title
Tutorial 11-Exploratory Data Analysis(EDA) of Titanic dataset

description
Here is the detailed explanation of Exploratory Data Analysis of the Titanic. Finally we are applying Logistic Regression for the prediction of the survived column. Github url: https://github.com/krishnaik06/EDA1 References from : Jose Portila EDA Materials And Kaggle ⭐ Kite is a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. I've been using Kite for a few months and I love it! https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=krishnaik&utm_content=description-only Stats playlist : https://www.youtube.com/watch?v=GGZfVeZs_v4&list=PLZoTAELRMXVMhVyr3Ri9IQ-t5QPBtxzJO You can buy my book where I have provided a detailed explanation of how we can use Machine Learning, Deep Learning in Finance using python Packt url : https://prod.packtpub.com/in/big-data-and-business-intelligence/hands-python-finance Amazon url: https://www.amazon.com/Hands-Python-Finance-implementing-strategies-ebook/dp/B07Q5W7GB1/ref=sr_1_1?keywords=Krish+naik&qid=1554285070&s=gateway&sr=8-1-spell

detail
{'title': 'Tutorial 11-Exploratory Data Analysis(EDA) of Titanic dataset', 'heatmap': [{'end': 1163.303, 'start': 1122.186, 'weight': 1}, {'end': 1277.02, 'start': 1237.57, 'weight': 0.765}, {'end': 1357.867, 'start': 1331.4, 'weight': 0.717}, {'end': 1754.713, 'start': 1538.735, 'weight': 0.783}], 'summary': 'Tutorial on exploratory data analysis (eda) of the titanic dataset in python emphasizes the significance of eda in the machine learning lifecycle, covers data preprocessing and analysis using the titanic dataset in jupyter notebook, explores survival prediction, passenger class, null value removal, and logistic regression model with 71.9% accuracy, highlighting potential improvements using more complex algorithms like xgboost to reach 87-88% accuracy.', 'chapters': [{'end': 55.023, 'segs': [{'end': 55.023, 'src': 'embed', 'start': 18.271, 'weight': 0, 'content': [{'end': 24.532, 'text': 'most of the percentage of your time span goes on analyzing your data and actually exploring the data.', 'start': 18.271, 'duration': 6.261}, {'end': 27.792, 'text': 'so it is basically called as exploratory data analysis.', 'start': 24.532, 'duration': 3.26}, {'end': 30.233, 'text': 'that basically means that if you have a data set,', 'start': 27.792, 'duration': 2.441}, {'end': 36.14, 'text': 'you have to be a detective where you need to find out more and more information from that particular data.', 'start': 30.819, 'duration': 5.321}, {'end': 45.422, 'text': 'So this topic is basically who is actually interested to know more about data pre-processing and to apply different kind of logics.', 'start': 37.1, 'duration': 8.322}, {'end': 50.082, 'text': "And over here we'll be using libraries like Pandas and NumPy.", 'start': 45.622, 'duration': 4.46}, {'end': 55.023, 'text': "Very simple function in Pandas we'll be seeing over here and we'll try to do the exploratory data analysis.", 'start': 50.542, 'duration': 4.481}], 'summary': 'Exploratory data analysis involves analyzing and exploring data, using pandas and numpy libraries.', 'duration': 36.752, 'max_score': 18.271, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs18271.jpg'}], 'start': 0.967, 'title': 'Exploratory data analysis in python', 'summary': 'Discusses the significance of exploratory data analysis in the machine learning lifecycle, emphasizing the need to spend a significant amount of time analyzing and exploring data, and highlights the use of pandas and numpy for data pre-processing and exploration.', 'chapters': [{'end': 55.023, 'start': 0.967, 'title': 'Exploratory data analysis in python', 'summary': 'Discusses the significance of exploratory data analysis in the machine learning lifecycle, emphasizing the need to spend a significant amount of time analyzing and exploring data, and highlights the use of pandas and numpy for data pre-processing and exploration.', 'duration': 54.056, 'highlights': ['The significance of exploratory data analysis in the machine learning lifecycle is emphasized, where a significant amount of time is spent analyzing and exploring data.', 'The need to be a detective when conducting exploratory data analysis is highlighted, emphasizing the importance of extracting as much information as possible from a given dataset.', 'The use of Pandas and NumPy libraries for data pre-processing and exploration is mentioned, indicating the specific tools and technologies utilized for the analysis.']}], 'duration': 54.056, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs967.jpg', 'highlights': ['The significance of exploratory data analysis in the machine learning lifecycle is emphasized, where a significant amount of time is spent analyzing and exploring data.', 'The need to be a detective when conducting exploratory data analysis is highlighted, emphasizing the importance of extracting as much information as possible from a given dataset.', 'The use of Pandas and NumPy libraries for data pre-processing and exploration is mentioned, indicating the specific tools and technologies utilized for the analysis.']}, {'end': 240.606, 'segs': [{'end': 127.015, 'src': 'embed', 'start': 96.843, 'weight': 0, 'content': [{'end': 103.146, 'text': "First we'll discuss about the dataset and then, when we are doing a lot of pre-processing steps, we'll try to see that.", 'start': 96.843, 'duration': 6.303}, {'end': 111.75, 'text': 'what are the pre-processing steps that we usually do on a particular dataset before we give that particular input to our model so that our model will be able to do the prediction?', 'start': 103.146, 'duration': 8.604}, {'end': 118.773, 'text': "It is very important to know that if we don't do this data analysis properly, definitely our model will not give a very good accuracy.", 'start': 112.33, 'duration': 6.443}, {'end': 127.015, 'text': 'Let us go ahead and start seeing this particular data set and let us see that what of data processing steps will come up with.', 'start': 120.049, 'duration': 6.966}], 'summary': 'Data preprocessing is crucial for model accuracy; understanding dataset and processing steps is key.', 'duration': 30.172, 'max_score': 96.843, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs96843.jpg'}, {'end': 223.667, 'src': 'embed', 'start': 154.474, 'weight': 1, 'content': [{'end': 157.216, 'text': "So that is the reason why I'm trying to implement this in Jupyter Notebook.", 'start': 154.474, 'duration': 2.742}, {'end': 160.118, 'text': 'So I hope everybody knows what Pandas library does.', 'start': 157.696, 'duration': 2.422}, {'end': 167.042, 'text': 'It helps you to read the dataset and most of the data pre-processing steps will be done by the inbuilt function that is present inside Pandas.', 'start': 160.218, 'duration': 6.824}, {'end': 170.545, 'text': 'Then, NumPy is basically used to work with arrays.', 'start': 167.603, 'duration': 2.942}, {'end': 173.867, 'text': 'It may be a multi-dimensional array or a single-dimensional array.', 'start': 170.845, 'duration': 3.022}, {'end': 177.165, 'text': 'Matplotlib, it will be used for visualization.', 'start': 175.103, 'duration': 2.062}, {'end': 179.646, 'text': 'Seaborn will also be used for visualization.', 'start': 177.205, 'duration': 2.441}, {'end': 186.591, 'text': 'And Seaborn will also consist of various statistical concepts, statistical functions, which will help you to visualize the data properly.', 'start': 180.027, 'duration': 6.564}, {'end': 193.636, 'text': 'So to begin with, I have downloaded the dataset and the dataset looks something like this, titanic-train.csv.', 'start': 187.132, 'duration': 6.504}, {'end': 197.939, 'text': "So I'll try to read the dataset by using Pandas.", 'start': 194.337, 'duration': 3.602}, {'end': 202.563, 'text': "So I'll write pd.read-csv and I'm going to read the titanic-train.csv.", 'start': 198.119, 'duration': 4.444}, {'end': 206.714, 'text': 'Now let me just discuss about some of the columns that are present inside this.', 'start': 203.772, 'duration': 2.942}, {'end': 215.941, 'text': 'So when I see the head part of this particular dataset, I have different information like passenger ID, survived, passenger class, name, sex, age.', 'start': 207.175, 'duration': 8.766}, {'end': 219.464, 'text': 'This sib is basically called a sibling spouse.', 'start': 216.662, 'duration': 2.802}, {'end': 223.667, 'text': 'And this is PR is basically called as parent child.', 'start': 220.785, 'duration': 2.882}], 'summary': 'Implementing data analysis in jupyter using pandas, numpy, matplotlib, and seaborn for visualizing and processing dataset titanic-train.csv.', 'duration': 69.193, 'max_score': 154.474, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs154474.jpg'}], 'start': 56.083, 'title': 'Data analysis and implementation in jupyter notebook using titanic dataset', 'summary': "Covers the importance of data preprocessing and analysis using the titanic dataset, emphasizing the impact on model accuracy and the necessary libraries for visualization. it also discusses implementing data analysis in jupyter notebook using pandas for dataset reading and preprocessing, numpy for working with arrays, and matplotlib and seaborn for visualization, with the dataset 'titanic-train.csv' being utilized for demonstration. additionally, it demonstrates reading the 'titanic-train.csv' dataset using pd.read-csv, highlighting the columns and their descriptions.", 'chapters': [{'end': 153.273, 'start': 56.083, 'title': 'Data analysis: titanic dataset', 'summary': 'Covers the importance of data preprocessing and analysis using the titanic dataset, emphasizing the impact on model accuracy and the necessary libraries for visualization.', 'duration': 97.19, 'highlights': ['Importance of data preprocessing for model accuracy', 'Emphasizing the significance of visualization using Seaborn for data analysis', 'Informing about the availability of the Titanic dataset on Kaggle and GitHub']}, {'end': 197.939, 'start': 154.474, 'title': 'Implementing data analysis in jupyter notebook', 'summary': "Discusses implementing data analysis in jupyter notebook using pandas for dataset reading and preprocessing, numpy for working with arrays, and matplotlib and seaborn for visualization, with the dataset 'titanic-train.csv' being utilized for demonstration.", 'duration': 43.465, 'highlights': ['Pandas library helps in reading the dataset and performing data pre-processing steps.', 'NumPy is used for working with arrays, including multi-dimensional and single-dimensional arrays.', 'Matplotlib and Seaborn are utilized for visualization, with Seaborn also offering statistical concepts and functions for better data visualization.']}, {'end': 240.606, 'start': 198.119, 'title': 'Reading titanic train data', 'summary': "Demonstrates reading the 'titanic-train.csv' dataset using pd.read-csv, highlighting the columns and their descriptions including passenger id, survived, passenger class, name, sex, age, sibling spouse count, parent child count, ticket information, fare, cabin, and embark.", 'duration': 42.487, 'highlights': ['The dataset includes columns such as passenger ID, survived, passenger class, name, sex, age, sibling spouse count, parent child count, ticket information, fare, cabin, and embark.', "The 'sib' column represents the count of siblings and spouses, the 'PR' column represents the count of parents and children, and the 'ticket' column provides ticket information."]}], 'duration': 184.523, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs56083.jpg', 'highlights': ['Importance of data preprocessing for model accuracy', 'Emphasizing the significance of visualization using Seaborn for data analysis', 'Pandas library helps in reading the dataset and performing data pre-processing steps', 'NumPy is used for working with arrays, including multi-dimensional and single-dimensional arrays', 'Matplotlib and Seaborn are utilized for visualization, with Seaborn also offering statistical concepts and functions for better data visualization', 'The dataset includes columns such as passenger ID, survived, passenger class, name, sex, age, sibling spouse count, parent child count, ticket information, fare, cabin, and embark']}, {'end': 771.73, 'segs': [{'end': 270.623, 'src': 'embed', 'start': 241.146, 'weight': 0, 'content': [{'end': 248.109, 'text': 'So this is the basic information about this data set, and the main aim of this data set is that we need to predict,', 'start': 241.146, 'duration': 6.963}, {'end': 252.933, 'text': 'based on this information that we have, whether the passenger has survived or not.', 'start': 248.109, 'duration': 4.824}, {'end': 255.215, 'text': 'So that is the main problem statement behind it.', 'start': 253.334, 'duration': 1.881}, {'end': 260.497, 'text': 'I hope everybody is familiar with Titanic movies, right? Over there, we have this particular information.', 'start': 255.755, 'duration': 4.742}, {'end': 262.919, 'text': 'Now we need to predict whether the passenger has survived or not.', 'start': 260.538, 'duration': 2.381}, {'end': 270.623, 'text': 'So, always to begin with your data pre-processing technique, first of all, you need to find out how many NAND values are present.', 'start': 263.679, 'duration': 6.944}], 'summary': 'The goal is to predict passenger survival based on a dataset, with emphasis on identifying and handling nan values.', 'duration': 29.477, 'max_score': 241.146, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs241146.jpg'}, {'end': 377.71, 'src': 'embed', 'start': 348.711, 'weight': 1, 'content': [{'end': 351.292, 'text': 'And Jupyter Notebooks also does not give the whole information.', 'start': 348.711, 'duration': 2.581}, {'end': 353.033, 'text': 'It skips some of the rows away.', 'start': 351.492, 'duration': 1.541}, {'end': 353.853, 'text': 'You can see this.', 'start': 353.393, 'duration': 0.46}, {'end': 359.485, 'text': "So, in order to do that, I'll be using a visualization concept which is introduced in Seaborn,", 'start': 354.383, 'duration': 5.102}, {'end': 362.725, 'text': "which is through a function which I'll prove to explain you in a while.", 'start': 359.485, 'duration': 3.24}, {'end': 367.247, 'text': "With that, we'll be able to see that how many null values are there and how many null values are not there.", 'start': 363.146, 'duration': 4.101}, {'end': 373.069, 'text': "So to do it, I'll be using a Seaborn library.", 'start': 367.827, 'duration': 5.242}, {'end': 375.629, 'text': 'So Seaborn, I have actually imported as SNF.', 'start': 373.229, 'duration': 2.4}, {'end': 377.71, 'text': 'So there is a heat map concept.', 'start': 376.069, 'duration': 1.641}], 'summary': 'Using seaborn to visualize null values in data, aiming to address missing information in jupyter notebooks.', 'duration': 28.999, 'max_score': 348.711, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs348711.jpg'}, {'end': 567.82, 'src': 'embed', 'start': 539.434, 'weight': 4, 'content': [{'end': 541.657, 'text': "And again, I'm going to use some of the statistics concepts.", 'start': 539.434, 'duration': 2.223}, {'end': 545.781, 'text': "And based on that statistics concept, I'm again going to visualize by using CBOT.", 'start': 542.197, 'duration': 3.584}, {'end': 551.756, 'text': 'So from here, we can make some observation that roughly 20% of age data is missing.', 'start': 546.855, 'duration': 4.901}, {'end': 558.958, 'text': 'Okay Just randomly, if you just see the proportion of the proportion of age missing is likely small enough for reasonable replacement.', 'start': 552.136, 'duration': 6.822}, {'end': 561.498, 'text': 'So you can read this all the kind of observation.', 'start': 559.218, 'duration': 2.28}, {'end': 567.82, 'text': "Okay Now let's see how, what all information can I do? Now, again, I will be using Seaborn.", 'start': 561.759, 'duration': 6.061}], 'summary': 'Roughly 20% of age data is missing, and seaborn will be used for visualization.', 'duration': 28.386, 'max_score': 539.434, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs539434.jpg'}, {'end': 766.246, 'src': 'embed', 'start': 731.599, 'weight': 3, 'content': [{'end': 734.461, 'text': 'So most of the males have not survived.', 'start': 731.599, 'duration': 2.862}, {'end': 736.402, 'text': 'you know when the value was actually 0..', 'start': 734.461, 'duration': 1.941}, {'end': 742.587, 'text': 'Whereas, less women, you know, less women died basically when I just say the 0 value and when I see this distribution.', 'start': 736.402, 'duration': 6.185}, {'end': 747.971, 'text': 'Now, similarly, in the case of survived value as 1, you can see that my male is, again, less.', 'start': 743.127, 'duration': 4.844}, {'end': 749.632, 'text': 'It is somewhere more than 100.', 'start': 748.011, 'duration': 1.621}, {'end': 754.275, 'text': "More than 100 male only survived in this considering, I'm considering only this 891 data set.", 'start': 749.632, 'duration': 4.643}, {'end': 756.137, 'text': "You won't get confused with that.", 'start': 754.315, 'duration': 1.822}, {'end': 766.246, 'text': 'Then, again you can see this over here, the female value is more than 200, that basically means that more than 200 women survived, female survived.', 'start': 756.798, 'duration': 9.448}], 'summary': 'Most males did not survive, while over 100 males and over 200 females survived.', 'duration': 34.647, 'max_score': 731.599, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs731599.jpg'}], 'start': 241.146, 'title': 'Predicting passenger survival on titanic', 'summary': 'Discusses the aim of predicting passenger survival on the titanic based on a dataset, emphasizing the need for data pre-processing, visualization techniques using seaborn library, and analysis of survival rates based on sex and survival status.', 'chapters': [{'end': 388.755, 'start': 241.146, 'title': 'Predicting passenger survival on titanic', 'summary': 'Discusses the aim of predicting passenger survival on the titanic based on a dataset, emphasizing the need for data pre-processing by identifying null values and introducing visualization techniques using seaborn library for efficient analysis.', 'duration': 147.609, 'highlights': ['The main aim is to predict whether the passenger has survived or not based on the available information from the dataset. The primary goal of the data analysis is to predict passenger survival based on the dataset.', 'Introduction to identifying null values in the dataset using the isNull function in Pandas and the visualization technique through Seaborn library. Explanation of using the isNull function in Pandas to identify null values and the introduction of visualization techniques using Seaborn library for efficient analysis.']}, {'end': 771.73, 'start': 388.755, 'title': 'Data visualization and analysis', 'summary': 'Discusses data visualization, handling of null values, and analysis of survival rates in a dataset, with key points including visualization techniques, null value handling, and analysis of survival rates based on sex and survival status.', 'duration': 382.975, 'highlights': ['The chapter discusses techniques for visualizing data, including the use of Seaborn for creating visualizations and heatmaps to identify columns with null values.', 'The chapter demonstrates the analysis of survival rates based on gender, revealing that a higher proportion of males did not survive compared to females.', 'The chapter explores the handling of null values in the dataset, utilizing statistical concepts and visualization techniques to identify and address missing data, with an observation that roughly 20% of age data is missing.']}], 'duration': 530.584, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs241146.jpg', 'highlights': ['The main aim is to predict whether the passenger has survived or not based on the available information from the dataset.', 'Introduction to identifying null values in the dataset using the isNull function in Pandas and the visualization technique through Seaborn library.', 'The chapter discusses techniques for visualizing data, including the use of Seaborn for creating visualizations and heatmaps to identify columns with null values.', 'The chapter demonstrates the analysis of survival rates based on gender, revealing that a higher proportion of males did not survive compared to females.', 'The chapter explores the handling of null values in the dataset, utilizing statistical concepts and visualization techniques to identify and address missing data, with an observation that roughly 20% of age data is missing.']}, {'end': 1118.924, 'segs': [{'end': 906.334, 'src': 'embed', 'start': 878.304, 'weight': 0, 'content': [{'end': 883.725, 'text': 'Now, similarly, in case of the person who had survived, you can see the passenger class one, many of them had survived.', 'start': 878.304, 'duration': 5.421}, {'end': 888.165, 'text': 'Whereas from passenger class two, they were like minimal only.', 'start': 885.043, 'duration': 3.122}, {'end': 892.067, 'text': 'Whereas passenger class three, they were very less number of people who had actually survived.', 'start': 888.505, 'duration': 3.562}, {'end': 894.628, 'text': 'So again, we are trying to get more and more data.', 'start': 892.487, 'duration': 2.141}, {'end': 896.029, 'text': "You're acting like a detective, right?", 'start': 894.668, 'duration': 1.361}, {'end': 901.952, 'text': "You're trying to just see a data and you're just plotting some beautiful diagrams by using some statistic concepts,", 'start': 896.429, 'duration': 5.523}, {'end': 906.334, 'text': 'and libraries like CBON are getting more and more, more and more information.', 'start': 901.952, 'duration': 4.382}], 'summary': 'Survival rates varied by passenger class. class 1 had most survivors, class 2 had minimal, and class 3 had very few.', 'duration': 28.03, 'max_score': 878.304, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs878304.jpg'}, {'end': 1017.741, 'src': 'embed', 'start': 992.257, 'weight': 1, 'content': [{'end': 997.9, 'text': 'if i consider the range between 5 to 10, they were around, you know, uh, 10, 10 number of people.', 'start': 992.257, 'duration': 5.643}, {'end': 1002.222, 'text': 'similarly, you can see that the average age that was between 20 to 30.', 'start': 997.9, 'duration': 4.322}, {'end': 1005.894, 'text': 'i think it is somewhere around you know, 17 to 30 were maximum.', 'start': 1002.222, 'duration': 3.672}, {'end': 1009.697, 'text': 'You can see that maximum number of people were in this particular age range.', 'start': 1006.034, 'duration': 3.663}, {'end': 1012.76, 'text': 'That is between 17 to 30.', 'start': 1009.997, 'duration': 2.763}, {'end': 1017.741, 'text': 'Then still there were very less number of people who were elder age like 70, 80, 60.', 'start': 1012.76, 'duration': 4.981}], 'summary': 'Around 10 people in the 5-10 range, average age 20-30 with maximum in 17-30.', 'duration': 25.484, 'max_score': 992.257, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs992257.jpg'}, {'end': 1106.518, 'src': 'embed', 'start': 1077.107, 'weight': 3, 'content': [{'end': 1087.146, 'text': 'so here again, this will give me the frequency or the count of the people who either did not have any sibling or spouse who had one sibling,', 'start': 1077.107, 'duration': 10.039}, {'end': 1092.03, 'text': 'or spouse who had two siblings, or spouse who had three siblings or spouse.', 'start': 1087.146, 'duration': 4.884}, {'end': 1095.613, 'text': 'And you can see that maximum people did not have any sibling or spouse.', 'start': 1092.07, 'duration': 3.543}, {'end': 1096.894, 'text': 'They were around 600 people.', 'start': 1095.733, 'duration': 1.161}, {'end': 1106.518, 'text': 'Whereas who had this one is basically with respect to spouse, because no one will know travel without spouse, like with only children.', 'start': 1097.315, 'duration': 9.203}], 'summary': 'Around 600 people did not have any sibling or spouse, while the rest had one or more siblings or spouse.', 'duration': 29.411, 'max_score': 1077.107, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs1077107.jpg'}], 'start': 772.09, 'title': 'Titanic survival analysis and data visualization', 'summary': 'Discusses survival rates by passenger class, indicating that a higher percentage of first-class passengers survived, and explores age distribution and sibling plus spouse count plot, revealing that the majority of passengers were aged between 17 to 30, and around 600 people did not have any sibling or spouse.', 'chapters': [{'end': 896.029, 'start': 772.09, 'title': 'Titanic survival analysis by passenger class', 'summary': 'Explores the survival rates of titanic passengers based on their passenger class, revealing that a higher percentage of first-class passengers survived compared to those in second and third class, indicating a correlation between passenger class and survival.', 'duration': 123.939, 'highlights': ['Passenger class one exhibited the highest survival rate, with a majority of passengers in this class surviving.', 'There was a lower survival rate for passengers in second class, with minimal survivors.', 'Passengers in third class had the lowest survival rate, with a very small percentage of passengers surviving.']}, {'end': 1118.924, 'start': 896.429, 'title': 'Data visualization and distribution analysis', 'summary': 'Explores the distribution of age and count plot of sibling plus spouse, revealing that the majority of people on the titanic were aged between 17 to 30, and most passengers did not have any sibling or spouse, with around 600 people in that category.', 'duration': 222.495, 'highlights': ['The majority of people on the Titanic were aged between 17 to 30, with around 17 to 30 being the age range with the maximum number of passengers.', 'The count plot of sibling plus spouse revealed that around 600 people did not have any sibling or spouse, indicating a high frequency of passengers in that category.', 'The histogram and kde plot of age distribution showed the count of people within different age ranges, such as 0 to 2, 5 to 10, and 20 to 30, providing insights into the age demographics of the passengers.']}], 'duration': 346.834, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs772090.jpg', 'highlights': ['Passenger class one exhibited the highest survival rate, with a majority of passengers in this class surviving.', 'The majority of people on the Titanic were aged between 17 to 30, with around 17 to 30 being the age range with the maximum number of passengers.', 'There was a lower survival rate for passengers in second class, with minimal survivors.', 'The count plot of sibling plus spouse revealed that around 600 people did not have any sibling or spouse, indicating a high frequency of passengers in that category.', 'Passengers in third class had the lowest survival rate, with a very small percentage of passengers surviving.']}, {'end': 1522.433, 'segs': [{'end': 1168.429, 'src': 'heatmap', 'start': 1122.186, 'weight': 1, 'content': [{'end': 1128.19, 'text': "similarly, what i'll do is that i'll try to find out the train.", 'start': 1122.186, 'duration': 6.004}, {'end': 1136.409, 'text': 'uh, fare Histogram also, if you just use dot test, you can see that what is the average pair of the people who are bought at tickets?', 'start': 1128.19, 'duration': 8.219}, {'end': 1137.569, 'text': 'And how did this one?', 'start': 1136.829, 'duration': 0.74}, {'end': 1144.271, 'text': 'Okay, Now, when I go down, guys, first step, as I told you that I am going to remove the null values.', 'start': 1137.929, 'duration': 6.342}, {'end': 1145.072, 'text': 'now, See what.', 'start': 1144.271, 'duration': 0.801}, {'end': 1146.552, 'text': 'how do I remove the null values?', 'start': 1145.072, 'duration': 1.48}, {'end': 1147.792, 'text': 'Null values of.', 'start': 1147.132, 'duration': 0.66}, {'end': 1155.215, 'text': "first of all, First of all, I'll go with the H column and then you saw that I will be going to my next column over here.", 'start': 1147.792, 'duration': 7.423}, {'end': 1158.696, 'text': 'so you can see that which are columns basically had null values.', 'start': 1155.215, 'duration': 3.481}, {'end': 1161.482, 'text': 'one is H and and one is cabin.', 'start': 1158.696, 'duration': 2.786}, {'end': 1163.303, 'text': 'so now, age and cabin.', 'start': 1161.482, 'duration': 1.821}, {'end': 1168.429, 'text': "first of all, we'll try to remove the h column, but before removing the h column, okay,", 'start': 1163.303, 'duration': 5.126}], 'summary': 'Analyzing train fare data to remove null values and find average ticket price.', 'duration': 49.505, 'max_score': 1122.186, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs1122186.jpg'}, {'end': 1301.486, 'src': 'heatmap', 'start': 1237.57, 'weight': 0, 'content': [{'end': 1243.052, 'text': 'this may be somewhere around 29 and this same may be somewhere around 24, 25.', 'start': 1237.57, 'duration': 5.482}, {'end': 1249.475, 'text': 'so let us see, based on this passenger class and age, i am going to replace the nan value in the h column.', 'start': 1243.052, 'duration': 6.423}, {'end': 1257.629, 'text': "so put a simple condition saying that and create a function where I'll be giving the columns that is my age, column.", 'start': 1249.475, 'duration': 8.154}, {'end': 1260.391, 'text': 'okay, age and passenger column.', 'start': 1257.629, 'duration': 2.762}, {'end': 1263.893, 'text': 'sorry, sorry, age and passenger class column.', 'start': 1260.391, 'duration': 3.502}, {'end': 1268.115, 'text': 'so first column will be my age, the second column will be my passenger.', 'start': 1263.893, 'duration': 4.222}, {'end': 1271.937, 'text': "I'm putting a condition if PD dot is null age, okay.", 'start': 1268.115, 'duration': 3.822}, {'end': 1277.02, 'text': 'if this is true, if there is a null value in that, each column, okay at that time.', 'start': 1271.937, 'duration': 5.083}, {'end': 1283.462, 'text': 'if that passenger class which i am getting from here is 1, okay, i am going to replace it with 37.', 'start': 1277.02, 'duration': 6.442}, {'end': 1284.362, 'text': 'why i am replacing with 37?', 'start': 1283.462, 'duration': 0.9}, {'end': 1292.744, 'text': 'because over here you can see that my average value of the passenger of first class with age is 37, okay.', 'start': 1284.362, 'duration': 8.382}, {'end': 1296.384, 'text': 'similarly, with the passenger class of 2 is somewhere around, you know, 29.', 'start': 1292.744, 'duration': 3.64}, {'end': 1301.486, 'text': 'so similarly i am going to replace it, i am saying over here if my t class is equal to 1, i am returning 37.', 'start': 1296.384, 'duration': 5.102}], 'summary': "Replacing null values in 'h' column based on passenger class and age.", 'duration': 63.916, 'max_score': 1237.57, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs1237570.jpg'}, {'end': 1365.719, 'src': 'heatmap', 'start': 1331.4, 'weight': 0.717, 'content': [{'end': 1333.501, 'text': "And I'm considering the age and p class.", 'start': 1331.4, 'duration': 2.101}, {'end': 1343.723, 'text': 'And I will call this function that is impute age, okay, impute age, by using the function called as dot apply.', 'start': 1333.821, 'duration': 9.902}, {'end': 1350.385, 'text': 'Now dot apply when I give the function name as impute underscore age and when I set the axis equal to one at that time.', 'start': 1343.843, 'duration': 6.542}, {'end': 1357.867, 'text': 'what this impute age is going to do for each and every records that are present in age and passenger class is going to apply this particular impute underscore age function?', 'start': 1350.385, 'duration': 7.482}, {'end': 1365.719, 'text': 'Now, once we do this and once again, we when we check the same heat map that we checked initially.', 'start': 1359.016, 'duration': 6.703}], 'summary': 'Using dot apply function to impute age based on age and passenger class.', 'duration': 34.319, 'max_score': 1331.4, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs1331400.jpg'}, {'end': 1458.592, 'src': 'embed', 'start': 1418.225, 'weight': 3, 'content': [{'end': 1420.206, 'text': "So I don't get any yellow lines over here.", 'start': 1418.225, 'duration': 1.981}, {'end': 1422.888, 'text': 'Now I have to replace this cabin.', 'start': 1420.927, 'duration': 1.961}, {'end': 1430.775, 'text': 'But the problem with this cabin is that there are many, many, many null values, right? Sorry.', 'start': 1423.469, 'duration': 7.306}, {'end': 1432.716, 'text': 'There are many, many null values.', 'start': 1431.515, 'duration': 1.201}, {'end': 1439.402, 'text': 'Now, if I want to replace something instead of this null values, you know, I have to apply some feature engineering.', 'start': 1433.197, 'duration': 6.205}, {'end': 1445.61, 'text': 'And feature engineering is altogether a different concept because, you know, we need to apply a lot of logic.', 'start': 1440.089, 'duration': 5.521}, {'end': 1447.71, 'text': 'But right now, there are so many null values.', 'start': 1445.65, 'duration': 2.06}, {'end': 1453.731, 'text': "So I'm going to drop this particular column because you know when there is so many NAND values,", 'start': 1447.73, 'duration': 6.001}, {'end': 1458.592, 'text': 'we have to do a lot of you know feature engineering to fill that NAND values with something else.', 'start': 1453.731, 'duration': 4.861}], 'summary': 'Replacing a cabin with many null values requires extensive feature engineering, considering dropping the column.', 'duration': 40.367, 'max_score': 1418.225, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs1418225.jpg'}], 'start': 1118.924, 'title': 'Data analysis and passenger class', 'summary': 'Covers data analysis, null value removal, and passenger class and age analysis, including average ticket fare, null value identification and removal, age analysis with box plots, and handling null values in the cabin column, resulting in averages of 37, 29, and 24 for classes 1, 2, and 3 respectively.', 'chapters': [{'end': 1168.429, 'start': 1118.924, 'title': 'Data analysis and null value removal', 'summary': "Covers the process of finding average ticket fare, identifying null values in columns 'h' and 'cabin', and the approach to removing null values.", 'duration': 49.505, 'highlights': ["Identifying null values in columns 'H' and 'cabin' and planning to remove them.", 'Exploring average ticket fare by analyzing the train fare histogram.', 'Emphasizing the process of removing null values as a crucial step in the data analysis.']}, {'end': 1522.433, 'start': 1168.429, 'title': 'Passenger class and age analysis', 'summary': 'Covers the analysis of passenger class and age, using box plots to find average ages for each class, and replacing null values in the age column based on the passenger class, resulting in averages of 37, 29, and 24 for classes 1, 2, and 3 respectively, and subsequent handling of null values in the cabin column by dropping it.', 'duration': 354.004, 'highlights': ['Replacing Null Values in Age Column Null values in the age column were replaced based on passenger class, resulting in averages of 37, 29, and 24 for classes 1, 2, and 3 respectively.', 'Handling Null Values in Cabin Column The approach to handling numerous null values in the cabin column involved dropping the column due to the complexity of feature engineering required to fill the null values.']}], 'duration': 403.509, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs1118924.jpg', 'highlights': ['Replacing null values in the age column based on passenger class, resulting in averages of 37, 29, and 24 for classes 1, 2, and 3 respectively.', "Identifying null values in columns 'H' and 'cabin' and planning to remove them.", 'Exploring average ticket fare by analyzing the train fare histogram.', 'Emphasizing the process of removing null values as a crucial step in the data analysis.', 'Handling null values in the cabin column involved dropping the column due to the complexity of feature engineering required to fill the null values.']}, {'end': 1903.336, 'segs': [{'end': 1754.713, 'src': 'heatmap', 'start': 1538.735, 'weight': 0.783, 'content': [{'end': 1543.779, 'text': 'So this has to be represented in some integer format before I pass to the model.', 'start': 1538.735, 'duration': 5.044}, {'end': 1550.926, 'text': "So I'm going to handle this particular category features by using Pandas by using an inbuilt function called as getDummies.", 'start': 1544.7, 'duration': 6.226}, {'end': 1561.515, 'text': "So if you go down and see this, I'm using pd.getDummies and I'm also going to apply this getDummies to mbac column also.", 'start': 1551.206, 'duration': 10.309}, {'end': 1562.576, 'text': 'Embark column.', 'start': 1561.935, 'duration': 0.641}, {'end': 1565.8, 'text': 'So there are three categories in Embark and two categories in text.', 'start': 1562.596, 'duration': 3.204}, {'end': 1570.625, 'text': "So I'm going to apply the category dummy variable to both the sex and Embark column.", 'start': 1565.82, 'duration': 4.805}, {'end': 1574.029, 'text': "So for this, what I'm going to do is that I'm going to go down.", 'start': 1571.025, 'duration': 3.004}, {'end': 1577.512, 'text': "I'm going to apply pd.get dummies and I'm going to convert.", 'start': 1574.689, 'duration': 2.823}, {'end': 1583.309, 'text': "I'm going to take the column which is called as Embark And I'm going to remove the first row.", 'start': 1577.673, 'duration': 5.636}, {'end': 1588.333, 'text': 'And what does get them is do is that it converts that many number of categories, that many number of columns.', 'start': 1583.569, 'duration': 4.764}, {'end': 1592.697, 'text': 'So if I have three domains, it converts into three categories or three columns.', 'start': 1588.794, 'duration': 3.903}, {'end': 1597.842, 'text': 'Okay And I can remove the first column because the other two columns can represent the first column.', 'start': 1592.778, 'duration': 5.064}, {'end': 1601.225, 'text': "How I'm saying is that suppose I had three columns like PQS.", 'start': 1597.902, 'duration': 3.323}, {'end': 1602.767, 'text': 'Okay Now, you know that.', 'start': 1601.506, 'duration': 1.261}, {'end': 1606.956, 'text': 'each and every category gets a unique value like 0, 1, 0.', 'start': 1603.769, 'duration': 3.187}, {'end': 1608.572, 'text': '1 is basically for s.', 'start': 1606.956, 'duration': 1.616}, {'end': 1609.172, 'text': 'okay, 1.', 'start': 1608.572, 'duration': 0.6}, {'end': 1613.315, 'text': '0 is basically for q, 0, 0 will be basically for p.', 'start': 1609.172, 'duration': 4.143}, {'end': 1616.457, 'text': "so for that case, what i'm doing is that i'm dropping the first column.", 'start': 1613.315, 'duration': 3.142}, {'end': 1620.5, 'text': "i don't require it, and this is basically called as dummy variable track.", 'start': 1616.457, 'duration': 4.043}, {'end': 1624.042, 'text': "okay, so i'm just taking the two columns similarly, with the help of sex.", 'start': 1620.5, 'duration': 3.542}, {'end': 1629.646, 'text': "uh, what i'm doing is that i'll try to convert the sex also into dummy variables and drop first is equal to.", 'start': 1624.042, 'duration': 5.604}, {'end': 1633.725, 'text': "So finally, I'm going to drop all the columns that are not required.", 'start': 1630.464, 'duration': 3.261}, {'end': 1636.586, 'text': "Like, I'll not require the passenger ID.", 'start': 1633.785, 'duration': 2.801}, {'end': 1638.026, 'text': "I'll not require name.", 'start': 1637.086, 'duration': 0.94}, {'end': 1641.347, 'text': "I'll not require the sex because I've converted this into category features.", 'start': 1638.166, 'duration': 3.181}, {'end': 1643.047, 'text': "I'll not require mbox also.", 'start': 1641.807, 'duration': 1.24}, {'end': 1645.088, 'text': 'So, here it is.', 'start': 1643.688, 'duration': 1.4}, {'end': 1649.109, 'text': 'I will go and drop all the columns.', 'start': 1646.949, 'duration': 2.16}, {'end': 1655.791, 'text': "I'm dropping sex, mbox name, ticket because I have created two more columns which is called sex and mbox in category features.", 'start': 1649.169, 'duration': 6.622}, {'end': 1658.964, 'text': "So what I'm going to do, first of all, I'm going to drop it.", 'start': 1656.44, 'duration': 2.524}, {'end': 1661.027, 'text': "After dropping it, I'll just see the head part.", 'start': 1659.345, 'duration': 1.682}, {'end': 1664.532, 'text': "My head part you can see that I'm not having sex embark name ticket right?", 'start': 1661.167, 'duration': 3.365}, {'end': 1668.919, 'text': 'But I have to append the sex column and embark column because these are the category features.', 'start': 1665.053, 'duration': 3.866}, {'end': 1676.31, 'text': "So for that, I'll be using pd.concat train sex mbar.", 'start': 1669.864, 'duration': 6.446}, {'end': 1681.574, 'text': 'So training data or sex data which is in my categories feature and mbar data.', 'start': 1676.43, 'duration': 5.144}, {'end': 1690.522, 'text': 'So here you can see that all my data has got added in the last and I have us for mbar and main as for sex data.', 'start': 1682.555, 'duration': 7.967}, {'end': 1693.202, 'text': 'Now, my data is ready.', 'start': 1691.801, 'duration': 1.401}, {'end': 1694.522, 'text': 'This is my whole data.', 'start': 1693.522, 'duration': 1}, {'end': 1699.144, 'text': 'Now, from this data, I have to divide this data into dependent and independent features.', 'start': 1694.922, 'duration': 4.222}, {'end': 1706.287, 'text': 'Now, you know, survive column is basically my dependent feature, whereas all the remaining columns are my independent feature.', 'start': 1699.644, 'duration': 6.643}, {'end': 1709.008, 'text': "So, I'll be applying a logistic regression model.", 'start': 1706.327, 'duration': 2.681}, {'end': 1710.989, 'text': "First of all, I'll be doing a train test split.", 'start': 1709.048, 'duration': 1.941}, {'end': 1716.531, 'text': "But before doing a train test split, I'm going to create a train data where I'm dropping the survive column,", 'start': 1711.369, 'duration': 5.162}, {'end': 1718.672, 'text': 'because survive column is basically my dependent feature.', 'start': 1716.531, 'duration': 2.141}, {'end': 1722.199, 'text': 'now this is the complete training data set.', 'start': 1719.375, 'duration': 2.824}, {'end': 1726.145, 'text': 'okay, and this is my output data set, that is train survive dot help.', 'start': 1722.199, 'duration': 3.946}, {'end': 1728.669, 'text': 'okay, train of survived is basically my output data.', 'start': 1726.145, 'duration': 2.524}, {'end': 1732.545, 'text': "So considering these two data, I'm going to do the train test split.", 'start': 1729.363, 'duration': 3.182}, {'end': 1736.468, 'text': "So for that, I'm using sklearn.model collection and train test split.", 'start': 1732.985, 'duration': 3.483}, {'end': 1741.491, 'text': "Here, in my test size, I'm taking it as 0.3, that is 30% will be my test.", 'start': 1736.848, 'duration': 4.643}, {'end': 1744.113, 'text': "And here, I'm just giving x and y value.", 'start': 1742.072, 'duration': 2.041}, {'end': 1748.116, 'text': 'x value is basically train.drop survive, which is actually equal to 1.', 'start': 1744.473, 'duration': 3.643}, {'end': 1749.897, 'text': 'And my y value is basically my survive column.', 'start': 1748.116, 'duration': 1.781}, {'end': 1754.713, 'text': "After this, I'm going to import my logistic regression model.", 'start': 1751.531, 'duration': 3.182}], 'summary': 'Using pandas getdummies to convert and handle categorical features for logistic regression model training.', 'duration': 215.978, 'max_score': 1538.735, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs1538735.jpg'}, {'end': 1570.625, 'src': 'embed', 'start': 1544.7, 'weight': 0, 'content': [{'end': 1550.926, 'text': "So I'm going to handle this particular category features by using Pandas by using an inbuilt function called as getDummies.", 'start': 1544.7, 'duration': 6.226}, {'end': 1561.515, 'text': "So if you go down and see this, I'm using pd.getDummies and I'm also going to apply this getDummies to mbac column also.", 'start': 1551.206, 'duration': 10.309}, {'end': 1562.576, 'text': 'Embark column.', 'start': 1561.935, 'duration': 0.641}, {'end': 1565.8, 'text': 'So there are three categories in Embark and two categories in text.', 'start': 1562.596, 'duration': 3.204}, {'end': 1570.625, 'text': "So I'm going to apply the category dummy variable to both the sex and Embark column.", 'start': 1565.82, 'duration': 4.805}], 'summary': 'Using pandas getdummies to apply category dummy variable to sex and embark columns.', 'duration': 25.925, 'max_score': 1544.7, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs1544700.jpg'}, {'end': 1722.199, 'src': 'embed', 'start': 1694.922, 'weight': 1, 'content': [{'end': 1699.144, 'text': 'Now, from this data, I have to divide this data into dependent and independent features.', 'start': 1694.922, 'duration': 4.222}, {'end': 1706.287, 'text': 'Now, you know, survive column is basically my dependent feature, whereas all the remaining columns are my independent feature.', 'start': 1699.644, 'duration': 6.643}, {'end': 1709.008, 'text': "So, I'll be applying a logistic regression model.", 'start': 1706.327, 'duration': 2.681}, {'end': 1710.989, 'text': "First of all, I'll be doing a train test split.", 'start': 1709.048, 'duration': 1.941}, {'end': 1716.531, 'text': "But before doing a train test split, I'm going to create a train data where I'm dropping the survive column,", 'start': 1711.369, 'duration': 5.162}, {'end': 1718.672, 'text': 'because survive column is basically my dependent feature.', 'start': 1716.531, 'duration': 2.141}, {'end': 1722.199, 'text': 'now this is the complete training data set.', 'start': 1719.375, 'duration': 2.824}], 'summary': 'Data divided into dependent and independent features for logistic regression model.', 'duration': 27.277, 'max_score': 1694.922, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs1694922.jpg'}, {'end': 1842.692, 'src': 'embed', 'start': 1816.531, 'weight': 2, 'content': [{'end': 1823.672, 'text': 'if i go and see my crazy score, there is somewhere around 71.9 percent.', 'start': 1816.531, 'duration': 7.141}, {'end': 1827.733, 'text': 'okay, so 71.9 percent is just by applying a simple logistic regression method.', 'start': 1823.672, 'duration': 4.061}, {'end': 1835.527, 'text': "If I go and apply some more complex algorithms like random forestization or XGBoost, I'll be getting a very good accuracy.", 'start': 1828.382, 'duration': 7.145}, {'end': 1838.509, 'text': 'Now with logistic regression also I can fine tune this model.', 'start': 1835.867, 'duration': 2.642}, {'end': 1842.692, 'text': 'Again, fine tuning part will be displayed in another video where I can improve.', 'start': 1838.569, 'duration': 4.123}], 'summary': 'Achieved 71.9% accuracy using logistic regression, aiming for better with advanced algorithms.', 'duration': 26.161, 'max_score': 1816.531, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs1816531.jpg'}, {'end': 1903.336, 'src': 'embed', 'start': 1880.962, 'weight': 3, 'content': [{'end': 1884.584, 'text': 'And please subscribe to the channel, share with all your friends who require this kind of help.', 'start': 1880.962, 'duration': 3.622}, {'end': 1891.149, 'text': "um. and one more good news is that in my upcoming videos i'm going to create a deep learning playlist separately,", 'start': 1885.565, 'duration': 5.584}, {'end': 1896.432, 'text': "where i'll be explaining all the theoretical concepts along with the practical um.", 'start': 1891.149, 'duration': 5.283}, {'end': 1897.413, 'text': "yeah, that's it.", 'start': 1896.432, 'duration': 0.981}, {'end': 1902.576, 'text': 'uh, happy learning, enjoy your day, have a wonderful day ahead and keep learning, guys.', 'start': 1897.413, 'duration': 5.163}, {'end': 1903.336, 'text': 'thank you 100, thank you all.', 'start': 1902.576, 'duration': 0.76}], 'summary': 'Upcoming videos will include a deep learning playlist, covering theoretical concepts and practical applications.', 'duration': 22.374, 'max_score': 1880.962, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs1880962.jpg'}], 'start': 1523.474, 'title': 'Data preprocessing and logistic regression model', 'summary': 'Outlines data preprocessing for logistic regression, including converting categorical features, dropping unnecessary columns, and performing a train-test split with a 30% test size. it also covers applying a logistic regression model with 71.9% accuracy and discusses potential improvements using more complex algorithms like xgboost to reach 87-88% accuracy, along with the promise of upcoming deep learning content.', 'chapters': [{'end': 1749.897, 'start': 1523.474, 'title': 'Data preprocessing for logistic regression', 'summary': 'Outlines the process of converting categorical features into dummy variables using pandas for logistic regression, dropping unnecessary columns, and performing a train-test split with a test size of 30%.', 'duration': 226.423, 'highlights': ['The process of converting categorical features into dummy variables using Pandas for logistic regression is outlined, including the use of pd.get_dummies and dropping unnecessary columns. Pandas method pd.get_dummies, dropping of unnecessary columns', 'The train-test split process is detailed, with a test size of 30% and the division of data into dependent and independent features for logistic regression. Train-test split with 30% test size']}, {'end': 1903.336, 'start': 1751.531, 'title': 'Logistic regression model and model improvement', 'summary': 'Covers importing and applying a logistic regression model, achieving 71.9% accuracy, and discussing the potential for improvement using more complex algorithms like xgboost to reach 87-88% accuracy, along with the promise of upcoming deep learning content.', 'duration': 151.805, 'highlights': ['The model achieved an accuracy of 71.9% using logistic regression, and could potentially be improved to 87-88% using more complex algorithms like XGBoost.', 'The process of fine-tuning the logistic regression model to achieve higher accuracy will be detailed in a separate video.', 'The speaker plans to create a separate deep learning playlist, providing theoretical concepts and practical guidance in upcoming videos.']}], 'duration': 379.862, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Ea_KAcdv1vs/pics/Ea_KAcdv1vs1523474.jpg', 'highlights': ['The process of converting categorical features into dummy variables using Pandas for logistic regression is outlined, including the use of pd.get_dummies and dropping unnecessary columns.', 'The train-test split process is detailed, with a test size of 30% and the division of data into dependent and independent features for logistic regression.', 'The model achieved an accuracy of 71.9% using logistic regression, and could potentially be improved to 87-88% using more complex algorithms like XGBoost.', 'The speaker plans to create a separate deep learning playlist, providing theoretical concepts and practical guidance in upcoming videos.']}], 'highlights': ['The model achieved an accuracy of 71.9% using logistic regression, and could potentially be improved to 87-88% using more complex algorithms like XGBoost.', 'The process of converting categorical features into dummy variables using Pandas for logistic regression is outlined, including the use of pd.get_dummies and dropping unnecessary columns.', 'The main aim is to predict whether the passenger has survived or not based on the available information from the dataset.', 'The chapter demonstrates the analysis of survival rates based on gender, revealing that a higher proportion of males did not survive compared to females.', 'The majority of people on the Titanic were aged between 17 to 30, with around 17 to 30 being the age range with the maximum number of passengers.', 'The significance of exploratory data analysis in the machine learning lifecycle is emphasized, where a significant amount of time is spent analyzing and exploring data.', 'The need to be a detective when conducting exploratory data analysis is highlighted, emphasizing the importance of extracting as much information as possible from a given dataset.', 'Importance of data preprocessing for model accuracy', 'Emphasizing the significance of visualization using Seaborn for data analysis', 'Pandas library helps in reading the dataset and performing data pre-processing steps']}