title
Decision Tree In Machine Learning | Decision Tree Algorithm In Python |Machine Learning |Simplilearn

description
🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=23AugustTubebuddyExpPCPAIandML&utm_medium=DescriptionFF&utm_source=youtube 🔥AI Engineer Masters Program (Discount Code - YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=SCE-AIMasters&utm_medium=DescriptionFF&utm_source=youtube 🔥AI & Machine Learning Bootcamp(US Only): https://www.simplilearn.com/ai-machine-learning-bootcamp?utm_campaign=MachineLearning-RmajweUFKvM&utm_medium=Descriptionff&utm_source=youtube 🔥 Purdue Post Graduate Program In AI And Machine Learning: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=MachineLearning-RmajweUFKvM&utm_medium=Descriptionff&utm_source=youtube This Decision Tree in the Machine Learning tutorial will help you understand all the basics of the Decision Tree and how the Decision Tree algorithm works. In the end, we will implement a Decision Tree algorithm in Python on loan payment prediction. This Decision Tree tutorial is ideal for both beginners as well as professionals who want to learn Machine Learning Algorithms. Below topics are covered in this Decision Tree Algorithm Tutorial: 0. Intro (0:00) 1. What is Machine Learning? ( 02:25 ) 2. Types of Machine Learning? ( 03:27 ) 3. Problems in Machine Learning ( 04:43 ) 4. What is a Decision Tree? ( 06:29 ) 5. What are the problems a Decision Tree Solves? ( 07:11 ) 6. Advantages of Decision Tree ( 07:54 ) 7. How does Decision Tree Work? ( 10:55 ) 8. Use Case - Loan Repayment Prediction ( 14:32 ) Dataset Link - https://drive.google.com/drive/folders/1JybOOdRsMYH0z8fehVuFNLhZ60UsO-4W ✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH ⏩ Check out the Machine Learning tutorial videos: https://bit.ly/3fFR4f4 Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Decision-Tree-Algorithm-With-Example-RmajweUFKvM&utm_medium=Tutorials&utm_source=youtube 👉Learn more at: https://bit.ly/3fouyY0 #DecisionTreeMachineLearning #DecisionTree #DecisionTreeAlgorithm #DecisionTreeAlgorithmInMachineLearning #DecisionTreePython #DecisionTrees #DecisionTreeExample #MachineLearningAlgorithms #MachineLearningTutorial #Simplilearn What is a Decision Tree Algorithm? A Decision Tree is a supervised machine learning algorithm for solving classification problems. Generally, a decision tree is drawn upside down with its root at the top and it is known as the Top-Down Approach. Decision trees are used for handling non-linear data sets effectively. The decision tree tool is used in real life in many areas, such as engineering, civil planning, law, and business. ➡️ About Post Graduate Program In AI And Machine Learning This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots. ✅ Key Features - Post Graduate Program certificate and Alumni Association membership - Exclusive hackathons and Ask me Anything sessions by IBM - 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies - Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more - Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools ✅ Skills Covered - ChatGPT - Generative AI - Explainable AI - Generative Modeling - Statistics - Python - Supervised Learning - Unsupervised Learning - NLP - Neural Networks - Computer Vision - And Many More… 👉 Learn More At: 👉Learn More at: https://www.simplilearn.com/pgp-data-science-certification-bootcamp-program?utm_campaign=MachineLearning-RmajweUFKvM&utm_medium=Description&utm_source=youtube 🔥 Enroll for FREE Machine Learning Course & Get your Completion Certificate: https://www.simplilearn.com/learn-machine-learning-basics-skillup?utm_campaign=MachineLearning&utm_medium=Description&utm_source=youtube 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688

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{'title': 'Decision Tree In Machine Learning | Decision Tree Algorithm In Python |Machine Learning |Simplilearn', 'heatmap': [{'end': 1063.08, 'start': 1037.887, 'weight': 1}], 'summary': 'Covers decision tree in machine learning, introducing basics, entropy calculation, debugging file errors in python, and machine learning implementation for loan repayment prediction with 94.6% accuracy.', 'chapters': [{'end': 42.028, 'segs': [{'end': 42.028, 'src': 'embed', 'start': 4.741, 'weight': 0, 'content': [{'end': 7.282, 'text': 'Welcome to the Decision Tree tutorial.', 'start': 4.741, 'duration': 2.541}, {'end': 10.223, 'text': 'My name is Richard Kirshner with SimplyLearn.', 'start': 7.522, 'duration': 2.701}, {'end': 12.824, 'text': "That's www.simplylearn.com.", 'start': 10.343, 'duration': 2.481}, {'end': 19.266, 'text': 'So the Decision Tree, one of the many powerful tools in the Machine Learning Library, begins with a problem.', 'start': 13.464, 'duration': 5.802}, {'end': 26.092, 'text': "I think I have to buy a So in making this question, you want to know, how do I decide which one to buy? And you're going to start asking questions.", 'start': 19.386, 'duration': 6.706}, {'end': 27.694, 'text': 'Is the mileage greater than 20?', 'start': 26.172, 'duration': 1.522}, {'end': 29.335, 'text': 'Is the price less than 15??', 'start': 27.694, 'duration': 1.641}, {'end': 31.478, 'text': 'Will it be sufficient for six people??', 'start': 29.335, 'duration': 2.143}, {'end': 33.74, 'text': 'Does it have enough airbags? Anti-lock brakes??', 'start': 31.498, 'duration': 2.242}, {'end': 35.201, 'text': 'All these questions come up.', 'start': 33.96, 'duration': 1.241}, {'end': 38.004, 'text': 'Then as we feed all this data in, we make a decision.', 'start': 35.242, 'duration': 2.762}, {'end': 41.248, 'text': 'And that decision comes up, oh, hey, this seems like a good idea.', 'start': 38.105, 'duration': 3.143}, {'end': 42.028, 'text': "Here's a car.", 'start': 41.408, 'duration': 0.62}], 'summary': 'Decision tree tutorial by richard kirshner explains how to make purchasing decisions based on specific criteria.', 'duration': 37.287, 'max_score': 4.741, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM4741.jpg'}], 'start': 4.741, 'title': 'Decision tree tutorial', 'summary': 'Introduces the decision tree as a powerful tool in the machine learning library, illustrating its process through the example of deciding on a car purchase based on criteria such as mileage, price, capacity, airbags, and anti-lock brakes.', 'chapters': [{'end': 42.028, 'start': 4.741, 'title': 'Decision tree tutorial', 'summary': 'Introduces the decision tree as a powerful tool in the machine learning library, illustrating its process through the example of deciding on a car purchase based on criteria such as mileage, price, capacity, airbags, and anti-lock brakes.', 'duration': 37.287, 'highlights': ['The Decision Tree is presented as a powerful tool in the Machine Learning Library, initiating with a problem and utilizing a series of questions to make a decision, such as in the case of determining a suitable car purchase based on specific criteria.', 'The process involves asking questions related to the mileage, price, capacity, airbags, and anti-lock brakes to feed data and make a decision.']}], 'duration': 37.287, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM4741.jpg', 'highlights': ['The Decision Tree is presented as a powerful tool in the Machine Learning Library, initiating with a problem and utilizing a series of questions to make a decision, such as in the case of determining a suitable car purchase based on specific criteria.', 'The process involves asking questions related to the mileage, price, capacity, airbags, and anti-lock brakes to feed data and make a decision.']}, {'end': 633.804, 'segs': [{'end': 97.035, 'src': 'embed', 'start': 74.857, 'weight': 3, 'content': [{'end': 84.468, 'text': "And then we'll go in and do a case loan repayment prediction where we actually are going to put together some Python code and show you the basic Python code for generating a decision tree.", 'start': 74.857, 'duration': 9.611}, {'end': 91.472, 'text': "What is machine learning? There are so many different ways to describe what is machine learning in today's world and illustrate it.", 'start': 85.169, 'duration': 6.303}, {'end': 97.035, 'text': "We're going to take a graphic here and making decisions or trying to understand what's going on,", 'start': 91.572, 'duration': 5.463}], 'summary': 'Python code for case loan repayment prediction and understanding machine learning.', 'duration': 22.178, 'max_score': 74.857, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM74857.jpg'}, {'end': 158.331, 'src': 'embed', 'start': 127.054, 'weight': 1, 'content': [{'end': 129.876, 'text': 'These are the most basic three premises of machine learning.', 'start': 127.054, 'duration': 2.822}, {'end': 135.538, 'text': "In learning, we can describe the data in new ways and be able to learn new aspects about what we're looking at.", 'start': 130.136, 'duration': 5.402}, {'end': 140.341, 'text': 'And then we can use that to predict things, and we can use that to make decisions.', 'start': 135.598, 'duration': 4.743}, {'end': 145.103, 'text': "So maybe it's something that's never happened before, but we can make a good guess whether it's going to be a good investment or not.", 'start': 140.541, 'duration': 4.562}, {'end': 150.986, 'text': "It also helps us categorize stuff so we can remember it better, so it's easier to pull it out of the catalog.", 'start': 145.484, 'duration': 5.502}, {'end': 153.768, 'text': 'We can analyze data in new ways we never thought possible.', 'start': 151.026, 'duration': 2.742}, {'end': 158.331, 'text': "And then, of course, there's the very large growing industry of recognize.", 'start': 154.048, 'duration': 4.283}], 'summary': 'Machine learning helps analyze data, predict outcomes, make decisions, and categorize information, contributing to a growing industry.', 'duration': 31.277, 'max_score': 127.054, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM127054.jpg'}, {'end': 192.133, 'src': 'embed', 'start': 165.716, 'weight': 0, 'content': [{'end': 171.119, 'text': "Going back to our guy here who's in his ordinary system and would like to be smarter, make better choices.", 'start': 165.716, 'duration': 5.403}, {'end': 180.406, 'text': 'What happens with machine learning is an application of artificial intelligence wherein the system gets the ability to automatically learn and improve based on experience.', 'start': 171.3, 'duration': 9.106}, {'end': 192.133, 'text': "form of information coming in, and this is with the artificial intelligence, helps him see things he never saw or track things he can't track.", 'start': 184.148, 'duration': 7.985}], 'summary': 'Machine learning enables automatic learning and improvement based on experience, enhancing the ability to perceive and track information.', 'duration': 26.417, 'max_score': 165.716, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM165716.jpg'}, {'end': 240.185, 'src': 'embed', 'start': 216.388, 'weight': 2, 'content': [{'end': 224.814, 'text': "So if you worked at a bank you'd already have a list of all the previous loans and who defaulted on them and who made good payments on them.", 'start': 216.388, 'duration': 8.426}, {'end': 232.299, 'text': "You then program your machine learning tool and that lets you predict on the next person whether they're going to be able to make their payments or not on their loan.", 'start': 224.854, 'duration': 7.445}, {'end': 237.843, 'text': "If you have one category where you already know the answers, the next one would be you don't know the answers.", 'start': 232.399, 'duration': 5.444}, {'end': 240.185, 'text': 'You just have a lot of information coming in.', 'start': 238.083, 'duration': 2.102}], 'summary': 'Banks use machine learning to predict loan repayments based on previous data.', 'duration': 23.797, 'max_score': 216.388, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM216388.jpg'}, {'end': 362.381, 'src': 'embed', 'start': 334.785, 'weight': 4, 'content': [{'end': 340.792, 'text': 'The most commonly used for the decision tree is for classification, for figuring out is it red or is it not.', 'start': 334.785, 'duration': 6.007}, {'end': 343.133, 'text': 'Is it a fruit or is it a vegetable?', 'start': 341.252, 'duration': 1.881}, {'end': 343.913, 'text': 'Yes or no?', 'start': 343.273, 'duration': 0.64}, {'end': 344.773, 'text': 'True or false?', 'start': 344.093, 'duration': 0.68}, {'end': 345.954, 'text': 'Left or right?', 'start': 345.334, 'duration': 0.62}, {'end': 346.754, 'text': 'Zero or one?', 'start': 346.054, 'duration': 0.7}, {'end': 350.896, 'text': "And so, when we talk about classification, we're going to look at the basic machine learning.", 'start': 346.874, 'duration': 4.022}, {'end': 353.577, 'text': 'These are the four main tools used in classification.', 'start': 350.916, 'duration': 2.661}, {'end': 358.679, 'text': "There's the knave Bayes, logistic regression, decision tree, and random forest.", 'start': 353.697, 'duration': 4.982}, {'end': 362.381, 'text': 'The first two are for simpler data.', 'start': 358.979, 'duration': 3.402}], 'summary': 'Decision tree is commonly used for classification, along with knave bayes, logistic regression, and random forest, for simpler data.', 'duration': 27.596, 'max_score': 334.785, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM334785.jpg'}, {'end': 582.522, 'src': 'embed', 'start': 556.604, 'weight': 5, 'content': [{'end': 562.048, 'text': "We need to have some definitions to go with our decision tree and the different parts we're going to be using.", 'start': 556.604, 'duration': 5.444}, {'end': 563.249, 'text': "We'll start with entropy.", 'start': 562.108, 'duration': 1.141}, {'end': 567.732, 'text': 'Entropy is a measure of randomness or unpredictability in the data set.', 'start': 563.669, 'duration': 4.063}, {'end': 570.314, 'text': 'For example, we have a group of animals.', 'start': 568.072, 'duration': 2.242}, {'end': 572.615, 'text': "In this picture, there's four different kinds of animals.", 'start': 570.634, 'duration': 1.981}, {'end': 575.697, 'text': 'And this data set is considered to have a high entropy.', 'start': 572.775, 'duration': 2.922}, {'end': 579.84, 'text': "You really can't pick out what kind of animal it is based on looking at just the four animals.", 'start': 575.757, 'duration': 4.083}, {'end': 582.522, 'text': 'as a big clump of entities.', 'start': 580.4, 'duration': 2.122}], 'summary': 'Entropy measures randomness in a data set, as seen in a group of four animals with high entropy.', 'duration': 25.918, 'max_score': 556.604, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM556604.jpg'}], 'start': 42.189, 'title': 'Machine learning basics and decision tree fundamentals', 'summary': 'Introduces the basics of machine learning, including the three fundamental premises of learn, predict, decide, and the three primary types of learning: supervised, unsupervised, and reinforcement learning. it also covers the basics of decision trees, including its application in classification and regression problems, with a focus on loan repayment prediction using python code, and introduces important terms such as entropy and information gain.', 'chapters': [{'end': 232.299, 'start': 42.189, 'title': 'Machine learning basics', 'summary': 'Introduces the basics of machine learning, including the three fundamental premises of machine learning: learn, predict, decide, and the three primary types of learning: supervised, unsupervised, and reinforcement learning, with a focus on decision trees and loan repayment prediction using python code.', 'duration': 190.11, 'highlights': ['Machine learning is a part of artificial intelligence and involves the ability to automatically learn and improve based on experience, enabling the system to make smarter choices with less work. Machine learning is a part of artificial intelligence and involves the ability to automatically learn and improve based on experience, enabling the system to make smarter choices with less work.', 'The three fundamental premises of machine learning are learn, predict, decide, which enable the system to describe data in new ways, make predictions, and make decisions. The three fundamental premises of machine learning are learn, predict, decide, which enable the system to describe data in new ways, make predictions, and make decisions.', 'The three primary types of learning are supervised, unsupervised, and reinforcement learning, with supervised learning involving using existing data to predict outcomes, such as loan repayment prediction at a bank. The three primary types of learning are supervised, unsupervised, and reinforcement learning, with supervised learning involving using existing data to predict outcomes, such as loan repayment prediction at a bank.', 'The chapter also covers the basics of decision trees and includes a case study on loan repayment prediction using Python code. The chapter also covers the basics of decision trees and includes a case study on loan repayment prediction using Python code.']}, {'end': 633.804, 'start': 232.399, 'title': 'Types of machine learning & decision tree basics', 'summary': 'Covers the three types of machine learning: supervised, unsupervised, and reinforcement learning, as well as the basics of decision trees, including its application in classification and regression problems. it also discusses the advantages and disadvantages of decision trees and introduces important terms such as entropy and information gain.', 'duration': 401.405, 'highlights': ['Decision tree is a part of the third type of machine learning, reinforcement learning, which adjusts based on feedback, and it is commonly used for classification problems with categorical solutions. Reinforcement learning involves adjusting based on feedback, and decision trees are commonly used for classification problems with categorical solutions.', 'The primary machine learning problems fall under classification, regression, and clustering, and decision trees are commonly used for classification to determine if an item belongs to a specific group or to solve problems with yes/no, true/false, or one/zero solutions. Decision trees are commonly used for classification problems with categorical solutions.', 'Decision tree is simple to understand, interpret, and visualize; requires little effort for data preparation; and can handle both numerical and categorical data, making it effective for decision making and prediction. Decision trees are simple to understand, interpret, and visualize, and can handle both numerical and categorical data effectively.', 'Decision tree has drawbacks such as overfitting, high variance, and low bias, which can lead to capturing noise in the data, model instability, and difficulty in working with new data. Decision trees have drawbacks like overfitting, high variance, and low bias, which can impact model stability and performance.', 'Important terms related to decision trees include entropy, which measures randomness or unpredictability in a dataset, and information gain, which measures the decrease in entropy after the dataset is split. Important terms related to decision trees include entropy and information gain, which are measures of randomness and decrease in entropy, respectively.']}], 'duration': 591.615, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM42189.jpg', 'highlights': ['Machine learning enables systems to automatically learn and improve based on experience, making smarter choices with less work.', 'The three fundamental premises of machine learning are learn, predict, decide, enabling the system to describe data in new ways, make predictions, and make decisions.', 'Supervised learning involves using existing data to predict outcomes, such as loan repayment prediction at a bank.', 'The chapter covers the basics of decision trees and includes a case study on loan repayment prediction using Python code.', 'Decision trees are commonly used for classification problems with categorical solutions and are simple to understand, interpret, and visualize.', 'Important terms related to decision trees include entropy and information gain, which are measures of randomness and decrease in entropy, respectively.']}, {'end': 1027.462, 'segs': [{'end': 664.23, 'src': 'embed', 'start': 633.884, 'weight': 1, 'content': [{'end': 637.447, 'text': 'Finally, we want to know the different parts of our tree, and they call the leaf node.', 'start': 633.884, 'duration': 3.563}, {'end': 642.331, 'text': "Leaf node carries the classification or the decision, so it's the final end at the bottom.", 'start': 637.627, 'duration': 4.704}, {'end': 645.294, 'text': 'The decision node has two or more branches.', 'start': 642.552, 'duration': 2.742}, {'end': 648.177, 'text': "This is where we're breaking the group up into different parts.", 'start': 645.614, 'duration': 2.563}, {'end': 650.239, 'text': 'And finally, you have the root node.', 'start': 648.517, 'duration': 1.722}, {'end': 653.522, 'text': 'The topmost decision node is known as the root node.', 'start': 650.419, 'duration': 3.103}, {'end': 656.687, 'text': 'How does a decision tree work?', 'start': 654.926, 'duration': 1.761}, {'end': 659.228, 'text': "Wonder what kind of animals I'll get in the jungle today?", 'start': 656.747, 'duration': 2.481}, {'end': 664.23, 'text': "Maybe you're the hunter with the gun or, if you're more into photography, you're a photographer with a camera.", 'start': 659.488, 'duration': 4.742}], 'summary': 'A decision tree consists of leaf, decision, and root nodes, with the root node being the topmost decision node.', 'duration': 30.346, 'max_score': 633.884, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM633884.jpg'}, {'end': 697.62, 'src': 'embed', 'start': 671.934, 'weight': 4, 'content': [{'end': 677.235, 'text': 'So the problem statement is to classify the different types of animals based on their features using a decision tree.', 'start': 671.934, 'duration': 5.301}, {'end': 681.396, 'text': 'The data set is looking quite messy and the entropy is high in this case.', 'start': 677.335, 'duration': 4.061}, {'end': 684.677, 'text': "So let's look at a training set or a training data set.", 'start': 681.696, 'duration': 2.981}, {'end': 689.398, 'text': "And we're looking at color, we're looking at height, and then we have our different animals.", 'start': 684.977, 'duration': 4.421}, {'end': 692.599, 'text': 'We have our elephants, our giraffes, our monkeys, and our tigers.', 'start': 689.558, 'duration': 3.041}, {'end': 694.78, 'text': "And they're of different colors and shapes.", 'start': 692.939, 'duration': 1.841}, {'end': 697.62, 'text': "Let's see what that looks like and how do we split the data.", 'start': 695.08, 'duration': 2.54}], 'summary': 'Classify animals using decision tree with messy data and high entropy.', 'duration': 25.686, 'max_score': 671.934, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM671934.jpg'}, {'end': 824.521, 'src': 'embed', 'start': 793.248, 'weight': 2, 'content': [{'end': 797.47, 'text': "Now we're not going to go through each set one at a time to see what those numbers are.", 'start': 793.248, 'duration': 4.222}, {'end': 801.192, 'text': 'We just want you to be aware that this is a formula or the mathematics behind it.', 'start': 797.55, 'duration': 3.642}, {'end': 805.895, 'text': 'Gain can be calculated by finding the difference of the subsequent entropy values after a split.', 'start': 801.332, 'duration': 4.563}, {'end': 809.576, 'text': 'Now we will try to choose a condition that gives us the highest gain.', 'start': 806.275, 'duration': 3.301}, {'end': 814.378, 'text': 'We will do that by splitting the data using each condition and checking that the gain we get out of them.', 'start': 809.676, 'duration': 4.702}, {'end': 818.019, 'text': 'The condition that gives us the highest gain will be used to make the first split.', 'start': 814.498, 'duration': 3.521}, {'end': 824.521, 'text': "Can you guess what that first split will be just by looking at this image? As a human, it's probably pretty easy to split it.", 'start': 818.199, 'duration': 6.322}], 'summary': 'Gain calculated by finding difference in entropy values; condition with highest gain used for first split.', 'duration': 31.273, 'max_score': 793.248, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM793248.jpg'}, {'end': 896.974, 'src': 'embed', 'start': 865.042, 'weight': 0, 'content': [{'end': 869.404, 'text': 'This tree can now predict all the classes of animals present in the data set with 100% accuracy.', 'start': 865.042, 'duration': 4.362}, {'end': 869.825, 'text': 'That was easy.', 'start': 869.424, 'duration': 0.401}, {'end': 873.008, 'text': 'Use case.', 'start': 872.427, 'duration': 0.581}, {'end': 874.69, 'text': 'Loan repayment prediction.', 'start': 873.368, 'duration': 1.322}, {'end': 880.498, 'text': "Let's get into my favorite part and open up some Python and see what the programming code and the scripting looks like.", 'start': 874.79, 'duration': 5.708}, {'end': 882.721, 'text': "In here, we're going to want to do a prediction.", 'start': 880.678, 'duration': 2.043}, {'end': 887.408, 'text': "And we start with this individual here who's requesting to find out how good his customers are going to be.", 'start': 882.861, 'duration': 4.547}, {'end': 890.23, 'text': "whether they're going to repay their loan or not for this bank.", 'start': 887.768, 'duration': 2.462}, {'end': 896.974, 'text': 'And from that we want to generate a problem statement to predict if a customer will repay loan amount or not,', 'start': 890.31, 'duration': 6.664}], 'summary': 'A tree model predicts animal classes with 100% accuracy. use case: loan repayment prediction with python programming.', 'duration': 31.932, 'max_score': 865.042, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM865042.jpg'}], 'start': 633.884, 'title': 'Decision trees and entropy calculation', 'summary': 'Discusses the process of decision tree, information gain, and animal classification. it also involves entropy calculation for a dataset and the implementation of a decision tree algorithm in python with 100% accuracy in loan repayment prediction.', 'chapters': [{'end': 708.247, 'start': 633.884, 'title': 'Decision tree for animal classification', 'summary': 'Discusses the different parts of a decision tree, the process of how a decision tree works, and the problem statement of classifying animals based on their features using a decision tree, emphasizing the importance of information gain in splitting the data.', 'duration': 74.363, 'highlights': ['The problem statement is to classify different types of animals based on their features using a decision tree, with the dataset consisting of elephants, giraffes, monkeys, and tigers, each with different colors and shapes.', 'The chapter explains the different parts of a decision tree, including leaf nodes, which carry the classification or decision, decision nodes with two or more branches for breaking the group into different parts, and the root node being the topmost decision node.', 'The importance of information gain in splitting the data is highlighted, with gain being a measure of decrease in entropy after splitting.']}, {'end': 1027.462, 'start': 708.407, 'title': 'Entropy calculation and decision tree algorithm', 'summary': 'Involves calculating entropy for a data set with 3 giraffes, 2 tigers, 1 monkey, 2 elephants, and 8 total animals, resulting in an entropy value of 0.571. it further explains using a decision tree algorithm in python for loan repayment prediction, achieving 100% accuracy in classifying animals and discussing the necessary python packages for its implementation.', 'duration': 319.055, 'highlights': ['The entropy for the given data set with 3 giraffes, 2 tigers, 1 monkey, and 2 elephants results in an entropy value of 0.571. The calculation of entropy for the data set provides quantitative insight into the distribution of animals and their respective percentages.', 'Using a decision tree algorithm in Python, the classification of animals in the data set achieves 100% accuracy. The successful application of the decision tree algorithm in Python demonstrates its capability to accurately classify animals based on conditions and attributes.', 'Explanation of necessary Python packages for implementing the decision tree algorithm, including numpy, pandas, and sklearn for data manipulation, splitting, and classification. The explanation of essential Python packages provides insight into the tools required for implementing the decision tree algorithm in Python, showcasing the utilization of numpy, pandas, and sklearn for data manipulation and classification.']}], 'duration': 393.578, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM633884.jpg', 'highlights': ['The successful application of the decision tree algorithm in Python demonstrates its capability to accurately classify animals based on conditions and attributes.', 'The chapter explains the different parts of a decision tree, including leaf nodes, decision nodes, and the root node.', 'The importance of information gain in splitting the data is highlighted, with gain being a measure of decrease in entropy after splitting.', 'Using a decision tree algorithm in Python, the classification of animals in the data set achieves 100% accuracy.', 'The problem statement is to classify different types of animals based on their features using a decision tree, with the dataset consisting of elephants, giraffes, monkeys, and tigers.']}, {'end': 1645.736, 'segs': [{'end': 1067.708, 'src': 'heatmap', 'start': 1027.622, 'weight': 0, 'content': [{'end': 1030.284, 'text': "And I'm going to run this, and we're going to get two things on here.", 'start': 1027.622, 'duration': 2.662}, {'end': 1032.003, 'text': "One, we're going to get an error.", 'start': 1030.763, 'duration': 1.24}, {'end': 1033.505, 'text': "And two, we're going to get a warning.", 'start': 1032.165, 'duration': 1.34}, {'end': 1034.726, 'text': "Let's see what that looks like.", 'start': 1033.685, 'duration': 1.041}, {'end': 1037.446, 'text': 'So, the first thing we had is we have an error.', 'start': 1035.185, 'duration': 2.261}, {'end': 1041.469, 'text': "Why is this error here? Well, it's looking at this and says, I need to read a file.", 'start': 1037.887, 'duration': 3.582}, {'end': 1047.291, 'text': 'And when this was written, the person who wrote it, this is their path where they stored the file.', 'start': 1042.009, 'duration': 5.282}, {'end': 1049.613, 'text': "So let's go ahead and fix that.", 'start': 1048.193, 'duration': 1.42}, {'end': 1054.196, 'text': "And I'm going to put in here my file path.", 'start': 1051.434, 'duration': 2.762}, {'end': 1056.096, 'text': "I'm just going to call it full file name.", 'start': 1054.396, 'duration': 1.7}, {'end': 1063.08, 'text': "And you'll see it's on my C drive and there's this very lengthy setup on here where I stored the data2.csv file.", 'start': 1056.697, 'duration': 6.383}, {'end': 1067.708, 'text': "Don't worry too much about the full path because on your computer it will be different.", 'start': 1064.465, 'duration': 3.243}], 'summary': 'Debugging process revealed one error and one warning, resolved by fixing file path.', 'duration': 40.086, 'max_score': 1027.622, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM1027622.jpg'}, {'end': 1091.868, 'src': 'embed', 'start': 1064.465, 'weight': 2, 'content': [{'end': 1067.708, 'text': "Don't worry too much about the full path because on your computer it will be different.", 'start': 1064.465, 'duration': 3.243}, {'end': 1072.892, 'text': 'The data.to.csv file was generated by Simply Learn.', 'start': 1068.468, 'duration': 4.424}, {'end': 1078.697, 'text': 'If you want a copy of that you can comment down below and request it here in the YouTube.', 'start': 1073.693, 'duration': 5.004}, {'end': 1089.266, 'text': "And then if I'm going to give it a name, full file name, I want to go ahead and change it here to full file name.", 'start': 1080.038, 'duration': 9.228}, {'end': 1091.868, 'text': "So let's go ahead and run it now and see what happens.", 'start': 1089.646, 'duration': 2.222}], 'summary': 'Data.to.csv file generated by simply learn, available on request via youtube.', 'duration': 27.403, 'max_score': 1064.465, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM1064465.jpg'}, {'end': 1143.156, 'src': 'embed', 'start': 1114.71, 'weight': 3, 'content': [{'end': 1119.232, 'text': 'If you read the warning, it says the cross validation is depreciated.', 'start': 1114.71, 'duration': 4.522}, {'end': 1121.473, 'text': "So it's a warning on it's being removed.", 'start': 1119.412, 'duration': 2.061}, {'end': 1124.594, 'text': "And it's going to be moved in favor of the model selection.", 'start': 1122.213, 'duration': 2.381}, {'end': 1129.111, 'text': 'So if we go up here, we have sklearn.crossvalidation.', 'start': 1125.629, 'duration': 3.482}, {'end': 1136.974, 'text': "And if you research this and go to the sklearn site, you'll find out that you can actually just swap it right in there with model selection.", 'start': 1129.371, 'duration': 7.603}, {'end': 1143.156, 'text': 'And so when I come in here and I run it again, that removes a warning.', 'start': 1138.915, 'duration': 4.241}], 'summary': 'The cross validation warning is being removed and replaced with model selection, solving the issue.', 'duration': 28.446, 'max_score': 1114.71, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM1114710.jpg'}, {'end': 1190.816, 'src': 'embed', 'start': 1162.958, 'weight': 1, 'content': [{'end': 1167.462, 'text': 'Remember the data file we just loaded on here, the data underscore two dot csv.', 'start': 1162.958, 'duration': 4.504}, {'end': 1170.764, 'text': "Let's talk a little bit more about that and see what that looks like,", 'start': 1168.302, 'duration': 2.462}, {'end': 1175.647, 'text': "both as a text file because it's a comma-separated variable file and in a spreadsheet.", 'start': 1170.764, 'duration': 4.883}, {'end': 1178.608, 'text': 'This is what it looks like as a basic text file.', 'start': 1176.287, 'duration': 2.321}, {'end': 1185.573, 'text': "You can see at the top they've created a header, and it's got one, two, three, four, five columns, and each column has data in it.", 'start': 1178.728, 'duration': 6.845}, {'end': 1190.816, 'text': "And let me flip this over, because we're also going to look at this in an actual spreadsheet, so you can see what that looks like.", 'start': 1185.653, 'duration': 5.163}], 'summary': "Data file 'data_2.csv' contains 5 columns with data, viewable as text and spreadsheet.", 'duration': 27.858, 'max_score': 1162.958, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM1162958.jpg'}, {'end': 1343.251, 'src': 'embed', 'start': 1314.115, 'weight': 4, 'content': [{'end': 1316.976, 'text': 'So when we were looking at the data, we had five columns of data.', 'start': 1314.115, 'duration': 2.861}, {'end': 1320.959, 'text': "And then let's take one more step to explore the data using Python.", 'start': 1317.196, 'duration': 3.763}, {'end': 1324.24, 'text': "And now that we've taken a look at the length and the shape,", 'start': 1321.239, 'duration': 3.001}, {'end': 1331.525, 'text': "let's go ahead and use the pandas module for head another beautiful thing in the data set that we can utilize.", 'start': 1324.24, 'duration': 7.285}, {'end': 1335.287, 'text': "So let's put that on our sheet here, and we have print data set.", 'start': 1331.845, 'duration': 3.442}, {'end': 1343.251, 'text': 'and balanceData.Head, and this is a pandas print statement of its own, so it has its own print feature in there.', 'start': 1336.047, 'duration': 7.204}], 'summary': "Five columns of data analyzed using python's pandas module.", 'duration': 29.136, 'max_score': 1314.115, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM1314115.jpg'}, {'end': 1442.983, 'src': 'embed', 'start': 1420.41, 'weight': 5, 'content': [{'end': 1429.656, 'text': "So in our next step We're going to train and build our data tree and to do that we need to first separate the data out.", 'start': 1420.41, 'duration': 9.246}, {'end': 1437.941, 'text': "We're going to separate into two groups so that we have something to actually train the data with and then we have some data on the side to test it to see how good our model is.", 'start': 1429.736, 'duration': 8.205}, {'end': 1442.983, 'text': 'Remember, with any of the machine learning, you always want to have some kind of test set to weigh it against.', 'start': 1438.161, 'duration': 4.822}], 'summary': 'Data will be separated into two groups for training and testing in machine learning.', 'duration': 22.573, 'max_score': 1420.41, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM1420410.jpg'}], 'start': 1027.622, 'title': 'File error debugging process and data exploration in python', 'summary': "Demonstrates the process of debugging a file error, including identifying the issue, providing a fix, and explaining the file's origin and availability, with specific mention of the generated data2.csv file. additionally, it discusses understanding and resolving coding warnings, exploring a dataset of 1000 rows and 5 columns using python's pandas module, and preparing data for training a decision tree classifier with a test set of roughly 30%.", 'chapters': [{'end': 1089.266, 'start': 1027.622, 'title': 'File error debugging process', 'summary': "Demonstrates the process of debugging a file error, including identifying the issue, providing a fix, and explaining the file's origin and availability, with specific mention of the generated data2.csv file and the offer to provide a copy.", 'duration': 61.644, 'highlights': ['The chapter outlines the process of debugging a file error, including identifying the issue and providing a fix.', 'The specific mention of the generated data2.csv file and the offer to provide a copy is highlighted.', "The detailed explanation of the file's origin and availability is highlighted, including the offer to provide a copy upon request."]}, {'end': 1645.736, 'start': 1089.646, 'title': 'Understanding warnings and data exploration in python', 'summary': "Discusses understanding and resolving coding warnings, exploring a dataset of 1000 rows and 5 columns using python's pandas module, and preparing data for training a decision tree classifier with a test set of roughly 30%.", 'duration': 556.09, 'highlights': ['Understanding Warnings and Errors The chapter emphasizes the importance of understanding and resolving coding warnings and errors, as demonstrated by resolving a warning related to the deprecation of cross validation in favor of model selection in sklearn, showcasing the practical application of handling warnings in coding.', "Data Exploration using Python's Pandas Module The transcript provides a detailed exploration of a dataset with 1000 rows and 5 columns using Python's pandas module, including the use of functions to determine the length, shape, and structure of the dataset, showcasing the practical application of data exploration in Python.", 'Data Preparation for Decision Tree Classifier The process of preparing data for training a decision tree classifier is explained, including separating the dataset into training and test sets, and building a decision tree classifier with specified parameters such as max depth and minimal samples of leaves, providing a comprehensive explanation of data preparation for machine learning.']}], 'duration': 618.114, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM1027622.jpg', 'highlights': ['The chapter outlines the process of debugging a file error, including identifying the issue and providing a fix.', 'The specific mention of the generated data2.csv file and the offer to provide a copy is highlighted.', "The detailed explanation of the file's origin and availability is highlighted, including the offer to provide a copy upon request.", 'Understanding Warnings and Errors The chapter emphasizes the importance of understanding and resolving coding warnings and errors, as demonstrated by resolving a warning related to the deprecation of cross validation in favor of model selection in sklearn, showcasing the practical application of handling warnings in coding.', "Data Exploration using Python's Pandas Module The transcript provides a detailed exploration of a dataset with 1000 rows and 5 columns using Python's pandas module, including the use of functions to determine the length, shape, and structure of the dataset, showcasing the practical application of data exploration in Python.", 'Data Preparation for Decision Tree Classifier The process of preparing data for training a decision tree classifier is explained, including separating the dataset into training and test sets, and building a decision tree classifier with specified parameters such as max depth and minimal samples of leaves, providing a comprehensive explanation of data preparation for machine learning.']}, {'end': 1957.055, 'segs': [{'end': 1694.695, 'src': 'embed', 'start': 1646.016, 'weight': 0, 'content': [{'end': 1654.857, 'text': "Now that we've created our decision tree classifier, not only created it but trained it, let's go ahead and apply it and see what that looks like.", 'start': 1646.016, 'duration': 8.841}, {'end': 1658.578, 'text': "So let's go ahead and make a prediction and see what that looks like.", 'start': 1655.297, 'duration': 3.281}, {'end': 1660.818, 'text': "We're going to paste our predict code in here.", 'start': 1658.918, 'duration': 1.9}, {'end': 1664.799, 'text': "And before we run it, let's just take a quick look at what it's doing here.", 'start': 1661.738, 'duration': 3.061}, {'end': 1668.3, 'text': "We have a variable Y predict that we're going to do.", 'start': 1665.639, 'duration': 2.661}, {'end': 1676.886, 'text': "And we're going to use our variable CLF entropy that we created, and then you'll see .predict.", 'start': 1669.36, 'duration': 7.526}, {'end': 1684.25, 'text': "And it's very common in the SKLearn modules that their different tools have the predict when you're actually running a prediction.", 'start': 1677.186, 'duration': 7.064}, {'end': 1687.632, 'text': "In this case we're going to put our X test data in here.", 'start': 1685.01, 'duration': 2.622}, {'end': 1694.695, 'text': 'Now, if you delivered this for use an actual commercial use and distributed it.', 'start': 1688.992, 'duration': 5.703}], 'summary': 'Trained decision tree classifier applied for prediction using sklearn modules.', 'duration': 48.679, 'max_score': 1646.016, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM1646016.jpg'}, {'end': 1858.334, 'src': 'embed', 'start': 1821.707, 'weight': 4, 'content': [{'end': 1830.417, 'text': 'So when we look at the number of loans and we look at how good our model fit, we can tell people it has about a 93.6 fitting to it.', 'start': 1821.707, 'duration': 8.71}, {'end': 1832.919, 'text': 'So just a quick recap on that.', 'start': 1830.997, 'duration': 1.922}, {'end': 1835.422, 'text': 'We now have accuracy set up on here.', 'start': 1833.42, 'duration': 2.002}, {'end': 1841.609, 'text': 'And so we have created a model that uses the decision tree algorithm to predict whether a customer will repay the loan or not.', 'start': 1835.662, 'duration': 5.947}, {'end': 1844.767, 'text': 'The accuracy of the model is about 94.6%.', 'start': 1842.025, 'duration': 2.742}, {'end': 1850.65, 'text': 'The bank can now use this model to decide whether it should approve the loan request from a particular customer or not.', 'start': 1844.767, 'duration': 5.883}, {'end': 1853.071, 'text': 'And so this information is really powerful.', 'start': 1850.99, 'duration': 2.081}, {'end': 1858.334, 'text': 'We may not be able to, as individuals, understand all these numbers because they have thousands of numbers that come in.', 'start': 1853.352, 'duration': 4.982}], 'summary': 'A model using decision tree algorithm predicts loan repayment with 94.6% accuracy, enabling the bank to make better loan approval decisions.', 'duration': 36.627, 'max_score': 1821.707, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM1821707.jpg'}, {'end': 1913.953, 'src': 'embed', 'start': 1877.227, 'weight': 7, 'content': [{'end': 1884.332, 'text': 'we covered up some different aspects of machine learning and what that is utilized in your everyday life and what you can use it for for predicting,', 'start': 1877.227, 'duration': 7.105}, {'end': 1889.896, 'text': 'for describing, for guessing what the next outcome is, for storing information.', 'start': 1884.332, 'duration': 5.564}, {'end': 1895.881, 'text': 'we looked at the three main types of machine learning supervised learning, unsupervised learning and reinforced learning.', 'start': 1889.896, 'duration': 5.985}, {'end': 1901.505, 'text': 'We looked at problems in machine learning and what it solves classification, regression and clustering.', 'start': 1896.021, 'duration': 5.484}, {'end': 1904.867, 'text': 'Finally we went through how does the decision tree work,', 'start': 1901.605, 'duration': 3.262}, {'end': 1908.95, 'text': "where we looked at the hunter and he's trying to sort out the different animals and what kind of animals they are.", 'start': 1904.867, 'duration': 4.083}, {'end': 1913.953, 'text': 'And then we rolled up our sleeves and did our Python coding and actually applied it to a data set.', 'start': 1909.13, 'duration': 4.823}], 'summary': 'Covered machine learning applications and types, including supervised, unsupervised, and reinforced learning. explored problem-solving in classification, regression, and clustering. demonstrated decision tree concept and applied python coding to a dataset.', 'duration': 36.726, 'max_score': 1877.227, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM1877227.jpg'}], 'start': 1646.016, 'title': 'Decision trees and machine learning', 'summary': 'Discusses the implementation of a decision tree algorithm for predicting loan repayments, achieving a model accuracy of 94.6% and showcasing the power of machine learning in making informed decisions for loan approvals.', 'chapters': [{'end': 1694.695, 'start': 1646.016, 'title': 'Decision tree classifier: applying predictions', 'summary': 'Discusses applying the decision tree classifier to make predictions using the sklearn module, emphasizing the use of the predict function and x test data.', 'duration': 48.679, 'highlights': ['The chapter discusses applying the decision tree classifier to make predictions using the SKLearn module, emphasizing the use of the predict function and X test data.', 'The chapter demonstrates the process of making predictions using the trained decision tree classifier, specifically using the predict function on the X test data.', 'The chapter emphasizes the importance of understanding the predict function within the SKLearn module and its usage for making predictions.', 'The chapter highlights the significance of using the X test data for making predictions with the decision tree classifier.']}, {'end': 1957.055, 'start': 1694.695, 'title': 'Decision trees and machine learning', 'summary': 'Discusses the implementation of a decision tree algorithm for predicting loan repayments, achieving a model accuracy of 94.6%, showcasing the power of machine learning in making informed decisions for loan approvals.', 'duration': 262.36, 'highlights': ['The accuracy of the model is about 94.6%. The decision tree model achieved an accuracy of 94.6% in predicting loan repayments, demonstrating its effectiveness in assessing customer creditworthiness.', "The model's accuracy was 93.66667% in predicting loan repayments. The model's accuracy was quantified at 93.66667% in predicting loan repayments, indicating a high level of precision in determining customer credit reliability.", 'The chapter covers the three main types of machine learning: supervised learning, unsupervised learning, and reinforced learning. The chapter provides insights into the three main types of machine learning, namely supervised learning, unsupervised learning, and reinforced learning, offering a comprehensive understanding of the diverse applications of machine learning.', 'The chapter also delves into the types of problems that machine learning solves, including classification, regression, and clustering. The chapter explores the problem-solving capabilities of machine learning, addressing classification, regression, and clustering, showcasing its versatility in resolving various data-driven challenges.', 'The bank can now use this model to decide whether it should approve the loan request from a particular customer or not. The developed decision tree model empowers banks to make informed decisions on loan approvals, enhancing the efficiency and accuracy of customer credit evaluations.']}], 'duration': 311.039, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/RmajweUFKvM/pics/RmajweUFKvM1646016.jpg', 'highlights': ['The chapter emphasizes the importance of understanding the predict function within the SKLearn module and its usage for making predictions.', 'The chapter demonstrates the process of making predictions using the trained decision tree classifier, specifically using the predict function on the X test data.', 'The chapter discusses applying the decision tree classifier to make predictions using the SKLearn module, emphasizing the use of the predict function and X test data.', 'The chapter highlights the significance of using the X test data for making predictions with the decision tree classifier.', "The model's accuracy was quantified at 93.66667% in predicting loan repayments, indicating a high level of precision in determining customer credit reliability.", 'The accuracy of the model is about 94.6%. The decision tree model achieved an accuracy of 94.6% in predicting loan repayments, demonstrating its effectiveness in assessing customer creditworthiness.', 'The bank can now use this model to decide whether it should approve the loan request from a particular customer or not. The developed decision tree model empowers banks to make informed decisions on loan approvals, enhancing the efficiency and accuracy of customer credit evaluations.', 'The chapter provides insights into the three main types of machine learning, namely supervised learning, unsupervised learning, and reinforced learning, offering a comprehensive understanding of the diverse applications of machine learning.', 'The chapter also delves into the types of problems that machine learning solves, including classification, regression, and clustering, showcasing its versatility in resolving various data-driven challenges.']}], 'highlights': ['The accuracy of the model is about 94.6%. The decision tree model achieved an accuracy of 94.6% in predicting loan repayments, demonstrating its effectiveness in assessing customer creditworthiness.', "The model's accuracy was quantified at 93.66667% in predicting loan repayments, indicating a high level of precision in determining customer credit reliability.", 'The bank can now use this model to decide whether it should approve the loan request from a particular customer or not. The developed decision tree model empowers banks to make informed decisions on loan approvals, enhancing the efficiency and accuracy of customer credit evaluations.', 'The chapter emphasizes the importance of understanding the predict function within the SKLearn module and its usage for making predictions.', 'The chapter demonstrates the process of making predictions using the trained decision tree classifier, specifically using the predict function on the X test data.', 'The chapter discusses applying the decision tree classifier to make predictions using the SKLearn module, emphasizing the use of the predict function and X test data.', 'The chapter highlights the significance of using the X test data for making predictions with the decision tree classifier.', 'The successful application of the decision tree algorithm in Python demonstrates its capability to accurately classify animals based on conditions and attributes.', 'Using a decision tree algorithm in Python, the classification of animals in the data set achieves 100% accuracy.', 'The problem statement is to classify different types of animals based on their features using a decision tree, with the dataset consisting of elephants, giraffes, monkeys, and tigers.']}