title
Kaggle's 30 Days Of ML (Day-1): Getting Started With Kaggle

description
This video is a walkthrough of Kaggle's #30DaysOfML. In this video, we create a #Kaggle account, learn what upvotes and comments are, and make our first submission to a Kaggle competition. If you follow all the steps in this video, you will become a Kaggle contributor from novice. We follow steps in: https://www.kaggle.com/alexisbcook/getting-started-with-kaggle Note: this video is not sponsored by Kaggle! Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :) To buy my book, Approaching (Almost) Any Machine Learning problem, please visit: https://bit.ly/buyaaml Follow me on: Twitter: https://twitter.com/abhi1thakur LinkedIn: https://www.linkedin.com/in/abhi1thakur/ Kaggle: https://kaggle.com/abhishek Instagram: https://instagram.com/abhi1thakur

detail
{'title': "Kaggle's 30 Days Of ML (Day-1): Getting Started With Kaggle", 'heatmap': [{'end': 2124.554, 'start': 2015.199, 'weight': 0.857}], 'summary': 'The 30 days of ml challenge on kaggle guides participants to progress from beginners to competitors in 30 days, covering python and pandas for data analysis, constructing a random forest model, and achieving 77.511% accuracy in the titanic competition, emphasizing optimization and community engagement.', 'chapters': [{'end': 356.702, 'segs': [{'end': 30.379, 'src': 'embed', 'start': 0.731, 'weight': 0, 'content': [{'end': 2.833, 'text': 'Hello everyone and welcome to my YouTube channel.', 'start': 0.731, 'duration': 2.102}, {'end': 8.74, 'text': "In today's video we are going to look at 30 days of ML which is the challenge from Kaggle.", 'start': 2.973, 'duration': 5.767}, {'end': 18.21, 'text': "So in these 30 days you're going to learn something new each day for the first 15 days and in the next 15 days you're going to work on a Kaggle competition.", 'start': 9.26, 'duration': 8.95}, {'end': 25.432, 'text': 'So it says that from machine learning beginner to a Kaggle competitor in 30 days.', 'start': 19.884, 'duration': 5.548}, {'end': 30.379, 'text': "And I think it's a very nice opportunity for beginners and you should definitely do that.", 'start': 25.592, 'duration': 4.787}], 'summary': 'Join the 30 days of ml challenge from kaggle to progress from beginner to kaggle competitor in one month.', 'duration': 29.648, 'max_score': 0.731, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY731.jpg'}, {'end': 211.315, 'src': 'embed', 'start': 179.377, 'weight': 1, 'content': [{'end': 189.825, 'text': 'And if you look at Kaggle.com slash progression, it will tell you what it takes to become grandmaster in any level or any other level.', 'start': 179.377, 'duration': 10.448}, {'end': 199.611, 'text': 'So if you have just joined, you are a novice, you have registered, and so you see this checkmark here in my profile.', 'start': 192.387, 'duration': 7.224}, {'end': 211.315, 'text': 'If I run one notebook or a script, I make one competition or task submission, I get one point for that, so another checkmark.', 'start': 200.531, 'duration': 10.784}], 'summary': 'Kaggle.com/progression shows steps to become grandmaster; 1 point for each notebook or competition submission.', 'duration': 31.938, 'max_score': 179.377, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY179377.jpg'}], 'start': 0.731, 'title': 'Joining kaggle and the 30 days of ml challenge', 'summary': 'Introduces the 30 days of ml challenge from kaggle, where participants learn something new each day for the first 15 days and then work on a kaggle competition for the remaining 15 days, aiming to progress from machine learning beginner to a kaggle competitor in 30 days. it also discusses the benefits of joining the machine learning discord community and setting up a kaggle account, including the features of kaggle and the progression system, detailing how to earn points and medals for various activities on the platform.', 'chapters': [{'end': 50.13, 'start': 0.731, 'title': '30 days of ml challenge', 'summary': 'Introduces the 30 days of ml challenge from kaggle, where participants learn something new each day for the first 15 days and then work on a kaggle competition for the remaining 15 days, aiming to progress from machine learning beginner to a kaggle competitor in 30 days.', 'duration': 49.399, 'highlights': ['Participants in the 30 days of ML challenge learn something new each day for the first 15 days and work on a Kaggle competition for the next 15 days.', 'The challenge aims to progress from machine learning beginner to a Kaggle competitor in 30 days.', 'The chapter discusses the opportunity for beginners to participate in the 30 days of ML challenge from Kaggle.']}, {'end': 356.702, 'start': 50.75, 'title': 'Joining kaggle and leveraging the community', 'summary': 'Discusses the benefits of joining the machine learning discord community and setting up a kaggle account, including the features of kaggle and the progression system, detailing how to earn points and medals for various activities on the platform.', 'duration': 305.952, 'highlights': ['Joining the machine learning Discord community allows for instant problem-solving with thousands of members.', 'Setting up a Kaggle account provides access to competitions, datasets, notebooks, and discussions, with progression from novice to grandmaster levels in each category.', 'Earning points on Kaggle involves activities such as making submissions, comments, quotes, and participating in discussions, competitions, and creating datasets or notebooks.', 'The points earned on Kaggle can decline over time and are divided by the number of teammates, with the system encouraging collaboration by dividing points among teammates.']}], 'duration': 355.971, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY731.jpg', 'highlights': ['The challenge aims to progress from machine learning beginner to a Kaggle competitor in 30 days.', 'Joining the machine learning Discord community allows for instant problem-solving with thousands of members.', 'Setting up a Kaggle account provides access to competitions, datasets, notebooks, and discussions, with progression from novice to grandmaster levels in each category.', 'Participants in the 30 days of ML challenge learn something new each day for the first 15 days and work on a Kaggle competition for the next 15 days.']}, {'end': 922.224, 'segs': [{'end': 411.078, 'src': 'embed', 'start': 387.966, 'weight': 4, 'content': [{'end': 395.729, 'text': "you don't know what anything is, how difficult it is, but you have to get to the contributor level and it's it's not very difficult,", 'start': 387.966, 'duration': 7.763}, {'end': 397.71, 'text': 'you just need to follow some steps.', 'start': 395.729, 'duration': 1.981}, {'end': 401.592, 'text': 'so now we are at step three.', 'start': 398.63, 'duration': 2.962}, {'end': 405.394, 'text': 'so now we have taken a look at your profile.', 'start': 401.592, 'duration': 3.802}, {'end': 411.078, 'text': 'so kaggle.com, abhishek, that because abhishek is my username, or i can also do kaggle.com me, it will take it.', 'start': 405.394, 'duration': 5.684}], 'summary': "Reaching contributor level on kaggle involves following a few steps, with step three involving reviewing the user's profile.", 'duration': 23.112, 'max_score': 387.966, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY387966.jpg'}, {'end': 495.284, 'src': 'embed', 'start': 442.804, 'weight': 0, 'content': [{'end': 444.225, 'text': 'So submit to Titanic.', 'start': 442.804, 'duration': 1.421}, {'end': 450.091, 'text': 'Okay, so first you will run a notebook and make a competition submission.', 'start': 445.026, 'duration': 5.065}, {'end': 454.716, 'text': 'To do this, follow the instructions in this notebook.', 'start': 451.252, 'duration': 3.464}, {'end': 461.042, 'text': 'So we have another notebook that we have to follow to make a submission in the Kaggle Titanic competition.', 'start': 454.756, 'duration': 6.286}, {'end': 463.725, 'text': "So let's take a look at this notebook.", 'start': 462.083, 'duration': 1.642}, {'end': 464.606, 'text': 'What do we have here?', 'start': 463.805, 'duration': 0.801}, {'end': 474.228, 'text': 'Okay, so you have logged into Kaggle and now you see a lot of things a lot of data sets, a lot of competition,', 'start': 466.553, 'duration': 7.675}, {'end': 475.731, 'text': 'people discussing different kinds of things.', 'start': 474.228, 'duration': 1.503}, {'end': 477.254, 'text': "So don't confuse yourself.", 'start': 475.771, 'duration': 1.483}, {'end': 486.255, 'text': 'look at 101 competitions first, and titanic is one of the most popular 101 competition for beginners.', 'start': 478.047, 'duration': 8.208}, {'end': 495.284, 'text': 'so, um now, um, what we are going to do is we are going to see what the titanic competition is about first.', 'start': 486.255, 'duration': 9.029}], 'summary': 'Follow steps to make a submission in kaggle titanic competition.', 'duration': 52.48, 'max_score': 442.804, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY442804.jpg'}, {'end': 706.312, 'src': 'embed', 'start': 671.625, 'weight': 5, 'content': [{'end': 676.029, 'text': "Before that, let's go back to this notebook and see what it says.", 'start': 671.625, 'duration': 4.404}, {'end': 679.912, 'text': 'Okay, so the notebook also talks about the data that we have.', 'start': 676.63, 'duration': 3.282}, {'end': 681.314, 'text': 'There are three different files.', 'start': 680.013, 'duration': 1.301}, {'end': 687.399, 'text': 'One is train.csv, then you have test.csv, and you have gendersubmission.csv.', 'start': 681.414, 'duration': 5.985}, {'end': 694.164, 'text': 'So train.csv contains the details of subset of passengers, 891 passengers.', 'start': 688.94, 'duration': 5.224}, {'end': 706.312, 'text': 'And if I go to this data page, I can see, I can click on train and then it will show me what train.csv consists of.', 'start': 695.184, 'duration': 11.128}], 'summary': 'Three data files: train.csv (891 passengers), test.csv, gendersubmission.csv.', 'duration': 34.687, 'max_score': 671.625, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY671625.jpg'}], 'start': 360.127, 'title': 'Kaggle progression and titanic competition submission', 'summary': 'Covers the progression from novice to contributor level on kaggle, involving steps such as profile overview, understanding progression and performance styles, and submitting to a competition. it also details the process of making a submission to the kaggle titanic competition, including joining, understanding rules, building a predictive model, and formatting the submission file, with an evaluation based on accuracy.', 'chapters': [{'end': 440.428, 'start': 360.127, 'title': 'Kaggle account progression', 'summary': 'Discusses the progression from novice to contributor level on kaggle, involving steps such as profile overview, understanding progression and performance styles, and submitting to a competition.', 'duration': 80.301, 'highlights': ['The progression from novice to contributor level on Kaggle involves steps such as profile overview, understanding progression and performance styles, and submitting to a competition.', 'To level up on Kaggle, users start as novices and need to follow steps to reach the contributor level, which is not very difficult to achieve.', 'The third step in the progression involves submitting to an actual competition on Kaggle.']}, {'end': 922.224, 'start': 442.804, 'title': 'Kaggle titanic competition submission', 'summary': "Details the process of making a submission to the kaggle titanic competition, including joining the competition, understanding the rules, building a predictive model, and formatting the submission file, with an evaluation based on accuracy. the notebook also covers the dataset's details, such as the columns in the training set and the process of running a kaggle notebook.", 'duration': 479.42, 'highlights': ['The process of joining the Kaggle Titanic competition and understanding the rules is explained, with emphasis on the popularity of the competition for beginners. None', 'The task of predicting who survives the Titanic using machine learning and the provided passenger data is outlined, with a focus on using the machine learning model to create predictions. None', 'The evaluation of the model is based on accuracy, with a clear explanation of how the predictions should be formatted and the simplicity of the evaluation metric. evaluation based on accuracy', "Details about the dataset, including the columns in the training set and the absence of the 'survived' column in the test set, are provided, with a brief explanation of the gender submission file's purpose. 12 columns in the train.csv, 891 passengers in the training set", 'The process of running a Kaggle notebook and the convenience of not having to set up everything or worry about installing libraries is highlighted. None']}], 'duration': 562.097, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY360127.jpg', 'highlights': ['The progression from novice to contributor level on Kaggle involves steps such as profile overview, understanding progression and performance styles, and submitting to a competition.', 'To level up on Kaggle, users start as novices and need to follow steps to reach the contributor level, which is not very difficult to achieve.', 'The third step in the progression involves submitting to an actual competition on Kaggle.', 'The process of joining the Kaggle Titanic competition and understanding the rules is explained, with emphasis on the popularity of the competition for beginners.', 'The task of predicting who survives the Titanic using machine learning and the provided passenger data is outlined, with a focus on using the machine learning model to create predictions.', 'The evaluation of the model is based on accuracy, with a clear explanation of how the predictions should be formatted and the simplicity of the evaluation metric.', "Details about the dataset, including the columns in the training set and the absence of the 'survived' column in the test set, are provided, with a brief explanation of the gender submission file's purpose.", 'The process of running a Kaggle notebook and the convenience of not having to set up everything or worry about installing libraries is highlighted.', '12 columns in the train.csv, 891 passengers in the training set']}, {'end': 1487.24, 'segs': [{'end': 982.458, 'src': 'embed', 'start': 953.345, 'weight': 0, 'content': [{'end': 955.306, 'text': "So let's take a look now.", 'start': 953.345, 'duration': 1.961}, {'end': 964.51, 'text': "So what's happening? So we have imported a package called numpy and a package pandas for reading CSVs.", 'start': 956.586, 'duration': 7.924}, {'end': 972.776, 'text': 'And then we import OS, which is also a Python package, which is there in Python when you install Python for the first time.', 'start': 965.854, 'duration': 6.922}, {'end': 982.458, 'text': 'And we go through each and every file and directory inside this folder called slash Kaggle slash input, and try to predict print.', 'start': 972.796, 'duration': 9.662}], 'summary': 'Using numpy and pandas to read csvs and iterate through directories in python.', 'duration': 29.113, 'max_score': 953.345, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY953345.jpg'}, {'end': 1369.624, 'src': 'embed', 'start': 1344.037, 'weight': 2, 'content': [{'end': 1349.998, 'text': 'So if you have to find out how many females survived, there are many different ways of doing this.', 'start': 1344.037, 'duration': 5.961}, {'end': 1351.198, 'text': 'This is one of the ways.', 'start': 1350.338, 'duration': 0.86}, {'end': 1353.718, 'text': 'So crane underscore data is your data frame.', 'start': 1351.318, 'duration': 2.4}, {'end': 1356.919, 'text': "It's your Pandas data frame and .lock is a function inside that.", 'start': 1353.798, 'duration': 3.121}, {'end': 1369.624, 'text': 'And to do the same thing, we can also do something like train underscore data, train underscore data dot sex equal to female.', 'start': 1359.201, 'duration': 10.423}], 'summary': 'Various methods for finding the number of female survivors in the data frame were discussed.', 'duration': 25.587, 'max_score': 1344.037, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY1344037.jpg'}], 'start': 923.417, 'title': 'Python and titanic data analysis', 'summary': 'Covers python and pandas for data analysis, including handling missing values and preparing for predictions in a machine learning problem, within the context of the titanic dataset. it also highlights the significant difference in survival rates between female and male passengers, with 74.2% of women and 18.89% of men surviving, crucial for exploratory data analysis (eda).', 'chapters': [{'end': 1241.355, 'start': 923.417, 'title': 'Python data analysis tutorial', 'summary': 'Covers the use of python and pandas for data analysis, including loading and exploring data, handling missing values, and preparing for predictions in a machine learning problem, all within the context of the titanic dataset.', 'duration': 317.938, 'highlights': ['Python environment allows for data analysis without the need to specify the accelerator, with the current usage being limited to CPU.', 'The tutorial involves importing packages like numpy, pandas, and OS, and demonstrates loading and exploring datasets using pandas, including identifying missing values and exploring data patterns.', 'The tutorial encourages further learning about Pandas through its documentation and emphasizes its popularity and widespread use in data analysis.']}, {'end': 1487.24, 'start': 1241.635, 'title': 'Survival rates of female and male passengers', 'summary': 'Explains the process of calculating the survival rates of female and male passengers, revealing that 74.2% of women survived, while only 18.89% of men survived, forming a crucial part of exploratory data analysis (eda).', 'duration': 245.605, 'highlights': ['The percentage of women who survived is 74.2%, indicating a significantly higher survival rate among female passengers.', 'The percentage of men who survived is 18.89%, highlighting a notably lower survival rate among male passengers.']}], 'duration': 563.823, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY923417.jpg', 'highlights': ['Significantly higher survival rate among female passengers (74.2%)', 'Notably lower survival rate among male passengers (18.89%)', 'Encourages further learning about Pandas through its documentation']}, {'end': 1952.105, 'segs': [{'end': 1530.351, 'src': 'embed', 'start': 1509.026, 'weight': 3, 'content': [{'end': 1519.469, 'text': "It's called Random Forest, which is a really very good model, has been used in the industry and research for a long, long time and still being used.", 'start': 1509.026, 'duration': 10.443}, {'end': 1530.351, 'text': "So what is Random Forest? You don't have to worry if you don't know what Random Forest is on day one, you can again, Google it, Google Random Forest.", 'start': 1520.909, 'duration': 9.442}], 'summary': 'Random forest is a widely used and effective model in industry and research.', 'duration': 21.325, 'max_score': 1509.026, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY1509026.jpg'}, {'end': 1819.142, 'src': 'embed', 'start': 1788.706, 'weight': 4, 'content': [{'end': 1792.548, 'text': 'So I choose the features B class, sex, the best B part.', 'start': 1788.706, 'duration': 3.842}, {'end': 1808.955, 'text': 'And when I look at the, if I have to choose only these features, I can say train features, train underscore data and features.', 'start': 1794.929, 'duration': 14.026}, {'end': 1813.026, 'text': "Let's run this one first and then this one.", 'start': 1811.161, 'duration': 1.865}, {'end': 1819.142, 'text': "So this is the training data that I have, but only the features that I've mentioned here.", 'start': 1813.086, 'duration': 6.056}], 'summary': 'Selecting b class, sex, and the best b part as the training features.', 'duration': 30.436, 'max_score': 1788.706, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY1788706.jpg'}, {'end': 1957.408, 'src': 'embed', 'start': 1927.279, 'weight': 0, 'content': [{'end': 1929.721, 'text': "So don't worry much about parameters at the moment.", 'start': 1927.279, 'duration': 2.442}, {'end': 1933.303, 'text': 'Number of estimators is the number of trees that you have.', 'start': 1929.761, 'duration': 3.542}, {'end': 1935.104, 'text': 'Max depth is how deep the trees are.', 'start': 1933.363, 'duration': 1.741}, {'end': 1939.146, 'text': 'And random state is just some seed that you set.', 'start': 1935.624, 'duration': 3.522}, {'end': 1943.077, 'text': "If you don't set it your results will have randomness in it.", 'start': 1940.58, 'duration': 2.497}, {'end': 1947.822, 'text': "So instead of CLF, let's call it model.", 'start': 1945.421, 'duration': 2.401}, {'end': 1952.105, 'text': 'And then we fit the model on X and instead of Y we wrote target.', 'start': 1948.123, 'duration': 3.982}, {'end': 1954.186, 'text': 'So I will just change it to target here.', 'start': 1952.145, 'duration': 2.041}, {'end': 1957.408, 'text': 'And then we create predictions on the test set.', 'start': 1954.547, 'duration': 2.861}], 'summary': 'Explanation of parameters: estimators, depth, random state; renaming variables; making predictions.', 'duration': 30.129, 'max_score': 1927.279, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY1927279.jpg'}], 'start': 1487.4, 'title': 'Random forest model in ensembles', 'summary': 'Introduces the widely used random forest model, explaining its construction using decision trees and majority voting. it also covers creating an ensemble model using random forest classifier in scikit-learn, including feature selection, data conversion, and model parameters.', 'chapters': [{'end': 1677.904, 'start': 1487.4, 'title': 'Introduction to random forest model', 'summary': 'Introduces the random forest model, which is widely used in the industry and research, and explains its construction using decision trees and majority voting.', 'duration': 190.504, 'highlights': ['Random Forest is a widely used model in the industry and research and is constructed of several decision trees. Random Forest is a widely used model in the industry and research, constructed of several decision trees.', 'Decision trees are simple machine learning models that are rule-based and used in constructing the Random Forest model. Decision trees are simple machine learning models that are rule-based and used in constructing the Random Forest model.', 'Random Forest combines the predictions of multiple trees using majority voting to determine the final prediction. Random Forest combines the predictions of multiple trees using majority voting to determine the final prediction.']}, {'end': 1952.105, 'start': 1679.091, 'title': 'Ensemble models in machine learning', 'summary': 'Explains how to create an ensemble model using random forest classifier in scikit-learn, including selecting features, converting strings to numbers, and explaining the parameters for the model.', 'duration': 273.014, 'highlights': ['The process involves selecting four specific columns - P class, sex, SibSP, and parse - from the dataset for training the model.', "The transcript emphasizes the need to convert string data to numerical format using Pandas' getDummies function to create binary variables for training the model.", 'The chapter provides an explanation of the key parameters used in the random forest classifier, such as number of estimators, max depth, and random state, and the significance of each parameter in model training.']}], 'duration': 464.705, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY1487400.jpg', 'highlights': ['Random Forest is a widely used model in the industry and research, constructed of several decision trees.', 'Random Forest combines the predictions of multiple trees using majority voting to determine the final prediction.', 'The chapter provides an explanation of the key parameters used in the random forest classifier, such as number of estimators, max depth, and random state, and the significance of each parameter in model training.', 'The process involves selecting four specific columns - P class, sex, SibSP, and parse - from the dataset for training the model.', "The transcript emphasizes the need to convert string data to numerical format using Pandas' getDummies function to create binary variables for training the model.", 'Decision trees are simple machine learning models that are rule-based and used in constructing the Random Forest model.']}, {'end': 2178.505, 'segs': [{'end': 2002.632, 'src': 'embed', 'start': 1952.145, 'weight': 4, 'content': [{'end': 1954.186, 'text': 'So I will just change it to target here.', 'start': 1952.145, 'duration': 2.041}, {'end': 1957.408, 'text': 'And then we create predictions on the test set.', 'start': 1954.547, 'duration': 2.861}, {'end': 1959.029, 'text': 'So model.predict.', 'start': 1957.949, 'duration': 1.08}, {'end': 1961.131, 'text': "And let's see what happens here.", 'start': 1959.83, 'duration': 1.301}, {'end': 1962.151, 'text': "And it's done.", 'start': 1961.591, 'duration': 0.56}, {'end': 1963.372, 'text': "So it's quite fast.", 'start': 1962.391, 'duration': 0.981}, {'end': 1965.313, 'text': "As you can see, we don't have a lot of data.", 'start': 1963.452, 'duration': 1.861}, {'end': 1971.277, 'text': "And now we've got predictions, which is an array of binary values, zero or one.", 'start': 1966.054, 'duration': 5.223}, {'end': 1974.239, 'text': 'So now we have the predictions.', 'start': 1972.216, 'duration': 2.023}, {'end': 1978.965, 'text': 'So what we will do is we will create a data frame out of these predictions.', 'start': 1974.68, 'duration': 4.285}, {'end': 1985.019, 'text': 'So I will take the same IDs, so testdata.passengerId.', 'start': 1980.376, 'duration': 4.643}, {'end': 1987.161, 'text': 'So you have to keep the order same.', 'start': 1985.179, 'duration': 1.982}, {'end': 1995.066, 'text': "So you don't want to assign survive to someone whose probability of being diseased is higher.", 'start': 1987.301, 'duration': 7.765}, {'end': 2001.151, 'text': 'So you have to keep the order of the IDs the same as the predictions.', 'start': 1995.507, 'duration': 5.644}, {'end': 2002.632, 'text': 'So they must match.', 'start': 2001.851, 'duration': 0.781}], 'summary': 'Creating predictions on test set, generating binary values, and matching ids with predictions.', 'duration': 50.487, 'max_score': 1952.145, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY1952145.jpg'}, {'end': 2124.554, 'src': 'heatmap', 'start': 2015.199, 'weight': 0.857, 'content': [{'end': 2023.506, 'text': "We will just choose index equal to false because that's how Kaggle expects your submission CSV to look like.", 'start': 2015.199, 'duration': 8.307}, {'end': 2028.46, 'text': "Okay, let's save it.", 'start': 2025.939, 'duration': 2.521}, {'end': 2035.324, 'text': 'And now here, if I want to take a look at output, we can see you have the passenger ID and survived or not survived.', 'start': 2028.7, 'duration': 6.624}, {'end': 2044.568, 'text': "So when you're done with all the explorations and you see that everything works in a sequence,", 'start': 2035.944, 'duration': 8.624}, {'end': 2051.472, 'text': "you can click on save version and here just choose save and run all, and it's going to run everything.", 'start': 2044.568, 'duration': 6.904}, {'end': 2053.233, 'text': 'And now you wait.', 'start': 2052.032, 'duration': 1.201}, {'end': 2057.815, 'text': "So till we wait, we can just go and see what's happening here.", 'start': 2054.233, 'duration': 3.582}, {'end': 2066.318, 'text': 'So yeah, they tell you save and run all, and then we have the first submission, but we need to wait for that.', 'start': 2059.094, 'duration': 7.224}, {'end': 2069.44, 'text': 'So we will just wait for a few minutes or seconds.', 'start': 2066.358, 'duration': 3.082}, {'end': 2072.021, 'text': "So after a while, it's successful.", 'start': 2070.44, 'duration': 1.581}, {'end': 2079.024, 'text': 'So I can either click here and click on open in viewer.', 'start': 2073.241, 'duration': 5.783}, {'end': 2082.946, 'text': 'You have to remember that Kaggle provides you a certain quota for GPUs.', 'start': 2079.123, 'duration': 3.823}, {'end': 2085.809, 'text': "Since you're not using a GPU, it's fine.", 'start': 2084.206, 'duration': 1.603}, {'end': 2091.016, 'text': "But now since I'm done with the notebook, I can just click on this button and it will power off.", 'start': 2085.849, 'duration': 5.167}, {'end': 2093.761, 'text': 'So saving some resources.', 'start': 2092.639, 'duration': 1.122}, {'end': 2098.668, 'text': 'So here is my notebook that has been generated and I take a look.', 'start': 2094.462, 'duration': 4.206}, {'end': 2102.096, 'text': 'and okay, survived or not.', 'start': 2099.594, 'duration': 2.502}, {'end': 2104.638, 'text': 'And here I have this output.', 'start': 2102.757, 'duration': 1.881}, {'end': 2109.402, 'text': 'So you can just click on the output and then you have the submit button.', 'start': 2104.678, 'duration': 4.724}, {'end': 2112.785, 'text': "Since it's associated to a competition, you have the submit button.", 'start': 2110.223, 'duration': 2.562}, {'end': 2114.506, 'text': "If it's not, you don't have the submit button.", 'start': 2112.825, 'duration': 1.681}, {'end': 2124.554, 'text': 'I click on submit and now it will tell me, okay, select notebook, the version of the notebook and blah, blah, blah, whatever the description.', 'start': 2114.526, 'duration': 10.028}], 'summary': 'Demonstration of submitting a kaggle notebook with a csv output.', 'duration': 109.355, 'max_score': 2015.199, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY2015199.jpg'}, {'end': 2124.554, 'src': 'embed', 'start': 2094.462, 'weight': 0, 'content': [{'end': 2098.668, 'text': 'So here is my notebook that has been generated and I take a look.', 'start': 2094.462, 'duration': 4.206}, {'end': 2102.096, 'text': 'and okay, survived or not.', 'start': 2099.594, 'duration': 2.502}, {'end': 2104.638, 'text': 'And here I have this output.', 'start': 2102.757, 'duration': 1.881}, {'end': 2109.402, 'text': 'So you can just click on the output and then you have the submit button.', 'start': 2104.678, 'duration': 4.724}, {'end': 2112.785, 'text': "Since it's associated to a competition, you have the submit button.", 'start': 2110.223, 'duration': 2.562}, {'end': 2114.506, 'text': "If it's not, you don't have the submit button.", 'start': 2112.825, 'duration': 1.681}, {'end': 2124.554, 'text': 'I click on submit and now it will tell me, okay, select notebook, the version of the notebook and blah, blah, blah, whatever the description.', 'start': 2114.526, 'duration': 10.028}], 'summary': 'Notebook generation with associated competition. submitting requires specific conditions and steps.', 'duration': 30.092, 'max_score': 2094.462, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY2094462.jpg'}], 'start': 1952.145, 'title': 'Predictions and kaggle submission', 'summary': "Demonstrates creating predictions, saving to csv with emphasis on maintaining order, generating binary values, and saving as per kaggle's requirements. it also explains kaggle competition submission process, making a submission, and analyzing the resulting score, demonstrating achieving an accuracy of 77.511% in the titanic competition.", 'chapters': [{'end': 2035.324, 'start': 1952.145, 'title': 'Creating predictions and saving output to csv', 'summary': "Demonstrates how to create predictions using a model and save the output to a csv file, with an emphasis on maintaining the order of ids for proper matching, generating an array of binary values, and saving the file as per kaggle's requirements.", 'duration': 83.179, 'highlights': ['Creating predictions using a model and saving the output to a CSV file is demonstrated, ensuring the order of IDs matches the predictions for accurate assignment (e.g., testdata.passengerId).', 'The process involves generating an array of binary values, indicating survival outcomes (0 or 1) for each passenger in the test set.', "The demonstration emphasizes the importance of maintaining the order of IDs to prevent misassignment of survival outcomes based on the probability of being diseased, ensuring alignment with Kaggle's submission CSV requirements."]}, {'end': 2178.505, 'start': 2035.944, 'title': 'Kaggle competition submission process', 'summary': 'Explains the process of saving and running a notebook on kaggle, making a submission to a competition, and analyzing the resulting score, with a demonstration of submitting a notebook to the titanic competition and achieving an accuracy of 77.511%.', 'duration': 142.561, 'highlights': ['The process of saving and running a notebook on Kaggle, making a submission to a competition, and analyzing the resulting score is explained, with a demonstration of submitting a notebook to the Titanic competition and achieving an accuracy of 77.511%.', 'The submission process includes selecting the notebook version, providing a description, and clicking on the submit button, resulting in a score of 77.511, equivalent to an accuracy of 0.775.', 'The speaker also mentions the possibility of improving the score by increasing the number of trees and the depth in the model.']}], 'duration': 226.36, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY1952145.jpg', 'highlights': ['Creating predictions using a model and saving the output to a CSV file is demonstrated, ensuring the order of IDs matches the predictions for accurate assignment (e.g., testdata.passengerId).', 'The process involves generating an array of binary values, indicating survival outcomes (0 or 1) for each passenger in the test set.', "The demonstration emphasizes the importance of maintaining the order of IDs to prevent misassignment of survival outcomes based on the probability of being diseased, ensuring alignment with Kaggle's submission CSV requirements.", 'The process of saving and running a notebook on Kaggle, making a submission to a competition, and analyzing the resulting score is explained, with a demonstration of submitting a notebook to the Titanic competition and achieving an accuracy of 77.511%.', 'The submission process includes selecting the notebook version, providing a description, and clicking on the submit button, resulting in a score of 77.511, equivalent to an accuracy of 0.775.', 'The speaker also mentions the possibility of improving the score by increasing the number of trees and the depth in the model.']}, {'end': 2620.142, 'segs': [{'end': 2353.098, 'src': 'embed', 'start': 2322.055, 'weight': 1, 'content': [{'end': 2324.597, 'text': 'You can check that and here it says version two of two.', 'start': 2322.055, 'duration': 2.542}, {'end': 2331.663, 'text': 'So now I have version two and click on submit and then I can see my submission again.', 'start': 2325.598, 'duration': 6.065}, {'end': 2337.905, 'text': 'So you see, despite of adding new feature, I did not improve.', 'start': 2333.922, 'duration': 3.983}, {'end': 2341.789, 'text': "So adding new feature doesn't always mean it will improve.", 'start': 2338.526, 'duration': 3.263}, {'end': 2346.152, 'text': 'Maybe I have to optimize certain hyperparameters to improve it further.', 'start': 2342.069, 'duration': 4.083}, {'end': 2349.495, 'text': 'So in the first iteration, my score was 775.', 'start': 2346.933, 'duration': 2.562}, {'end': 2353.098, 'text': 'Now I have 0.75.', 'start': 2349.495, 'duration': 3.603}], 'summary': 'Despite adding a new feature, the score remained at 0.75, showing no improvement.', 'duration': 31.043, 'max_score': 2322.055, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY2322055.jpg'}, {'end': 2576.264, 'src': 'embed', 'start': 2549.834, 'weight': 2, 'content': [{'end': 2555.738, 'text': "Or you can respond to someone else's comment if you want, and that's called writing a comment.", 'start': 2549.834, 'duration': 5.904}, {'end': 2560.909, 'text': 'So I have shown you already how to give one upvote.', 'start': 2557.526, 'duration': 3.383}, {'end': 2569.237, 'text': 'So you have upvoted a notebook, but you can also, if you want, you can also go and upvote some discussions if you want.', 'start': 2561.31, 'duration': 7.927}, {'end': 2574.402, 'text': 'So you have the both options, upvote and downvote for discussions.', 'start': 2569.998, 'duration': 4.404}, {'end': 2576.264, 'text': "For notebooks, it's only upvote.", 'start': 2574.883, 'duration': 1.381}], 'summary': 'Learn to upvote notebooks and discussions, with downvote option for discussions.', 'duration': 26.43, 'max_score': 2549.834, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY2549834.jpg'}, {'end': 2620.142, 'src': 'embed', 'start': 2605.605, 'weight': 0, 'content': [{'end': 2607.808, 'text': "So what's next? We will see tomorrow.", 'start': 2605.605, 'duration': 2.203}, {'end': 2610.682, 'text': "So that's it for day one.", 'start': 2609.039, 'duration': 1.643}, {'end': 2613.027, 'text': 'I will see you tomorrow on day two.', 'start': 2611.163, 'duration': 1.864}, {'end': 2618.639, 'text': "And if you like this video, don't forget to click on the like button and do subscribe to the channel and do tell your friends about it.", 'start': 2613.408, 'duration': 5.231}, {'end': 2619.641, 'text': 'See you.', 'start': 2619.401, 'duration': 0.24}, {'end': 2620.142, 'text': 'Bye bye.', 'start': 2619.822, 'duration': 0.32}], 'summary': 'Day one ends, day two tomorrow. like and subscribe to the channel.', 'duration': 14.537, 'max_score': 2605.605, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY2605605.jpg'}], 'start': 2179.046, 'title': 'Kaggle notebook optimization', 'summary': 'Highlights the process of optimizing a kaggle notebook, addressing overfitting, experimenting with new features, handling missing values, and improving through hyperparameter optimization and feature engineering. it also stresses learning from and engaging with the kaggle community.', 'chapters': [{'end': 2620.142, 'start': 2179.046, 'title': 'Kaggle notebook optimization', 'summary': 'Highlights the process of optimizing a kaggle notebook, including avoiding overfitting, experimenting with new features, handling missing values, and seeking improvement through hyperparameter optimization and feature engineering. the chapter also emphasizes the importance of learning from and engaging with the kaggle community.', 'duration': 441.096, 'highlights': ['Avoid overfitting by not overly optimizing parameters, as it may lead to poor performance on test data. Overfitting occurs when the model learns the training data perfectly but performs poorly on unseen test data. It is essential to avoid over-optimizing parameters to prevent this.', "Experiment with new features and hyperparameters to seek improvements in the model's performance. Adding new features does not always guarantee improvement, and it may be necessary to optimize certain hyperparameters to enhance model performance.", 'Handle missing values by filling with appropriate substitutes, such as zero for non-advisable values like age, and continue iterating to improve the model. Addressing missing values by filling them with suitable substitutes, such as zero or non-advisable values like age with -1, allows for continued iteration and improvement of the model.', "Engage with the Kaggle community by learning from and interacting with shared notebooks and discussions, as well as contributing to the community through upvoting, commenting, and seeking help. Participating in the Kaggle community involves learning from shared notebooks and discussions, contributing through upvoting, commenting, and seeking help or providing answers to others' inquiries."]}], 'duration': 441.096, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_55G24aghPY/pics/_55G24aghPY2179046.jpg', 'highlights': ["Experiment with new features and hyperparameters to seek improvements in the model's performance. Adding new features does not always guarantee improvement, and it may be necessary to optimize certain hyperparameters to enhance model performance.", "Engage with the Kaggle community by learning from and interacting with shared notebooks and discussions, as well as contributing to the community through upvoting, commenting, and seeking help. Participating in the Kaggle community involves learning from shared notebooks and discussions, contributing through upvoting, commenting, and seeking help or providing answers to others' inquiries.", 'Handle missing values by filling with appropriate substitutes, such as zero for non-advisable values like age, and continue iterating to improve the model. Addressing missing values by filling them with suitable substitutes, such as zero or non-advisable values like age with -1, allows for continued iteration and improvement of the model.', 'Avoid overfitting by not overly optimizing parameters, as it may lead to poor performance on test data. Overfitting occurs when the model learns the training data perfectly but performs poorly on unseen test data. It is essential to avoid over-optimizing parameters to prevent this.']}], 'highlights': ['The 30 days of ml challenge on kaggle guides participants to progress from beginners to competitors in 30 days, covering python and pandas for data analysis, constructing a random forest model, and achieving 77.511% accuracy in the titanic competition, emphasizing optimization and community engagement.', 'Joining the machine learning Discord community allows for instant problem-solving with thousands of members.', 'Setting up a Kaggle account provides access to competitions, datasets, notebooks, and discussions, with progression from novice to grandmaster levels in each category.', 'Participants in the 30 days of ML challenge learn something new each day for the first 15 days and work on a Kaggle competition for the next 15 days.', 'The progression from novice to contributor level on Kaggle involves steps such as profile overview, understanding progression and performance styles, and submitting to a competition.', 'The process of joining the Kaggle Titanic competition and understanding the rules is explained, with emphasis on the popularity of the competition for beginners.', 'The task of predicting who survives the Titanic using machine learning and the provided passenger data is outlined, with a focus on using the machine learning model to create predictions.', 'The evaluation of the model is based on accuracy, with a clear explanation of how the predictions should be formatted and the simplicity of the evaluation metric.', "Details about the dataset, including the columns in the training set and the absence of the 'survived' column in the test set, are provided, with a brief explanation of the gender submission file's purpose.", 'Random Forest is a widely used model in the industry and research, constructed of several decision trees.', 'Random Forest combines the predictions of multiple trees using majority voting to determine the final prediction.', 'The chapter provides an explanation of the key parameters used in the random forest classifier, such as number of estimators, max depth, and random state, and the significance of each parameter in model training.', 'The process involves selecting four specific columns - P class, sex, SibSP, and parse - from the dataset for training the model.', 'Decision trees are simple machine learning models that are rule-based and used in constructing the Random Forest model.', 'Creating predictions using a model and saving the output to a CSV file is demonstrated, ensuring the order of IDs matches the predictions for accurate assignment (e.g., testdata.passengerId).', 'The process involves generating an array of binary values, indicating survival outcomes (0 or 1) for each passenger in the test set.', "The demonstration emphasizes the importance of maintaining the order of IDs to prevent misassignment of survival outcomes based on the probability of being diseased, ensuring alignment with Kaggle's submission CSV requirements.", 'The process of saving and running a notebook on Kaggle, making a submission to a competition, and analyzing the resulting score is explained, with a demonstration of submitting a notebook to the Titanic competition and achieving an accuracy of 77.511%.', 'The speaker also mentions the possibility of improving the score by increasing the number of trees and the depth in the model.', "Experiment with new features and hyperparameters to seek improvements in the model's performance. Adding new features does not always guarantee improvement, and it may be necessary to optimize certain hyperparameters to enhance model performance.", "Engage with the Kaggle community by learning from and interacting with shared notebooks and discussions, as well as contributing to the community through upvoting, commenting, and seeking help. Participating in the Kaggle community involves learning from shared notebooks and discussions, contributing through upvoting, commenting, and seeking help or providing answers to others' inquiries.", 'Handle missing values by filling with appropriate substitutes, such as zero for non-advisable values like age, and continue iterating to improve the model. Addressing missing values by filling them with suitable substitutes, such as zero or non-advisable values like age with -1, allows for continued iteration and improvement of the model.', 'Avoid overfitting by not overly optimizing parameters, as it may lead to poor performance on test data. Overfitting occurs when the model learns the training data perfectly but performs poorly on unseen test data. It is essential to avoid over-optimizing parameters to prevent this.']}