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Data Science With Python | Python for Data Science | Python Data Science Tutorial | Simplilearn

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This Data Science with Python Tutorial will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python. The aim of this video is to provide a comprehensive knowledge to beginners who are new to Python for data analysis. This video provides a comprehensive overview of basic concepts that you need to learn to use Python for data analysis. Now, let us understand how Python is used in Data Science for data analysis.
This Data Science with Python tutorial will cover the following topics:
1. What is Data Science?
2. Basics of Python for data analysis
- Why learn Python?
- How to install Python?
3. Python libraries for data analysis
4. Exploratory analysis using Pandas
- Introduction to series and dataframe
- Loan prediction problem
5. Data wrangling using Pandas
6. Building a predictive model using Scikit-learn
- Logistic regression
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- Data Visualization
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{'title': 'Data Science With Python | Python for Data Science | Python Data Science Tutorial | Simplilearn', 'heatmap': [{'end': 479.229, 'start': 439.711, 'weight': 0.789}, {'end': 1162.564, 'start': 1122.834, 'weight': 1}], 'summary': "Tutorial on 'data science with python' covers an introduction to data science with python, its role in business, exploratory data analysis, data handling, implementing scikit-learn for machine learning, and logistic regression. it emphasizes key python libraries and demonstrates achieving 80% accuracy and precision using logistic regression and 150 observations.", 'chapters': [{'end': 128.197, 'segs': [{'end': 51.501, 'src': 'embed', 'start': 26.972, 'weight': 1, 'content': [{'end': 35.875, 'text': 'Why to learn Python? How to install Python? And then we will talk about some of the important libraries which are required for data analysis.', 'start': 26.972, 'duration': 8.903}, {'end': 40.337, 'text': 'And then we will go into a little bit of details about exploratory data analysis.', 'start': 36.175, 'duration': 4.162}, {'end': 44.218, 'text': 'And we will take an example there of loan prediction.', 'start': 40.537, 'duration': 3.681}, {'end': 51.501, 'text': 'And we will see a little bit about data wrangling using Pandas, which is one of the libraries of Python.', 'start': 44.638, 'duration': 6.863}], 'summary': 'Learn python for data analysis, including libraries and data wrangling using pandas.', 'duration': 24.529, 'max_score': 26.972, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk26972.jpg'}, {'end': 123.012, 'src': 'embed', 'start': 91.94, 'weight': 0, 'content': [{'end': 98.868, 'text': 'so if there is a lot of data, if you have sufficient data, how to analyze and find some insights out of it.', 'start': 91.94, 'duration': 6.928}, {'end': 101.171, 'text': 'this is what is data science all about.', 'start': 98.868, 'duration': 2.303}, {'end': 103.793, 'text': 'a couple of examples here customer prediction.', 'start': 101.171, 'duration': 2.622}, {'end': 110.22, 'text': "now let's say you have a customer base and you want to find out who are most likely to buy your product.", 'start': 103.793, 'duration': 6.427}, {'end': 113.163, 'text': 'so you can use from your past behavior.', 'start': 110.22, 'duration': 2.943}, {'end': 123.012, 'text': 'you can probably develop a model and try to predict who are the people out of the thousand leads or potential customers who will actually buy.', 'start': 113.163, 'duration': 9.849}], 'summary': 'Data science involves analyzing large datasets to make predictions, such as customer behavior and purchasing trends.', 'duration': 31.072, 'max_score': 91.94, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk91940.jpg'}], 'start': 3.305, 'title': 'Introduction to data science with python', 'summary': 'Provides an overview of data science with python, covering the basics of python, important libraries for data analysis, exploratory data analysis, and a logistic regression model, emphasizing the use of scikit-learn library and insights from data science, with examples of customer prediction and service planning.', 'chapters': [{'end': 128.197, 'start': 3.305, 'title': 'Introduction to data science with python', 'summary': 'Provides an overview of data science with python, covering the basics of python, important libraries for data analysis, exploratory data analysis, and a logistic regression model, emphasizing the use of scikit-learn library and insights from data science, with examples of customer prediction and service planning.', 'duration': 124.892, 'highlights': ['The chapter provides an overview of data science with Python, covering the basics of Python, important libraries for data analysis, exploratory data analysis, and a logistic regression model, emphasizing the use of scikit-learn library and insights from data science. overview of data science, basics of Python, important libraries for data analysis, logistic regression model, scikit-learn library, insights from data science', 'Examples of customer prediction and service planning are provided to illustrate how data science can be applied to predict customer behavior and plan services based on past behavior patterns. customer prediction, service planning, predicting customer behavior, planning services']}], 'duration': 124.892, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk3305.jpg', 'highlights': ['Examples of customer prediction and service planning are provided to illustrate how data science can be applied to predict customer behavior and plan services based on past behavior patterns.', 'The chapter provides an overview of data science with Python, covering the basics of Python, important libraries for data analysis, exploratory data analysis, and a logistic regression model, emphasizing the use of scikit-learn library and insights from data science.']}, {'end': 932.465, 'segs': [{'end': 152.029, 'src': 'embed', 'start': 128.197, 'weight': 1, 'content': [{'end': 135.961, 'text': 'you are running a restaurant and you want to know how many people will be coming or how many customers will be visiting your restaurant on a given day.', 'start': 128.197, 'duration': 7.764}, {'end': 143.606, 'text': 'Now based on your historical data you can build a model to predict that as well so that there is no wastage of food and so on and so forth.', 'start': 136.121, 'duration': 7.485}, {'end': 149.948, 'text': 'So these are very quick and easy examples of how data science can be used in business.', 'start': 143.706, 'duration': 6.242}, {'end': 152.029, 'text': "Now let's talk about Python.", 'start': 150.188, 'duration': 1.841}], 'summary': 'Predict customer visits, avoid food wastage using data science in business.', 'duration': 23.832, 'max_score': 128.197, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk128197.jpg'}, {'end': 199.494, 'src': 'embed', 'start': 170.518, 'weight': 0, 'content': [{'end': 176.162, 'text': 'everybody is talking about python, not only data science, in iot and ai and many other places.', 'start': 170.518, 'duration': 5.644}, {'end': 177.763, 'text': "so it's a very popular.", 'start': 176.162, 'duration': 1.601}, {'end': 178.944, 'text': "it's getting very popular.", 'start': 177.763, 'duration': 1.181}, {'end': 183.828, 'text': 'so if you are not yet familiar with python, this may be a good time to get started with it.', 'start': 178.944, 'duration': 4.884}, {'end': 185.849, 'text': 'so why do we want to use Python?', 'start': 184.028, 'duration': 1.821}, {'end': 192.751, 'text': 'so, basically, Python is used as a programming language because it is for data science,', 'start': 185.849, 'duration': 6.902}, {'end': 199.494, 'text': 'because it has some rich tools from mathematics and from a statistical perspective, it has some rich tools.', 'start': 192.751, 'duration': 6.743}], 'summary': 'Python is gaining popularity in various fields including data science, iot, and ai due to its rich tools and wide usage.', 'duration': 28.976, 'max_score': 170.518, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk170518.jpg'}, {'end': 260.594, 'src': 'embed', 'start': 231.695, 'weight': 2, 'content': [{'end': 238.14, 'text': 'but beyond that, i think it is the ease of understanding this language, the ease of using this language, which is also making it very popular,', 'start': 231.695, 'duration': 6.445}, {'end': 242.823, 'text': 'in addition to the availability of fantastic libraries for performing data science.', 'start': 238.26, 'duration': 4.563}, {'end': 251.209, 'text': 'What are the other factors? There are speed, then there are availability of number of packages, and then of course, the design goal.', 'start': 243.023, 'duration': 8.186}, {'end': 260.594, 'text': 'Alright, so what are each of these? Design goal, primarily the syntax rules in Python are relatively intuitive and easy to understand.', 'start': 251.469, 'duration': 9.125}], 'summary': "Python's popularity is attributed to its ease of use, availability of libraries, speed, packages, and intuitive syntax rules.", 'duration': 28.899, 'max_score': 231.695, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk231695.jpg'}, {'end': 342.366, 'src': 'embed', 'start': 309.442, 'weight': 3, 'content': [{'end': 310.602, 'text': 'You can always look around.', 'start': 309.442, 'duration': 1.16}, {'end': 312.043, 'text': 'But this is one of the again.', 'start': 310.762, 'duration': 1.281}, {'end': 317.825, 'text': 'there are different ways in which you can also install python, so we will use the anaconda path.', 'start': 312.043, 'duration': 5.782}, {'end': 322.546, 'text': 'there is a packaging tool called anaconda, so we will use that path.', 'start': 317.825, 'duration': 4.721}, {'end': 327.468, 'text': 'you can also directly install python, but in our session we will use the anaconda route.', 'start': 322.546, 'duration': 4.922}, {'end': 331.833, 'text': 'So the first thing you need to do is download Anaconda, and this is the path for that.', 'start': 327.668, 'duration': 4.165}, {'end': 337.96, 'text': 'And once you click on this, you will come to a page somewhat like this and download.', 'start': 332.214, 'duration': 5.746}, {'end': 342.366, 'text': 'You can do the corresponding download based on whether you have a Windows or Ubuntu.', 'start': 337.98, 'duration': 4.386}], 'summary': 'Anaconda is recommended for installing python, available for windows and ubuntu.', 'duration': 32.924, 'max_score': 309.442, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk309442.jpg'}, {'end': 584.055, 'src': 'heatmap', 'start': 439.711, 'weight': 5, 'content': [{'end': 444.473, 'text': 'so pandas, for example, is used for structured data operations.', 'start': 439.711, 'duration': 4.762}, {'end': 450.037, 'text': "so if you, let's say, are performing something on a csv file, you import a csv file,", 'start': 444.473, 'duration': 5.564}, {'end': 458.261, 'text': 'create a data frame and then you can do a lot of stuff like data munging and data preparation before you do any other stuff like, for example,', 'start': 450.037, 'duration': 8.224}, {'end': 459.342, 'text': 'machine learning or so on.', 'start': 458.261, 'duration': 1.081}, {'end': 462.783, 'text': "so that's pandas scipy, as the name suggests.", 'start': 459.582, 'duration': 3.201}, {'end': 463.583, 'text': 'it is kind of.', 'start': 462.783, 'duration': 0.8}, {'end': 471.346, 'text': 'it provides more scientific capabilities, like, for example, it has linear algebra, it has fourier transform and so on and so forth.', 'start': 463.583, 'duration': 7.763}, {'end': 479.229, 'text': 'then you have numpy, which is a very powerful library for performing n dimensional or creating n dimensional arrays,', 'start': 471.346, 'duration': 7.883}, {'end': 486.331, 'text': 'and it also has some of the stuff that is there in scipy, like, for example, linear algebra and fourier transform and so on and so forth.', 'start': 479.229, 'duration': 7.102}, {'end': 491.394, 'text': 'Then you have Matplotlib, which is primarily for visualization purpose.', 'start': 486.391, 'duration': 5.003}, {'end': 500.219, 'text': 'It has again very powerful features for visualizing your data, for doing the initial, what is known as exploratory data analysis,', 'start': 491.614, 'duration': 8.605}, {'end': 503.501, 'text': 'for doing univariate analysis, bivariate analysis.', 'start': 500.219, 'duration': 3.282}, {'end': 506.622, 'text': 'So this is extremely useful for visualizing the data.', 'start': 503.541, 'duration': 3.081}, {'end': 511.385, 'text': 'And then scikit-learn is used for performing all the machine learning activities.', 'start': 506.782, 'duration': 4.603}, {'end': 519.491, 'text': 'If you want to do anything like linear regression, classification or any of this stuff, then the scikit-learn library will be extremely helpful.', 'start': 511.425, 'duration': 8.066}, {'end': 524.795, 'text': 'In addition to that, there are a few other libraries, for example, networks and iGraph.', 'start': 519.831, 'duration': 4.964}, {'end': 527.257, 'text': 'Then, of course, a very important one is TensorFlow.', 'start': 524.915, 'duration': 2.342}, {'end': 535.924, 'text': 'So if you are interested in doing some deep learning or AI related stuff, then it would be a good idea to learn about TensorFlow,', 'start': 527.317, 'duration': 8.607}, {'end': 537.465, 'text': 'and TensorFlow is one of the libraries.', 'start': 535.924, 'duration': 1.541}, {'end': 539.347, 'text': 'There is a separate video on TensorFlow.', 'start': 537.625, 'duration': 1.722}, {'end': 540.508, 'text': 'You can look for that.', 'start': 539.367, 'duration': 1.141}, {'end': 544.872, 'text': 'And this is one of the libraries created by Google, open source library.', 'start': 540.668, 'duration': 4.204}, {'end': 551.538, 'text': "So once you're familiar with machine learning, data analysis, machine learning, then that may be the next step to go to deep learning and AI.", 'start': 544.952, 'duration': 6.586}, {'end': 553.4, 'text': "So that's where TensorFlow will be used.", 'start': 551.578, 'duration': 1.822}, {'end': 557.764, 'text': 'Then you have Beautiful Soup, which is primarily used for web scraping.', 'start': 553.52, 'duration': 4.244}, {'end': 560.006, 'text': 'And then you take that data and then analyze.', 'start': 558.024, 'duration': 1.982}, {'end': 560.646, 'text': 'and so on.', 'start': 560.166, 'duration': 0.48}, {'end': 563.607, 'text': 'then OS library is a very common library, as the name suggests.', 'start': 560.646, 'duration': 2.961}, {'end': 570.35, 'text': 'it is for operating system, so if you want to do something on creating directories or folders and things like that,', 'start': 563.607, 'duration': 6.743}, {'end': 572.991, 'text': "that's when you would use OS all right.", 'start': 570.35, 'duration': 2.641}, {'end': 577.832, 'text': "so, moving on, let's talk in a little bit more detail about each of these libraries.", 'start': 572.991, 'duration': 4.841}, {'end': 584.055, 'text': 'so scipy, as the name suggests, is a scientific library and it, very specifically,', 'start': 577.832, 'duration': 6.223}], 'summary': 'Pandas, numpy, matplotlib, scikit-learn, and tensorflow are key libraries for data operations, visualization, and machine learning, while beautiful soup is used for web scraping and os for operating system tasks.', 'duration': 112.709, 'max_score': 439.711, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk439711.jpg'}, {'end': 681.322, 'src': 'embed', 'start': 649.964, 'weight': 4, 'content': [{'end': 653.866, 'text': 'These are very powerful data structures that are used in Python programming.', 'start': 649.964, 'duration': 3.902}, {'end': 657.008, 'text': 'So pandas library provides this capability.', 'start': 654.086, 'duration': 2.922}, {'end': 664.292, 'text': "And once you import a data, import the data into data frame, you can pretty much do whatever you're doing like in a regular database.", 'start': 657.408, 'duration': 6.884}, {'end': 673.257, 'text': 'So people who are coming from a database background or SQL background would really like this, because it is very they will feel very much at home,', 'start': 664.372, 'duration': 8.885}, {'end': 681.322, 'text': "because it feels like you're using your viewing a table or using a table and you can do a lot of stuff using the Pandas library.", 'start': 673.257, 'duration': 8.065}], 'summary': 'Pandas library in python enables database-like operations on data frames, appealing to users with database or sql backgrounds.', 'duration': 31.358, 'max_score': 649.964, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk649964.jpg'}], 'start': 128.197, 'title': "Python's role in data science for business", 'summary': 'Covers the use of python for predicting customer visits, its popularity due to rich tools and open-source nature, guidance on installing python using anaconda, and the applications of key python libraries like pandas, numpy, matplotlib, scikit-learn, tensorflow, beautiful soup, and os in data analysis and manipulation.', 'chapters': [{'end': 430.024, 'start': 128.197, 'title': 'Python for data science in business', 'summary': "Discusses the use of data science in business, highlighting the importance of predicting customer visits to minimize food wastage, and emphasizes python's growing popularity due to its rich tools, open-source nature, ease of use, and availability of libraries. it also provides guidance on installing python using anaconda and introduces the concept of libraries in python.", 'duration': 301.827, 'highlights': ["Python's growing popularity in data science, IoT, and AI is emphasized, making it the programming language of choice, especially for data science, due to its rich tools and open-source nature. Python is becoming popular in various domains like data science, IoT, and AI, with its rich tools and open-source nature, making it the programming language of choice, especially for data science.", 'The importance of predicting customer visits to minimize food wastage is emphasized, showcasing the practical application of data science in business. Using historical data to predict customer visits helps in minimizing food wastage, showcasing the practical application of data science in business.', "Python's ease of use, availability of fantastic libraries, speed, and open-source nature are highlighted as key factors contributing to its popularity for data science. Python's popularity for data science is attributed to its ease of use, availability of libraries, speed, and open-source nature.", 'Guidance on installing Python using Anaconda is provided, offering a practical approach to getting started with Python. The chapter provides guidance on installing Python using Anaconda, offering a practical approach to getting started with Python.', 'Introduction to the concept of libraries in Python is provided, highlighting their importance in simplifying code development and reuse. The chapter introduces the concept of libraries in Python, emphasizing their role in simplifying code development and reuse.']}, {'end': 932.465, 'start': 430.024, 'title': 'Python libraries for data analysis', 'summary': 'Discusses the importance and applications of key python libraries like pandas, numpy, matplotlib, scikit-learn, tensorflow, beautiful soup, and os, in data analysis and data manipulation, with emphasis on their specific functions and capabilities.', 'duration': 502.441, 'highlights': ['Pandas is used for structured data operations and data manipulation, where data frames serve as powerful data structures used in Python programming. Pandas is essential for structured data operations and data manipulation, utilizing data frames as powerful data structures in Python programming.', 'NumPy is a powerful library for creating n-dimensional arrays with mathematical capabilities, including linear algebra and Fourier transformation. NumPy is crucial for creating n-dimensional arrays and offers mathematical functionalities such as linear algebra and Fourier transformation.', 'Matplotlib is primarily for visualization purposes, offering powerful features for visualizing data and conducting exploratory data analysis. Matplotlib is essential for visualization purposes, providing powerful features for exploratory data analysis and data visualization.', 'scikit-learn is used for machine learning activities, including linear regression, classification, and other machine learning tasks. scikit-learn is utilized for various machine learning activities such as linear regression, classification, and other machine learning tasks.', 'TensorFlow is an important library for deep learning and AI-related tasks, particularly useful for those interested in these domains. TensorFlow is significant for deep learning and AI-related tasks, particularly beneficial for individuals interested in these domains.', 'Beautiful Soup is primarily used for web scraping, enabling the extraction of data from web pages for further analysis. Beautiful Soup is crucial for web scraping, facilitating data extraction from web pages for subsequent analysis.', 'The OS library is commonly used for operating system-related tasks, such as creating directories or folders. The OS library is commonly employed for operating system-related tasks, including creating directories or folders.']}], 'duration': 804.268, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk128197.jpg', 'highlights': ["Python's growing popularity in data science, IoT, and AI is emphasized, making it the programming language of choice, especially for data science, due to its rich tools and open-source nature.", 'The importance of predicting customer visits to minimize food wastage is emphasized, showcasing the practical application of data science in business.', "Python's ease of use, availability of fantastic libraries, speed, and open-source nature are highlighted as key factors contributing to its popularity for data science.", 'Guidance on installing Python using Anaconda is provided, offering a practical approach to getting started with Python.', 'Pandas is used for structured data operations and data manipulation, where data frames serve as powerful data structures used in Python programming.', 'NumPy is a powerful library for creating n-dimensional arrays with mathematical capabilities, including linear algebra and Fourier transformation.', 'Matplotlib is primarily for visualization purposes, offering powerful features for visualizing data and conducting exploratory data analysis.', 'scikit-learn is used for machine learning activities, including linear regression, classification, and other machine learning tasks.', 'TensorFlow is an important library for deep learning and AI-related tasks, particularly useful for those interested in these domains.', 'Beautiful Soup is primarily used for web scraping, enabling the extraction of data from web pages for further analysis.', 'The OS library is commonly used for operating system-related tasks, such as creating directories or folders.']}, {'end': 1400.477, 'segs': [{'end': 1031.19, 'src': 'embed', 'start': 1009.13, 'weight': 0, 'content': [{'end': 1019.159, 'text': 'And, in addition, if we include this piece of code percentage matplotlib inline what will happen is all the graphs that we are going to create,', 'start': 1009.13, 'duration': 10.029}, {'end': 1024.443, 'text': 'the visualizations that we are going to create, will be displayed within the notebook.', 'start': 1019.159, 'duration': 5.284}, {'end': 1028.887, 'text': 'So if you want to have that kind of a provision, you need to have this line.', 'start': 1024.463, 'duration': 4.424}, {'end': 1031.19, 'text': "so it's always a good idea when you're starting off.", 'start': 1028.887, 'duration': 2.303}], 'summary': "Including the code 'percentage matplotlib inline' will display all graphs and visualizations within the notebook.", 'duration': 22.06, 'max_score': 1009.13, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk1009130.jpg'}, {'end': 1127.958, 'src': 'embed', 'start': 1097.771, 'weight': 1, 'content': [{'end': 1099.373, 'text': 'now you have imported the data.', 'start': 1097.771, 'duration': 1.602}, {'end': 1105.04, 'text': 'you want to initially have a quick look how your data is looking, what are the values in some of the columns and so on and so forth.', 'start': 1099.373, 'duration': 5.667}, {'end': 1113.446, 'text': "right, so typically you would do a head df.head to get the sample of, let's say, the first few lines of your data.", 'start': 1105.18, 'duration': 8.266}, {'end': 1114.767, 'text': "so that's what has happened here.", 'start': 1113.446, 'duration': 1.321}, {'end': 1122.834, 'text': 'so it displays the first few lines and then you can see what are the columns within that and what are the values in each of these cells and so on.', 'start': 1114.767, 'duration': 8.067}, {'end': 1127.958, 'text': 'and so you can also, typically you would like to see if there are any null values or are there any?', 'start': 1122.834, 'duration': 5.124}], 'summary': 'After importing data, check for null values and view sample data using df.head.', 'duration': 30.187, 'max_score': 1097.771, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk1097771.jpg'}, {'end': 1162.564, 'src': 'heatmap', 'start': 1122.834, 'weight': 1, 'content': [{'end': 1127.958, 'text': 'and so you can also, typically you would like to see if there are any null values or are there any?', 'start': 1122.834, 'duration': 5.124}, {'end': 1135.544, 'text': 'is the data, for whatever reason, is invalid or looking dirty for whatever reason, some unnecessary character?', 'start': 1127.958, 'duration': 7.586}, {'end': 1138.226, 'text': 'so this will give a quick view of that.', 'start': 1135.544, 'duration': 2.682}, {'end': 1141.509, 'text': 'so in this case pretty much everything looks okay.', 'start': 1138.226, 'duration': 3.283}, {'end': 1147.333, 'text': 'then the next step is to understand the data a little bit overall for each of the columns.', 'start': 1141.509, 'duration': 5.824}, {'end': 1148.634, 'text': 'what is the information?', 'start': 1147.333, 'duration': 1.301}, {'end': 1153.898, 'text': 'so the describe function is will basically give us a summary of the data.', 'start': 1148.634, 'duration': 5.264}, {'end': 1162.564, 'text': 'What else can we do? Pandas also allows us to visualize the data and this is more like a part of what we call it as univariate analysis.', 'start': 1154.038, 'duration': 8.526}], 'summary': 'Data analysis involves checking for null values, invalid data, and summary statistics using functions like describe and visualization with pandas for univariate analysis.', 'duration': 39.73, 'max_score': 1122.834, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk1122834.jpg'}, {'end': 1170.829, 'src': 'embed', 'start': 1138.226, 'weight': 2, 'content': [{'end': 1141.509, 'text': 'so in this case pretty much everything looks okay.', 'start': 1138.226, 'duration': 3.283}, {'end': 1147.333, 'text': 'then the next step is to understand the data a little bit overall for each of the columns.', 'start': 1141.509, 'duration': 5.824}, {'end': 1148.634, 'text': 'what is the information?', 'start': 1147.333, 'duration': 1.301}, {'end': 1153.898, 'text': 'so the describe function is will basically give us a summary of the data.', 'start': 1148.634, 'duration': 5.264}, {'end': 1162.564, 'text': 'What else can we do? Pandas also allows us to visualize the data and this is more like a part of what we call it as univariate analysis.', 'start': 1154.038, 'duration': 8.526}, {'end': 1170.829, 'text': 'That means each and every column you can take and do some plots and visualization to understand data in each of the columns.', 'start': 1162.644, 'duration': 8.185}], 'summary': 'Analyze data using describe function and visualize with pandas for univariate analysis.', 'duration': 32.603, 'max_score': 1138.226, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk1138226.jpg'}, {'end': 1365.35, 'src': 'embed', 'start': 1336.078, 'weight': 3, 'content': [{'end': 1345.023, 'text': 'some of them are going from Some columns are going from 0 to 100, 000 and some columns are just between 10 to 20, and so on.', 'start': 1336.078, 'duration': 8.945}, {'end': 1348.284, 'text': 'These will affect the accuracy of the analysis.', 'start': 1345.183, 'duration': 3.101}, {'end': 1352.506, 'text': 'So we need to do some kind of unifying the data and so on.', 'start': 1348.344, 'duration': 4.162}, {'end': 1356.487, 'text': 'So that is what data wrangling is all about.', 'start': 1352.546, 'duration': 3.941}, {'end': 1365.35, 'text': 'So, before we actually perform any analysis, we need to bring the data to some kind of a shape so that we can perform additional analysis,', 'start': 1356.567, 'duration': 8.783}], 'summary': 'Data wrangling involves unifying data of varying scales to ensure accurate analysis.', 'duration': 29.272, 'max_score': 1336.078, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk1336078.jpg'}], 'start': 932.585, 'title': 'Python exploratory data analysis', 'summary': 'Covers python program structure for exploratory data analysis, including importing libraries and data, visualizing with histograms, and data wrangling for cleaning and unifying data ranges, with examples and best practices provided.', 'chapters': [{'end': 1400.477, 'start': 932.585, 'title': 'Exploratory data analysis in python', 'summary': 'Covers the python program structure for exploratory data analysis, including the import of required libraries, data import, visualization using histograms, and the process of data wrangling, emphasizing the need for data cleaning and unifying data ranges before analysis, with examples and best practices provided.', 'duration': 467.892, 'highlights': ['The Python program structure involves importing required libraries such as pandas, numpy, and matplotlib, and including the code percentage matplotlib inline to display visualizations within the notebook. Importing necessary libraries like pandas, numpy, and matplotlib, and using the code percentage matplotlib inline for displaying visualizations within the notebook.', 'Importing data using the read_csv method from an external CSV file and utilizing the head method to display the first few lines of the data frame for initial assessment. Utilizing the read_csv method to import data from an external CSV file and using the head method to display the initial few lines of the data frame for assessment.', 'Utilizing the describe function to obtain a summary of the data and performing univariate analysis using histograms to visualize the distribution of values in each column. Using the describe function for obtaining a summary of the data and conducting univariate analysis through histograms to visualize the distribution of values in each column.', 'Emphasizing the need for data wrangling, which involves cleaning and unifying the data ranges, especially addressing missing values and disparate data ranges, to prepare for subsequent analysis and insights. Highlighting the importance of data wrangling to clean and unify data ranges, addressing missing values and disparate data ranges to prepare for further analysis and insights.']}], 'duration': 467.892, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk932585.jpg', 'highlights': ['Importing necessary libraries like pandas, numpy, and matplotlib, and using the code percentage matplotlib inline for displaying visualizations within the notebook.', 'Utilizing the read_csv method to import data from an external CSV file and using the head method to display the initial few lines of the data frame for assessment.', 'Using the describe function for obtaining a summary of the data and conducting univariate analysis through histograms to visualize the distribution of values in each column.', 'Highlighting the importance of data wrangling to clean and unify data ranges, addressing missing values and disparate data ranges to prepare for further analysis and insights.']}, {'end': 1826.18, 'segs': [{'end': 1454.946, 'src': 'embed', 'start': 1422.933, 'weight': 0, 'content': [{'end': 1427.878, 'text': 'And what we are saying is find out if a value is null and then you add all of them.', 'start': 1422.933, 'duration': 4.945}, {'end': 1432.142, 'text': 'How many observations are there where this particular column is null.', 'start': 1428.138, 'duration': 4.004}, {'end': 1434.024, 'text': 'So it does that for all the column.', 'start': 1432.322, 'duration': 1.702}, {'end': 1437.448, 'text': "So here you will see that for loan ID, obviously, it's an ID.", 'start': 1434.184, 'duration': 3.264}, {'end': 1439.65, 'text': 'So there are no null values or missing values.', 'start': 1437.508, 'duration': 2.142}, {'end': 1443.614, 'text': 'values. gender has about 13 observations where the values are missing.', 'start': 1439.65, 'duration': 3.964}, {'end': 1446.497, 'text': 'similarly, marital status has three, and so on and so forth.', 'start': 1443.614, 'duration': 2.883}, {'end': 1454.946, 'text': "so we'll see here for example, loan amount has 21 observations where the values are missing, loan amount term has 14 observations and so on.", 'start': 1446.497, 'duration': 8.449}], 'summary': 'Identified null values in columns: loan id: 0, gender: 13, marital status: 3, loan amount: 21, loan amount term: 14', 'duration': 32.013, 'max_score': 1422.933, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk1422933.jpg'}, {'end': 1513.117, 'src': 'embed', 'start': 1483.119, 'weight': 1, 'content': [{'end': 1486.761, 'text': 'it may not be worth doing something to fill up those values.', 'start': 1483.119, 'duration': 3.642}, {'end': 1490.503, 'text': 'it may be better off to get rid of those observations right.', 'start': 1486.761, 'duration': 3.742}, {'end': 1497.608, 'text': 'so that is, the missing values are proportionately very small, but if there are relatively large number of missing values,', 'start': 1490.503, 'duration': 7.105}, {'end': 1503.631, 'text': 'if you exclude those observations, then your accuracy may not be that very good.', 'start': 1497.608, 'duration': 6.023}, {'end': 1513.117, 'text': 'so the other way of doing it is we can take a mean value for a particular column and fill up wherever there are missing values,', 'start': 1503.631, 'duration': 9.486}], 'summary': 'Consider excluding observations with large missing values or filling missing values with mean for better accuracy.', 'duration': 29.998, 'max_score': 1483.119, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk1483119.jpg'}, {'end': 1553.556, 'src': 'embed', 'start': 1529.328, 'weight': 2, 'content': [{'end': 1537.336, 'text': 'it can be case to case and you may have to take a call based on your specific situation, but these are some of the common method.', 'start': 1529.328, 'duration': 8.008}, {'end': 1544.106, 'text': 'if you see, in the previous case, loan amount had 21 and now we went ahead and filled all of those with the mean value.', 'start': 1537.336, 'duration': 6.77}, {'end': 1546.749, 'text': 'so now there are zero with missing values.', 'start': 1544.106, 'duration': 2.643}, {'end': 1549.874, 'text': 'okay. so this is one part of a data wrangling activity.', 'start': 1546.749, 'duration': 3.125}, {'end': 1550.915, 'text': 'What else you can do.', 'start': 1550.114, 'duration': 0.801}, {'end': 1553.556, 'text': 'you can also check what are the types of the data.', 'start': 1550.915, 'duration': 2.641}], 'summary': 'Data wrangling involves filling missing loan amounts with mean value, resulting in zero missing values. it also includes checking data types.', 'duration': 24.228, 'max_score': 1529.328, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk1529328.jpg'}, {'end': 1596.586, 'src': 'embed', 'start': 1569.326, 'weight': 3, 'content': [{'end': 1576.391, 'text': 'it will actually perform or display or calculate the mean for pretty much all the numerical columns that are available in there.', 'start': 1569.326, 'duration': 7.065}, {'end': 1581.475, 'text': 'So, for example, here applicant income, co-applicant income and all these are numerical values.', 'start': 1576.851, 'duration': 4.624}, {'end': 1584.717, 'text': 'So, it will display the mean values of all of those.', 'start': 1581.615, 'duration': 3.102}, {'end': 1589.781, 'text': 'Now, another thing that you can do is you can actually also combine data frames.', 'start': 1584.977, 'duration': 4.804}, {'end': 1596.586, 'text': "So, let's say you import data from one CSV file into one data frame and another CSV file into another data frame.", 'start': 1589.841, 'duration': 6.745}], 'summary': 'The script calculates mean for numerical columns and can combine different data frames.', 'duration': 27.26, 'max_score': 1569.326, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk1569326.jpg'}, {'end': 1708.008, 'src': 'embed', 'start': 1683.719, 'weight': 4, 'content': [{'end': 1690.442, 'text': "Like I said, this could be let's say sales data coming for 12 different months, but each of the files has the same structure.", 'start': 1683.719, 'duration': 6.723}, {'end': 1694.903, 'text': 'So now you can combine all of them merge all of them using the concat method.', 'start': 1690.722, 'duration': 4.181}, {'end': 1700.145, 'text': "If we have, let's say, structure is not identical, then what will happen?", 'start': 1695.163, 'duration': 4.982}, {'end': 1708.008, 'text': "let's say we have these two data frames one has a column by the name key and the second column is lval,", 'start': 1700.145, 'duration': 7.863}], 'summary': 'Combine 12 months of sales data with the same structure using the concat method.', 'duration': 24.289, 'max_score': 1683.719, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk1683719.jpg'}], 'start': 1400.717, 'title': 'Data handling and analysis', 'summary': 'Discusses identifying and handling missing values, demonstrating the use of lambda functions for null value counts, and suggests methods such as deletion or mean value imputation. it also covers data types retrieval, mean calculation, and combining data frames using merge and concatenate methods.', 'chapters': [{'end': 1550.915, 'start': 1400.717, 'title': 'Handling missing values in data', 'summary': 'Discusses identifying and handling missing values in a dataset, demonstrating the use of lambda functions to count null values for each column and suggesting methods such as deletion or mean value imputation based on the proportion of missing values.', 'duration': 150.198, 'highlights': ['The chapter demonstrates the use of lambda functions to count null values for each column, revealing that loan amount has 21 missing observations, followed by loan amount term with 14 missing observations and gender with 13 missing observations.', 'The chapter highlights the methods of handling missing values, suggesting that if the proportion of missing values is small compared to the total number of observations, it may be better to exclude those records, while for a relatively large number of missing values, mean value imputation can be used to fill up those observations.', 'The chapter emphasizes the importance of considering the specific situation when deciding on the method to handle missing values, and illustrates the impact of mean value imputation by showing that after filling loan amount missing values with the mean value, there are zero missing values for loan amount.']}, {'end': 1826.18, 'start': 1550.915, 'title': 'Data manipulation and analysis', 'summary': 'Covers data types retrieval, mean calculation for numerical columns, and combining data frames using merge and concatenate methods, with an emphasis on maintaining identical structure for concatenation and utilizing common columns for merging.', 'duration': 275.265, 'highlights': ['The chapter covers data types retrieval, mean calculation for numerical columns, and combining data frames using merge and concatenate methods. Data types retrieval, mean calculation, combining data frames', 'Emphasizes maintaining identical structure for concatenation and utilizing common columns for merging. Importance of maintaining identical structure, Utilizing common columns for merging']}], 'duration': 425.463, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk1400717.jpg', 'highlights': ['Demonstrates use of lambda functions to count null values for each column, revealing loan amount has 21 missing observations, loan amount term has 14 missing observations, and gender has 13 missing observations.', 'Suggests methods for handling missing values based on the proportion of missing values compared to the total number of observations, recommending exclusion for small proportions and mean value imputation for relatively large proportions.', 'Emphasizes the importance of considering the specific situation when deciding on the method to handle missing values and illustrates the impact of mean value imputation by showing zero missing values for loan amount after filling with the mean value.', 'Covers data types retrieval, mean calculation for numerical columns, and combining data frames using merge and concatenate methods.', 'Emphasizes maintaining identical structure for concatenation and utilizing common columns for merging.']}, {'end': 2903.034, 'segs': [{'end': 1854.172, 'src': 'embed', 'start': 1826.42, 'weight': 0, 'content': [{'end': 1829.601, 'text': 'Now, we will talk a little bit about scikit-learn.', 'start': 1826.42, 'duration': 3.181}, {'end': 1836.543, 'text': 'So scikit-learn is a library which is used for doing machine learning or for performing machine learning activities.', 'start': 1829.701, 'duration': 6.842}, {'end': 1845.126, 'text': 'So, if you want to do linear regression, logistic regression and so on, there are easily usable APIs that you can call,', 'start': 1836.803, 'duration': 8.323}, {'end': 1848.327, 'text': "and that's the advantage of scikit-learn.", 'start': 1845.126, 'duration': 3.201}, {'end': 1850.969, 'text': 'and it provides a bunch of algorithms.', 'start': 1848.587, 'duration': 2.382}, {'end': 1854.172, 'text': 'so I think that is the good part about this library.', 'start': 1850.969, 'duration': 3.203}], 'summary': 'Scikit-learn is a machine learning library with easily usable apis and a variety of algorithms.', 'duration': 27.752, 'max_score': 1826.42, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk1826420.jpg'}, {'end': 1973.35, 'src': 'embed', 'start': 1928.309, 'weight': 1, 'content': [{'end': 1936.592, 'text': 'some people do it like 50-50, some people do it 80-20, which is training is 80 and test is 20, and so on.', 'start': 1928.309, 'duration': 8.283}, {'end': 1938.033, 'text': 'So it is individual preference.', 'start': 1936.612, 'duration': 1.421}, {'end': 1939.633, 'text': 'There are no hard and fast rules.', 'start': 1938.053, 'duration': 1.58}, {'end': 1944.215, 'text': 'By and large, we have seen that training data set is larger than the test data set.', 'start': 1939.873, 'duration': 4.342}, {'end': 1951.098, 'text': "And again, we will probably not go into details of why do we do this at this point, but that's one of the steps in machine learning.", 'start': 1944.255, 'duration': 6.843}, {'end': 1957.882, 'text': 'So scikit-learn offers a readily available method to do this, which is train test split.', 'start': 1951.178, 'duration': 6.704}, {'end': 1966.987, 'text': "Alright. so in this example, let's say, we are taking the values x and y are our values, x is the independent variables and y is our dependent variable.", 'start': 1957.942, 'duration': 9.045}, {'end': 1973.35, 'text': 'Okay, and we are using these two and then I want to split this into train and test data.', 'start': 1967.247, 'duration': 6.103}], 'summary': 'In machine learning, training data set is usually larger than the test data set, and scikit-learn offers a method called train test split.', 'duration': 45.041, 'max_score': 1928.309, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk1928309.jpg'}, {'end': 2118.255, 'src': 'embed', 'start': 2089.619, 'weight': 3, 'content': [{'end': 2094.88, 'text': 'So logistic regression is for classification and usually it is binary classification.', 'start': 2089.619, 'duration': 5.261}, {'end': 2097.383, 'text': 'So binary classification means there are two classes.', 'start': 2094.922, 'duration': 2.461}, {'end': 2102.004, 'text': 'So either like a yes, no or for example, customer will buy or will not buy.', 'start': 2097.503, 'duration': 4.501}, {'end': 2103.285, 'text': 'So that is a binary classification.', 'start': 2102.125, 'duration': 1.16}, {'end': 2105.767, 'text': "So that's where we use logistic regression.", 'start': 2103.465, 'duration': 2.302}, {'end': 2110.35, 'text': "So let's take a look at the code, how to implement something like that using scikit-learn.", 'start': 2105.867, 'duration': 4.483}, {'end': 2118.255, 'text': 'So the first thing is to import this logistic regression sub module or subclass, whatever you call it, and then create an instance of that.', 'start': 2110.47, 'duration': 7.785}], 'summary': 'Logistic regression is used for binary classification, such as predicting customer purchase behavior.', 'duration': 28.636, 'max_score': 2089.619, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk2089619.jpg'}, {'end': 2186, 'src': 'embed', 'start': 2153.602, 'weight': 4, 'content': [{'end': 2158.626, 'text': 'there is no method like train here, but we call what is known, as there is a method called fit.', 'start': 2153.602, 'duration': 5.024}, {'end': 2163.929, 'text': 'So you are basically by calling the fit method, you are training this model.', 'start': 2158.826, 'duration': 5.103}, {'end': 2168.451, 'text': 'And in order to train the model, you need to pass the training data set.', 'start': 2164.289, 'duration': 4.162}, {'end': 2177.215, 'text': 'So, x underscore train is your independent variables, the set of independent variables and y underscore train is your dependent variable or the label.', 'start': 2168.571, 'duration': 8.644}, {'end': 2186, 'text': 'So, you pass both of these and call the fit function or fit method, which will actually result in the training of this model classifier.', 'start': 2177.396, 'duration': 8.604}], 'summary': 'Using fit method, train model with x_train and y_train.', 'duration': 32.398, 'max_score': 2153.602, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk2153602.jpg'}, {'end': 2244.743, 'src': 'embed', 'start': 2220.754, 'weight': 5, 'content': [{'end': 2227.477, 'text': 'in order to test our data, we have to actually call what is known as the method known as predict right?', 'start': 2220.754, 'duration': 6.723}, {'end': 2230.038, 'text': 'So here this is where the training is done.', 'start': 2227.537, 'duration': 2.501}, {'end': 2233.46, 'text': "Now is the time for inference, isn't it? So we have the model.", 'start': 2230.198, 'duration': 3.262}, {'end': 2236.861, 'text': 'Now we want to check whether our model is working correctly or not.', 'start': 2233.7, 'duration': 3.161}, {'end': 2239.401, 'text': 'So what do you do? You have your test data.', 'start': 2236.941, 'duration': 2.46}, {'end': 2244.743, 'text': 'Remember, we split it 25% of our data was stored here, right? We split it into test and training.', 'start': 2239.521, 'duration': 5.222}], 'summary': 'To test the data, we use the predict method after training and split 25% for testing.', 'duration': 23.989, 'max_score': 2220.754, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk2220754.jpg'}, {'end': 2450.497, 'src': 'embed', 'start': 2424.639, 'weight': 6, 'content': [{'end': 2430.065, 'text': 'So there are two things that we can do from a confusion matrix or that we can calculate from a confusion matrix.', 'start': 2424.639, 'duration': 5.426}, {'end': 2432.448, 'text': 'One is the accuracy and the other is the precision.', 'start': 2430.205, 'duration': 2.243}, {'end': 2439.571, 'text': 'What is the accuracy? Accuracy is basically a measure of how many of the observations have been correctly predicted.', 'start': 2432.688, 'duration': 6.883}, {'end': 2445.294, 'text': "Okay, so let's say this is a little bit more detailed view of the confusion matrix.", 'start': 2439.732, 'duration': 5.562}, {'end': 2450.497, 'text': 'It looks very similar like as we saw in this case, right? So this is a two by two matrix.', 'start': 2445.514, 'duration': 4.983}], 'summary': 'Confusion matrix can be used to calculate accuracy and precision in predictions.', 'duration': 25.858, 'max_score': 2424.639, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk2424639.jpg'}, {'end': 2844.337, 'src': 'embed', 'start': 2819.754, 'weight': 7, 'content': [{'end': 2827.062, 'text': 'the accuracy calculation was manual, but we can also use some libraries which are already existing and the functions within that library.', 'start': 2819.754, 'duration': 7.308}, {'end': 2830.285, 'text': 'So scikit-learn provides one such method.', 'start': 2827.122, 'duration': 3.163}, {'end': 2833.348, 'text': 'So for example, accuracy underscore score is one such method.', 'start': 2830.345, 'duration': 3.003}, {'end': 2841.356, 'text': 'So if you use that and pass your test and predicted values, only the y you need to pass, the dependent variable values.', 'start': 2833.408, 'duration': 7.948}, {'end': 2844.337, 'text': 'So if you pass that, it will calculate it for you.', 'start': 2841.556, 'duration': 2.781}], 'summary': "Manual accuracy calculation can be replaced with scikit-learn's accuracy_score method for faster, more efficient calculations.", 'duration': 24.583, 'max_score': 2819.754, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk2819754.jpg'}], 'start': 1826.42, 'title': 'Implementing scikit-learn for machine learning', 'summary': 'Introduces scikit-learn and its ease of use for various algorithms, explains logistic regression implementation for binary classification, and delves into evaluating model accuracy and precision with an 80% accuracy and precision using a 2x2 matrix and 150 observations.', 'chapters': [{'end': 2062.222, 'start': 1826.42, 'title': 'Introduction to scikit-learn', 'summary': 'Introduces scikit-learn, a machine learning library, highlighting its ease of use for various algorithms, the process of splitting data for training and testing, and the method train_test_split for achieving this with specified test sizes.', 'duration': 235.802, 'highlights': ['Scikit-learn provides easily usable APIs for various machine learning algorithms like linear regression, logistic regression, and random forest classification. It offers a range of algorithms such as linear regression, logistic regression, and random forest classification, enhancing the ease of use with readily available APIs.', 'The process of splitting labeled data into training and test sets is an essential step in machine learning, with different preferences such as 50-50, 80-20 splits. Different preferences exist for splitting data into training and test sets, with common splits like 50-50, 80-20, and variations, without strict rules, emphasizing the need for a larger training dataset.', 'The train_test_split method in scikit-learn allows for the easy splitting of data into training and test sets, specifying the size of the test data with a specified ratio. The train_test_split method in scikit-learn enables the straightforward division of data into training and test sets, with the ability to specify the size of the test data, maintaining the norm of having a larger training dataset.']}, {'end': 2342.56, 'start': 2062.302, 'title': 'Implementing logistic regression for binary classification', 'summary': 'Explains the implementation of logistic regression for binary classification using scikit-learn, focusing on model training, testing, and prediction accuracy.', 'duration': 280.258, 'highlights': ["Logistic regression is an algorithm for supervised learning for performing classification, usually for binary classification. Logistic regression is used for binary classification, distinguishing between two classes, such as yes/no or buy/don't buy.", 'The model is trained using the fit method, where the training data set (x_train and y_train) is passed to the model. Model training involves passing the set of independent variables (x_train) and the dependent variable or label (y_train) to the fit method.', 'Testing the model involves using the test data set (x_test) to make predictions with the predict method, and then comparing the predicted values (y_predict) with the actual labels to calculate accuracy. Testing the model includes using the predict method with the test data set (x_test) to generate predicted values (y_predict) for comparison with actual labels, assessing model accuracy.']}, {'end': 2903.034, 'start': 2342.56, 'title': 'Model accuracy and precision', 'summary': "Explains the concept of confusion matrix, which is used to evaluate the accuracy and precision of a machine learning model, and provides a detailed breakdown of how to calculate accuracy (80%) and precision (80%) using a 2x2 matrix, with a total of 150 observations, and also demonstrates the use of scikit-learn's accuracy_score method.", 'duration': 560.474, 'highlights': ['Confusion matrix is used to evaluate the accuracy and precision of a machine learning model by comparing predicted and actual values, with a detailed breakdown of the 2x2 matrix, and a demonstration of calculating accuracy (80%) and precision (80%) using a total of 150 observations. The confusion matrix is a key tool for evaluating the performance of a machine learning model, providing a detailed breakdown of predicted and actual values and demonstrating the calculation of accuracy and precision.', "Calculation of accuracy (80%) and precision (80%) using a 2x2 matrix, with a total of 150 observations, and demonstration of the use of scikit-learn's accuracy_score method for the same calculation. The detailed calculation of accuracy and precision using a 2x2 matrix with 150 observations, along with the demonstration of using scikit-learn's accuracy_score method, showcases the practical application of these concepts."]}], 'duration': 1076.614, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk1826420.jpg', 'highlights': ['Scikit-learn offers easily usable APIs for various machine learning algorithms, enhancing ease of use with readily available APIs.', 'Different preferences exist for splitting data into training and test sets, with common splits like 50-50, 80-20, and variations, emphasizing the need for a larger training dataset.', 'The train_test_split method in scikit-learn enables the straightforward division of data into training and test sets, maintaining the norm of having a larger training dataset.', "Logistic regression is used for binary classification, distinguishing between two classes, such as yes/no or buy/don't buy.", 'Model training involves passing the set of independent variables (x_train) and the dependent variable or label (y_train) to the fit method.', 'Testing the model includes using the predict method with the test data set (x_test) to generate predicted values (y_predict) for comparison with actual labels, assessing model accuracy.', 'The confusion matrix is a key tool for evaluating the performance of a machine learning model, providing a detailed breakdown of predicted and actual values and demonstrating the calculation of accuracy and precision.', "The detailed calculation of accuracy and precision using a 2x2 matrix with 150 observations, along with the demonstration of using scikit-learn's accuracy_score method, showcases the practical application of these concepts."]}, {'end': 3409.45, 'segs': [{'end': 2927.691, 'src': 'embed', 'start': 2903.034, 'weight': 0, 'content': [{'end': 2912.822, 'text': 'recall here we have pandas, we have numpy and for visualization we have matplotlib, and this line is basically reading the csv file.', 'start': 2903.034, 'duration': 9.788}, {'end': 2919.786, 'text': "so we have the csv file locally on our local drive, and this is where i'm checking the data.", 'start': 2912.822, 'duration': 6.964}, {'end': 2923.649, 'text': "just so i'm starting with my exploratory analysis how the data is looking.", 'start': 2919.786, 'duration': 3.863}, {'end': 2924.609, 'text': 'so it looks good.', 'start': 2923.649, 'duration': 0.96}, {'end': 2927.691, 'text': 'there are no major missing values or anything like that.', 'start': 2924.609, 'duration': 3.082}], 'summary': 'Using pandas, numpy, and matplotlib for data analysis. no major missing values found.', 'duration': 24.657, 'max_score': 2903.034, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk2903034.jpg'}, {'end': 2992.934, 'src': 'embed', 'start': 2967.966, 'weight': 1, 'content': [{'end': 2976.069, 'text': "so in this case i'm taking a look at the loan amount and if i create the histogram, it displays the data here in the form of a histogram.", 'start': 2967.966, 'duration': 8.103}, {'end': 2982.571, 'text': 'One thing that we gather from this, as I mentioned in the slides, as well as how the data is kind of scattered.', 'start': 2976.289, 'duration': 6.282}, {'end': 2989.673, 'text': 'So while most of the values are in this range, 0 to 300 range, there are a few extreme values around the 700 range.', 'start': 2982.651, 'duration': 7.022}, {'end': 2992.934, 'text': 'So that is one information we get from this histogram.', 'start': 2989.833, 'duration': 3.101}], 'summary': 'Loan amounts are mostly in the 0 to 300 range, with a few extreme values around 700.', 'duration': 24.968, 'max_score': 2967.966, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk2967966.jpg'}, {'end': 3076.783, 'src': 'embed', 'start': 3048.619, 'weight': 2, 'content': [{'end': 3053.942, 'text': 'So in this case, we will fill the missing values with the mean value of the loan amount.', 'start': 3048.619, 'duration': 5.323}, {'end': 3056.384, 'text': "So let's go ahead and do that.", 'start': 3054.122, 'duration': 2.262}, {'end': 3063.07, 'text': 'and now, if we check here now, loan amount number of missing values is zero, because what we did was,', 'start': 3056.384, 'duration': 6.686}, {'end': 3068.595, 'text': 'for all these 21 cells where the values were missing, we filled with the mean value of the loan amount.', 'start': 3063.07, 'duration': 5.525}, {'end': 3071.018, 'text': 'So now there are no more missing values for loan amount.', 'start': 3068.675, 'duration': 2.343}, {'end': 3076.783, 'text': 'We can do this for other columns as well but this was just one example so we have shown it here.', 'start': 3071.098, 'duration': 5.685}], 'summary': 'Filled missing loan amount values with mean, reducing missing values to zero.', 'duration': 28.164, 'max_score': 3048.619, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk3048619.jpg'}, {'end': 3191.179, 'src': 'embed', 'start': 3157.322, 'weight': 3, 'content': [{'end': 3161.464, 'text': 'As I mentioned in the during the slides, we use the train test split method.', 'start': 3157.322, 'duration': 4.142}, {'end': 3166.367, 'text': 'And when we call this and pass the independent variables and the dependent variables.', 'start': 3161.784, 'duration': 4.583}, {'end': 3174.37, 'text': 'And we specify the test size to be 0.25,, which means the training size will be 0.75,, which is nothing,', 'start': 3166.847, 'duration': 7.523}, {'end': 3182.493, 'text': 'but you split the data into training data set, which is 75%, and test data set, in which is 25%.', 'start': 3174.37, 'duration': 8.123}, {'end': 3191.179, 'text': 'So, once you split that, you will have all your independent variables data in x train, the training data, which is 75% of it.', 'start': 3182.493, 'duration': 8.686}], 'summary': 'Using train test split method with 25% test size to split data into 75% training and 25% test datasets.', 'duration': 33.857, 'max_score': 3157.322, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk3157322.jpg'}, {'end': 3234.854, 'src': 'embed', 'start': 3208.244, 'weight': 4, 'content': [{'end': 3213.746, 'text': 'Remember, we had some data which was kind of very scattered, there were some extreme values and so on.', 'start': 3208.244, 'duration': 5.502}, {'end': 3223.19, 'text': 'So this will take care of that, so that the data is normalized, so that before we pass to our algorithm, the data is normalized,', 'start': 3213.866, 'duration': 9.324}, {'end': 3224.971, 'text': 'so that the performance will be much better.', 'start': 3223.19, 'duration': 1.781}, {'end': 3229.653, 'text': 'The next step is to create the instance of logistic regression object.', 'start': 3225.191, 'duration': 4.462}, {'end': 3230.973, 'text': "So that's what we are doing here.", 'start': 3229.693, 'duration': 1.28}, {'end': 3234.854, 'text': 'So classifier is our logistic regression instance.', 'start': 3231.193, 'duration': 3.661}], 'summary': 'Normalize data to improve algorithm performance.', 'duration': 26.61, 'max_score': 3208.244, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk3208244.jpg'}, {'end': 3351.674, 'src': 'embed', 'start': 3320.99, 'weight': 5, 'content': [{'end': 3329.898, 'text': 'here So this is the confusion matrix and then you want to do the measure the accuracy you can directly use this method and we find that it is 80%.', 'start': 3320.99, 'duration': 8.908}, {'end': 3335.263, 'text': 'So we in the slides we have seen when we calculate manually as well, we get an accuracy of 80%.', 'start': 3329.898, 'duration': 5.365}, {'end': 3340.948, 'text': "Okay, let's go back to our slides and do a summary.", 'start': 3335.263, 'duration': 5.685}, {'end': 3342.049, 'text': 'So what we have done?', 'start': 3341.149, 'duration': 0.9}, {'end': 3351.674, 'text': 'in the session, we talked about what is data science and why Python is being used, why it is becoming so popular, how to install Python.', 'start': 3342.049, 'duration': 9.625}], 'summary': 'Accuracy of 80% achieved in measuring data science concepts and python popularity.', 'duration': 30.684, 'max_score': 3320.99, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk3320990.jpg'}], 'start': 2903.034, 'title': 'Data analysis, visualization, and logistic regression in python', 'summary': 'Demonstrates data analysis and visualization using python libraries such as pandas, numpy, and matplotlib. it includes handling missing values and performing logistic regression with a 75% training data set, achieving an 80% accuracy after exploratory data analysis and manipulation using python libraries like panda, scipy, and numpy.', 'chapters': [{'end': 3114.014, 'start': 2903.034, 'title': 'Data analysis and visualization with pandas and matplotlib', 'summary': 'Demonstrates data analysis and visualization using python libraries such as pandas, numpy, and matplotlib, including reading a csv file, exploring and visualizing the data, checking for missing values, and handling them by filling with mean values.', 'duration': 210.98, 'highlights': ['The chapter demonstrates reading a CSV file, exploring and visualizing the data, and checking for missing values. It covers the use of Pandas, NumPy, and Matplotlib for data analysis and visualization, including reading a CSV file, exploring and visualizing the data, and checking for missing values.', 'The data visualization includes histograms for loan amount and applicant income, revealing insights about the data distribution and extreme values. It demonstrates creating histograms for loan amount and applicant income, revealing insights about the data distribution and extreme values, such as the majority of loan amounts being in the range of 0 to 300 with a few extreme values around 700, and most applicant incomes being in the range of 0 to 20,000 with some outliers in the range of 65,000 to 80,000.', 'The chapter showcases checking for missing values and handling them by filling with the mean value, ensuring no more missing values for the loan amount. It showcases checking for missing values in different columns, filling missing values with the mean value of the loan amount, and ensuring no more missing values for the loan amount.']}, {'end': 3409.45, 'start': 3114.534, 'title': 'Logistic regression example', 'summary': 'Covers the process of performing logistic regression using a 75% training data set and achieving an accuracy of 80%, following exploratory data analysis and data manipulation using python libraries like panda, scipy, and numpy.', 'duration': 294.916, 'highlights': ['The process involves separating the data into training and test datasets with a 75% training size and a 25% test size, utilizing the train test split method, resulting in x_train, x_test, y_train, and y_test.', 'The data is normalized to handle extreme values before being passed to the logistic regression algorithm, ensuring better performance.', 'After training the model, the accuracy of 80% is measured using confusion matrix, aligning with the previously described manual calculation during the session.', "The session covers Python's popularity, installation, and libraries like Panda, SciPy, NumPy, along with examples of exploratory analysis, data manipulation, and logistic regression using scikit-learn library.", 'The session concludes with an invitation for queries and comments, as well as the option for viewers to provide their email IDs for further communication if needed.']}], 'duration': 506.416, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mkv5mxYu0Wk/pics/mkv5mxYu0Wk2903034.jpg', 'highlights': ['The chapter demonstrates reading a CSV file, exploring and visualizing the data, and checking for missing values using Pandas, NumPy, and Matplotlib.', 'The data visualization includes histograms for loan amount and applicant income, revealing insights about the data distribution and extreme values.', 'The chapter showcases checking for missing values and handling them by filling with the mean value, ensuring no more missing values for the loan amount.', 'The process involves separating the data into training and test datasets with a 75% training size and a 25% test size, utilizing the train test split method.', 'The data is normalized to handle extreme values before being passed to the logistic regression algorithm, ensuring better performance.', 'After training the model, the accuracy of 80% is measured using confusion matrix, aligning with the previously described manual calculation during the session.']}], 'highlights': ['The chapter provides an overview of data science with Python, covering the basics of Python, important libraries for data analysis, exploratory data analysis, and a logistic regression model, emphasizing the use of scikit-learn library and insights from data science.', "Python's growing popularity in data science, IoT, and AI is emphasized, making it the programming language of choice, especially for data science, due to its rich tools and open-source nature.", 'The importance of predicting customer visits to minimize food wastage is emphasized, showcasing the practical application of data science in business.', "The detailed calculation of accuracy and precision using a 2x2 matrix with 150 observations, along with the demonstration of using scikit-learn's accuracy_score method, showcases the practical application of these concepts.", 'The chapter demonstrates reading a CSV file, exploring and visualizing the data, and checking for missing values using Pandas, NumPy, and Matplotlib.']}