifier, explaining the concepts of hard and soft voting, the benefits of using ensemble approaches in solving problems, and the limitations of using deep learning for small data.', 'duration': 125.14, 'highlights': ['Ensemble approaches like Random Forests and XGBoost are generally a best practice when not using deep learning. Ensemble approaches such as Random Forests and XGBoost are powerful techniques for solving problems and are considered best practice when not using deep learning.', 'Voting classifiers work well with a breadth of models, helping to normalize results and generalize the data. Voting classifiers work well with a variety of models, aiding in result normalization and data generalization.', 'Explanation of hard and soft voting in the context of voting classifier. The chapter explains the concepts of hard and soft voting in the context of a voting classifier, illustrating how the models make decisions and handle confidence or probability.']}, {'end': 1888.784, 'start': 1739.352, 'title': 'Model tuning and performance improvement', 'summary': 'Discusses the importance of testing data in model validation, the use of gridsearch and randomizedsearch for model tuning, and the approach of using a funnel method to find optimal parameters, resulting in improved performance across classifiers.', 'duration': 149.432, 'highlights': ['The chapter emphasizes the importance of experimenting with testing data, as models performing well in validation may overtrain and not perform as well on the actual test set. ', 'GridSearch and RandomizedSearch are used for model tuning, with GridSearch allowing the exploration of various parameters to identify those with the best results and RandomizedSearch providing a simplified approach by randomly sampling from parameter options. ', 'The approach of using a funnel method to find optimal parameters is described, aiming to simplify the process, save time, and improve performance across classifiers. ']}], 'duration': 274.752, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg1614032.jpg', 'highlights': ['Ensemble approaches like Random Forests and XGBoost are generally a best practice when not using deep learning.', 'Voting classifiers work well with a breadth of models, helping to normalize results and generalize the data.', 'Explanation of hard and soft voting in the context of voting classifier.', 'GridSearch allowing the exploration of various parameters to identify those with the best results.', 'RandomizedSearch providing a simplified approach by randomly sampling from parameter options.', 'The approach of using a funnel method to find optimal parameters is described.']}, {'end': 2275.109, 'segs': [{'end': 1941.194, 'src': 'embed', 'start': 1889.605, 'weight': 0, 'content': [{'end': 1902.693, 'text': 'So for here we get our best classifier with random forest was at, you know, 83.5%, and then for down here, XGBoost, the best performance, I believe,', 'start': 1889.605, 'duration': 13.088}, {'end': 1904.576, 'text': 'was like 85.2%, which is really high.', 'start': 1902.693, 'duration': 1.883}, {'end': 1911.243, 'text': "So in this case it actually ended up overfitting spoiler alert and that didn't produce the best results.", 'start': 1905.336, 'duration': 5.907}, {'end': 1915.008, 'text': "but it's interesting to have in here that that was a really high performance.", 'start': 1911.243, 'duration': 3.765}, {'end': 1920.748, 'text': "One thing that I haven't talked at all about in this is the actual feature importances.", 'start': 1916.167, 'duration': 4.581}, {'end': 1925.37, 'text': "So what this means is which of the variables that we've put in,", 'start': 1921.208, 'duration': 4.162}, {'end': 1932.252, 'text': 'which of the features actually have the greatest impact on predicting if someone will survive or not?', 'start': 1925.37, 'duration': 6.882}, {'end': 1941.194, 'text': "So you know, just looking at this, we see that the how much they paid their age, if they're, you know if they had, if they were male,", 'start': 1932.752, 'duration': 8.442}], 'summary': 'Best classifier: xgboost with 85.2% performance, overfitting, and feature importances.', 'duration': 51.589, 'max_score': 1889.605, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg1889605.jpg'}, {'end': 2028.136, 'src': 'embed', 'start': 2002.228, 'weight': 2, 'content': [{'end': 2010.471, 'text': 'Um, and so I submitted both of those, but I also wanted to do another voting classifier with our actual tuned model.', 'start': 2002.228, 'duration': 8.243}, {'end': 2015.472, 'text': 'So what I did is I went through and we did the exact same thing that we did before.', 'start': 2010.691, 'duration': 4.781}, {'end': 2021.594, 'text': 'I just took the best estimators from all of the tuned variables, and I made a bunch of different voting classifiers.', 'start': 2015.872, 'duration': 5.722}, {'end': 2028.136, 'text': 'So I tried a hard voting, a soft voting with just, uh, k-nearest, neighbors, random forest and support vector classifier.', 'start': 2022.014, 'duration': 6.122}], 'summary': 'Submitted two models, created multiple voting classifiers with tuned variables: hard voting, soft voting with k-nearest neighbors, random forest, and support vector classifier.', 'duration': 25.908, 'max_score': 2002.228, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg2002228.jpg'}, {'end': 2089.476, 'src': 'embed', 'start': 2064.117, 'weight': 3, 'content': [{'end': 2072.563, 'text': 'So one of the last things that I did was I wanted to see if the weighting impacted the output here.', 'start': 2064.117, 'duration': 8.446}, {'end': 2080.889, 'text': 'So I did a grid search and I experimented with different votes in the soft voting classifier.', 'start': 2072.643, 'duration': 8.246}, {'end': 2089.476, 'text': 'So what that means is I can add additional weighting to one or two of the models so that it counts for more in the analysis.', 'start': 2081.35, 'duration': 8.126}], 'summary': 'Conducted grid search and experimented with weighting in soft voting classifier.', 'duration': 25.359, 'max_score': 2064.117, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg2064117.jpg'}, {'end': 2237.305, 'src': 'embed', 'start': 2207.54, 'weight': 4, 'content': [{'end': 2212.541, 'text': "And well, it looks like it didn't train, so that wasn't very good.", 'start': 2207.54, 'duration': 5.001}, {'end': 2215.302, 'text': 'But you get the idea here.', 'start': 2212.621, 'duration': 2.681}, {'end': 2222.004, 'text': 'So as you can see, the best results that I had were these two runs that I did.', 'start': 2215.602, 'duration': 6.402}, {'end': 2228.218, 'text': "I'm going to probably try and get it just a little bit better so I can break into that top 10%.", 'start': 2222.024, 'duration': 6.194}, {'end': 2237.305, 'text': "You know, that's just some fun tuning that I'm going to experiment with and you know, hopefully we can, um, we can do a little better and, and uh,", 'start': 2228.218, 'duration': 9.087}], 'summary': 'Best results from two runs, aiming for top 10% with further tuning.', 'duration': 29.765, 'max_score': 2207.54, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg2207540.jpg'}], 'start': 1889.605, 'title': 'Tuning models for improved performance', 'summary': 'Discusses tuning xgboost and random forest classifiers, experimenting with feature importance, model ensembling, and exploring weighting impact, aiming to enhance kaggle performance, achieving 85.2% and targeting top 10%.', 'chapters': [{'end': 2275.109, 'start': 1889.605, 'title': 'Tuning models for improved performance', 'summary': 'Discusses the tuning of xgboost and random forest classifiers, experimenting with feature importance, model ensembling, and exploring the impact of weighting on the output, aiming to enhance the performance on kaggle, with the best performance achieved at 85.2% and the goal to break into the top 10%.', 'duration': 385.504, 'highlights': ['The best performance achieved was 85.2% with XGBoost, indicating a high accuracy. XGBoost achieved the highest performance at 85.2%, demonstrating its potential for accuracy.', 'Feature importances revealed that variables such as fare, age, gender, and class had the greatest impact on predicting survival, providing valuable insights for model interpretation. Feature importances highlighted the significant impact of fare, age, gender, and class on survival prediction, offering valuable insights for model interpretability.', 'Experimenting with different voting classifiers and model ensembling showed iterative improvements, with the best results achieved through soft voting with specific models, highlighting the importance of the ensemble approach. The iterative process of experimenting with different voting classifiers and model ensembling demonstrated continuous improvements, emphasizing the significance of the ensemble approach for enhanced performance.', "Exploration of weighting in the soft voting classifier revealed that the optimal weighting produced the best score, showcasing the impact of weighting on model performance. Exploring weighting in the soft voting classifier indicated that optimal weighting significantly influenced the model's performance, leading to the best score.", 'The goal to break into the top 10% on Kaggle reflects the ambition to achieve exceptional performance and competitive ranking. The aspiration to break into the top 10% on Kaggle signifies the ambition to attain exceptional performance and secure a competitive ranking.']}], 'duration': 385.504, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I3FBJdiExcg/pics/I3FBJdiExcg1889605.jpg', 'highlights': ['XGBoost achieved the highest performance at 85.2%, demonstrating its potential for accuracy.', 'Feature importances highlighted the significant impact of fare, age, gender, and class on survival prediction, offering valuable insights for model interpretability.', 'The iterative process of experimenting with different voting classifiers and model ensembling demonstrated continuous improvements, emphasizing the significance of the ensemble approach for enhanced performance.', "Exploring weighting in the soft voting classifier indicated that optimal weighting significantly influenced the model's performance, leading to the best score.", 'The aspiration to break into the top 10% on Kaggle signifies the ambition to attain exceptional performance and secure a competitive ranking.']}], 'highlights': ['Ken demonstrates accountability in engaging with kaggle community, analyzing titanic dataset, emphasizes citing sources in data analysis, focuses on feature engineering for 800 samples, covers data preprocessing and model training with various classifiers, and discusses voting classifier, model tuning, and performance improvement targeting top 10% on kaggle.', 'XGBoost achieved the highest performance at 85.2%, demonstrating its potential for accuracy.', 'The iterative process of experimenting with different voting classifiers and model ensembling demonstrated continuous improvements, emphasizing the significance of the ensemble approach for enhanced performance.', 'The chapter emphasizes the need for feature engineering due to the large amount of data and the limited number of samples in the training set, around 800, to ensure better data cooperation.', 'Ensemble approaches like Random Forests and XGBoost are generally a best practice when not using deep learning.', 'Joining training and test sets to ensure consistent columns and provide more information in the training set about the distribution of the test set.', 'The video focuses on analyzing the Titanic dataset and guides viewers through the process of understanding the problem, using techniques and algorithms, and submitting results to the competition.', 'The importance of citing sources in data analysis is emphasized for ethical reasons, and the option to fork notebooks on Kaggle for experimentation is mentioned.', 'Voting classifiers work well with a breadth of models, helping to normalize results and generalize the data.', "Exploring weighting in the soft voting classifier indicated that optimal weighting significantly influenced the model's performance, leading to the best score."]}