Download or read book HOUSE PRICE ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2022-02-20 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: The dataset used in this project is taken from the second chapter of Aurélien Géron's recent book 'Hands-On Machine learning with Scikit-Learn and TensorFlow'. It serves as an excellent introduction to implementing machine learning algorithms because it requires rudimentary data cleaning, has an easily understandable list of variables and sits at an optimal size between being to toyish and too cumbersome. The data contains information from the 1990 California census. Although it may not help you with predicting current housing prices like the Zillow Zestimate dataset, it does provide an accessible introductory dataset for teaching people about the basics of machine learning. The data pertains to the houses found in a given California district and some summary stats about them based on the 1990 census data. Be warned the data aren't cleaned so there are some preprocessing steps required! The columns are as follows: longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, median_house_value, and ocean_proximity. The machine learning models used in this project used to perform regression on median_house_value and to predict it as target variable are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, and MLP classifier. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.

Download or read book Handbook of Research on Advances in Data Analytics and Complex Communication Networks written by P. Venkata Krishna and published by IGI Global. This book was released on 2021 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This edited book discusses data analytics and complex communication networks and recommends new methodologies, system architectures, and other solutions to prevail over the current limitations faced by the field"--

Download or read book Prediction of House Pricing Using Machine Learning with Python written by Namit Jain and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a study done for the purpose of knowledge among the people to understand and estimate the pricing of their houses. This will help them realize about the factors that affect the pricing of the house most and understand them. This prediction is done using 5 base algorithms and using simple stacking approach to compare the output of each algorithm and predict the best possible outcome.

Download or read book Emerging Technologies in Data Mining and Information Security written by João Manuel R. S. Tavares and published by Springer Nature. This book was released on 2021-05-04 with total page 994 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features research papers presented at the International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS 2020) held at the University of Engineering & Management, Kolkata, India, during July 2020. The book is organized in three volumes and includes high-quality research work by academicians and industrial experts in the field of computing and communication, including full-length papers, research-in-progress papers, and case studies related to all the areas of data mining, machine learning, Internet of things (IoT), and information security.

Download or read book Predicting Log Error for House Price Using Machine Learning written by Swapnilkumar Kanani and published by . This book was released on 2019 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt: Buying a house is the major investment decision for an individual. Identifying the property in a desirable location with the right size and build quality at an affordable price is a daunting task. In recent years, many online platforms such as Zillow, Trulia, Redfin, Homesnap have developed algorithms to estimate housing prices across the US. These platforms use statistical model and machine learning algorithm to provide their users the price estimation services at no cost. For example, Zillow has data for 110 million homes across the US. It estimates the price based on the publicly available data including square footage, number of bedrooms, bathrooms, quality of the neighborhood and many other features. The goal of the project is to understand the data analysis techniques and use the understanding to predict the error in the house price prediction. By trying different machine learning algorithms including XGBoost, LightGBM and linear regression, different accuracies can be achieved. The thesis explores four key areas of the machine learning process: Data acquisition, exploratory data analysis, feature engineering, prediction, and validation. Housing data from Orange, Ventura, and Los Angeles counties are used to estimate the log errors. Following formula is used to calculate log error. logerror=log (Prediction)−log (SalePrice)

Download or read book Data Mining in Agriculture written by Antonio Mucherino and published by Springer Science & Business Media. This book was released on 2009-09-22 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining in Agriculture represents a comprehensive effort to provide graduate students and researchers with an analytical text on data mining techniques applied to agriculture and environmental related fields. This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in MATLAB®.

Download or read book REGRESSION SEGMENTATION CLUSTERING AND PREDICTION PROJECTS WITH PYTHON written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2022-02-25 with total page 623 pages. Available in PDF, EPUB and Kindle. Book excerpt: PROJECT 1: TIME-SERIES WEATHER: FORECASTING AND PREDICTION WITH PYTHON Weather data are described and quantified by the variables of Earth's atmosphere: temperature, air pressure, humidity, and the variations and interactions of these variables, and how they change over time. Different spatial scales are used to describe and predict weather on local, regional, and global levels. The dataset used in this project contains weather data for New Delhi, India. This data was taken out from wunderground. It contains various features such as temperature, pressure, humidity, rain, precipitation, etc. The main target is to develop a prediction model accurate enough for forecasting temperature and predicting target variable (condition). Time-series weather forecasting will be done using ARIMA models. The machine learning models used in this project to predict target variable (condition) are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, and MLP classifier. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 2: HOUSE PRICE: ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON The dataset used in this project is taken from the second chapter of Aurélien Géron's recent book 'Hands-On Machine learning with Scikit-Learn and TensorFlow'. It serves as an excellent introduction to implementing machine learning algorithms because it requires rudimentary data cleaning, has an easily understandable list of variables and sits at an optimal size between being to toyish and too cumbersome. The data contains information from the 1990 California census. Although it may not help you with predicting current housing prices like the Zillow Zestimate dataset, it does provide an accessible introductory dataset for teaching people about the basics of machine learning. The data pertains to the houses found in a given California district and some summary stats about them based on the 1990 census data. Be warned the data aren't cleaned so there are some preprocessing steps required! The columns are as follows: longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, median_house_value, and ocean_proximity. The machine learning models used in this project used to perform regression on median_house_value and to predict it as target variable are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, and MLP classifier. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 3: CUSTOMER PERSONALITY ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON Customer Personality Analysis is a detailed analysis of a company’s ideal customers. It helps a business to better understand its customers and makes it easier for them to modify products according to the specific needs, behaviors and concerns of different types of customers. Customer personality analysis helps a business to modify its product based on its target customers from different types of customer segments. For example, instead of spending money to market a new product to every customer in the company’s database, a company can analyze which customer segment is most likely to buy the product and then market the product only on that particular segment. Following are the features in the dataset: ID = Customer's unique identifier; Year_Birth = Customer's birth year; Education = Customer's education level; Marital_Status = Customer's marital status; Income = Customer's yearly household income; Kidhome = Number of children in customer's household; Teenhome = Number of teenagers in customer's household; Dt_Customer = Date of customer's enrollment with the company; Recency = Number of days since customer's last purchase; MntWines = Amount spent on wine in the last 2 years; MntFruits = Amount spent on fruits in the last 2 years; MntMeatProducts = Amount spent on meat in the last 2 years; MntFishProducts = Amount spent on fish in the last 2 years; MntSweetProducts = Amount spent on sweets in the last 2 years; MntGoldProds = Amount spent on gold in the last 2 years; NumDealsPurchases = Number of purchases made with a discount; NumWebPurchases = Number of purchases made through the company's web site; NumCatalogPurchases = Number of purchases made using a catalogue; NumStorePurchases = Number of purchases made directly in stores; NumWebVisitsMonth = Number of visits to company's web site in the last month; AcceptedCmp3 = 1 if customer accepted the offer in the 3rd campaign, 0 otherwise; AcceptedCmp4 = 1 if customer accepted the offer in the 4th campaign, 0 otherwise; AcceptedCmp5 = 1 if customer accepted the offer in the 5th campaign, 0 otherwise; AcceptedCmp1 = 1 if customer accepted the offer in the 1st campaign, 0 otherwise; AcceptedCmp2 = 1 if customer accepted the offer in the 2nd campaign, 0 otherwise; Response = 1 if customer accepted the offer in the last campaign, 0 otherwise; and Complain = 1 if customer complained in the last 2 years, 0 otherwise. The target in this project is to perform clustering and predicting to summarize customer segments. In this project, you will perform clustering using KMeans to get 4 clusters. The machine learning models used in this project to perform regression on total number of purchase and to predict clusters as target variable are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM, Gradient Boosting, XGB, and MLP. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 4: CUSTOMER SEGMENTATION, CLUSTERING, AND PREDICTION WITH PYTHON In this project, you will develop a customer segmentation, clustering, and prediction to define marketing strategy. The sample dataset summarizes the usage behavior of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables. Following is the Data Dictionary for Credit Card dataset: CUSTID: Identification of Credit Card holder (Categorical); BALANCE: Balance amount left in their account to make purchases; BALANCEFREQUENCY: How frequently the Balance is updated, score between 0 and 1 (1 = frequently updated, 0 = not frequently updated); PURCHASES: Amount of purchases made from account; ONEOFFPURCHASES: Maximum purchase amount done in one-go; INSTALLMENTSPURCHASES: Amount of purchase done in installment; CASHADVANCE: Cash in advance given by the user; PURCHASESFREQUENCY: How frequently the Purchases are being made, score between 0 and 1 (1 = frequently purchased, 0 = not frequently purchased); ONEOFFPURCHASESFREQUENCY: How frequently Purchases are happening in one-go (1 = frequently purchased, 0 = not frequently purchased); PURCHASESINSTALLMENTSFREQUENCY: How frequently purchases in installments are being done (1 = frequently done, 0 = not frequently done); CASHADVANCEFREQUENCY: How frequently the cash in advance being paid; CASHADVANCETRX: Number of Transactions made with "Cash in Advanced"; PURCHASESTRX: Number of purchase transactions made; CREDITLIMIT: Limit of Credit Card for user; PAYMENTS: Amount of Payment done by user; MINIMUM_PAYMENTS: Minimum amount of payments made by user; PRCFULLPAYMENT: Percent of full payment paid by user; and TENURE: Tenure of credit card service for user. In this project, you will perform clustering using KMeans to get 5 clusters. The machine learning models used in this project to perform regression on total number of purchase and to predict clusters as target variable are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM, Gradient Boosting, XGB, and MLP. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.

Download or read book Classification and Regression Trees written by Leo Breiman and published by Routledge. This book was released on 2017-10-19 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.

Download or read book Practical Machine Learning for Data Analysis Using Python written by Abdulhamit Subasi and published by Academic Press. This book was released on 2020-06-05 with total page 534 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Machine Learning for Data Analysis Using Python is a problem solver’s guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data Explores important classification and regression algorithms as well as other machine learning techniques Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features

Download or read book Intelligent Manufacturing Systems in Industry 4 0 written by B. B. V. L. Deepak and published by Springer Nature. This book was released on 2023-06-30 with total page 713 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the select proceedings of the 4th International Conference on Innovative Product Design and Intelligent Manufacturing System (IPDIMS 2022). It covers the latest trends in the areas of design and manufacturing. The main topics covered include Industry 4.0, smart manufacturing, advanced robotics, and CAD/CAM/CIM. The contents of this book are useful for researchers and professionals working in the disciplines of mechatronics, mechanical, manufacturing, production, and industrial engineering.

Download or read book Computational Intelligence for Modern Business Systems written by Sandeep Kautish and published by Springer Nature. This book was released on 2023-12-05 with total page 523 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the applications of computational intelligence techniques in business systems and advocates how these techniques are useful in modern business operations. The book redefines the computational intelligence foundations, the three pillars - neural networks, evolutionary computation, and fuzzy systems. It also discusses emerging areas such as swarm intelligence, artificial immune systems (AIS), support vector machines, rough sets, and chaotic systems. The other areas have also been demystified in the book to strengthen the range of computational intelligence techniques such as expert systems, knowledge-based systems, and genetic algorithms. Therefore, this book will redefine the role of computational intelligence techniques in modern business system operations such as marketing, finance & accounts, operations, personnel management, supply chain management, and logistics. Besides, this book guides the readers through using them to model, discover, and interpret new patterns that cannot be found through statistical methods alone in various business system operations. This book reveals how computational intelligence can inform the design and integration of services, architecture, brand identity, and product portfolio across the entire enterprise. The book will provide insights into research gaps, open challenges, and unsolved computational intelligence problems. The book will act as a premier reference and instant material for all the users who are contributing/practicing the adaptation of computational intelligence modern techniques in business systems.

Download or read book Artificial Intelligence with Python Cookbook written by Ben Auffarth and published by Packt Publishing Ltd. This book was released on 2020-10-30 with total page 459 pages. Available in PDF, EPUB and Kindle. Book excerpt: Work through practical recipes to learn how to solve complex machine learning and deep learning problems using Python Key FeaturesGet up and running with artificial intelligence in no time using hands-on problem-solving recipesExplore popular Python libraries and tools to build AI solutions for images, text, sounds, and imagesImplement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much moreBook Description Artificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research. Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you’ll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. Throughout the book, you’ll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems. By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production. What you will learnImplement data preprocessing steps and optimize model hyperparametersDelve into representational learning with adversarial autoencodersUse active learning, recommenders, knowledge embedding, and SAT solversGet to grips with probabilistic modeling with TensorFlow probabilityRun object detection, text-to-speech conversion, and text and music generationApply swarm algorithms, multi-agent systems, and graph networksGo from proof of concept to production by deploying models as microservicesUnderstand how to use modern AI in practiceWho this book is for This AI machine learning book is for Python developers, data scientists, machine learning engineers, and deep learning practitioners who want to learn how to build artificial intelligence solutions with easy-to-follow recipes. You’ll also find this book useful if you’re looking for state-of-the-art solutions to perform different machine learning tasks in various use cases. Basic working knowledge of the Python programming language and machine learning concepts will help you to work with code effectively in this book.

Download or read book Electronic Governance with Emerging Technologies written by Fernando Ortiz-Rodríguez and published by Springer Nature. This book was released on 2023-10-17 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second International Conference on Electronic Governance with Emerging Technologies, EGETC 2023, held in Poznan, Poland, during September 11–12, 2023. The 15 full papers and one short paper presented were thoroughly reviewed and selected from the 76 submissions. This volume focuses on the recent developments in the domain of eGovernment and governance of digital organizations also aims to shed light on the emerging research trends and their applications.

Download or read book Sixth International Conference on Intelligent Computing and Applications written by Subhransu Sekhar Dash and published by Springer Nature. This book was released on 2021-07-27 with total page 579 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the peer-reviewed proceedings of the Sixth International Conference on Intelligent Computing and Applications (ICICA 2020), held at Government College of Engineering, Keonjhar, Odisha, India, during December 22–24, 2020. The book includes the latest research on advanced computational methodologies such as neural networks, fuzzy systems, evolutionary algorithms, hybrid intelligent systems, uncertain reasoning techniques, and other machine learning methods and their applications to decision-making and problem-solving in mobile and wireless communication networks.

Download or read book Hands On Machine Learning with Scikit Learn Keras and TensorFlow written by Aurélien Géron and published by "O'Reilly Media, Inc.". This book was released on 2019-09-05 with total page 851 pages. Available in PDF, EPUB and Kindle. Book excerpt: Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Download or read book Housing Sale Price Prediction Using Machine Learning Algorithms written by Yichen Zhou and published by . This book was released on 2020 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, I explore how predictive modeling can be applied in housing sale price prediction by analyzing the housing dataset and use machine learning models. Actually, I try four different models, namely, linear regression, lasso regression, randomforest and xgboost. Additionally, as the data have 79 explanatory variables with many missing values, I spend much time dealing with the data. I do explorary data analysis, feature enginnering before model fitting. And then using rmse and R-squared to measure the model performance. After I try four different models, I get some results. As for the first model - linear regression, it doesn't meet the assumption of equality of the variances. Therefore we can't use the linear model as the candidate of our final model. Then I try lasso regression, but the RMSE and R-squared looks not so good. Then I try Random forest. The R squared in this model of training set is very good, but in the test set the R squared is relatively low, which may show the RF model is a little bit overfitting. Finally I try the fourth model - xgboost. All of the results of this xgboost model seem very good. Therefore, I will use this xgboost model as my final model to predict the housing price. The xgboost model also shows which variables have important effects on sale price.