Download or read book DATA SCIENCE FOR SALES ANALYSIS FORECASTING CLUSTERING AND PREDICTION WITH PYTHON written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-07-28 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this comprehensive data science project focusing on sales analysis, forecasting, clustering, and prediction with Python, we embarked on an enlightening journey of data exploration and analysis. Our primary objective was to gain valuable insights from the dataset and leverage the power of machine learning to make accurate predictions and informed decisions. We began by meticulously exploring the dataset, examining its structure, and identifying any missing or inconsistent data. By visualizing features' distributions and conducting statistical analyses, we gained a better understanding of the data's characteristics and potential challenges. The first key aspect of the project was weekly sales forecasting. We employed various machine learning regression models, including Linear Regression, Support Vector Regression, Random Forest Regression, Decision Tree Regression, Gradient Boosting Regression, Extreme Gradient Boosting Regression, Light Gradient Boosting Regression, KNN Regression, Catboost Regression, Naïve Bayes Regression, and Multi-Layer Perceptron Regression. These models enabled us to predict weekly sales based on relevant features, allowing us to uncover patterns and relationships between different factors and sales performance. To optimize the performance of our regression models, we employed grid search with cross-validation. This technique systematically explored hyperparameter combinations to find the optimal configuration, maximizing the models' accuracy and predictive capabilities. Moving on to data segmentation, we adopted the widely-used K-means clustering technique, an unsupervised learning method. The goal was to divide data into distinct segments. By determining the optimal number of clusters through grid search with cross-validation, we ensured that the clustering accurately captured the underlying patterns in the data. The next phase of the project focused on predicting the cluster of new customers using machine learning classifiers. We employed powerful classifiers such as Logistic Regression, K-Nearest Neighbors, Support Vector, Decision Trees, Random Forests, Gradient Boosting, Adaboost, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron (MLP) to make accurate predictions. Grid search with cross-validation was again applied to fine-tune the classifiers' hyperparameters, enhancing their performance. Throughout the project, we emphasized the significance of feature scaling techniques, such as Min-Max scaling and Standardization. These preprocessing steps played a crucial role in ensuring that all features were on the same scale, contributing equally during model training, and improving the models' interpretability. Evaluation of our models was conducted using various metrics. For regression tasks, we utilized mean squared error, while classification tasks employed accuracy, precision, recall, and F1-score. The use of cross-validation helped validate the models' robustness, providing comprehensive assessments of their effectiveness. Visualization played a vital role in presenting our findings effectively. Utilizing libraries such as Matplotlib and Seaborn, we created informative visualizations that facilitated the communication of complex insights to stakeholders and decision-makers. Throughout the project, we followed an iterative approach, refining our strategies through data preprocessing, model training, and hyperparameter tuning. The grid search technique proved to be an invaluable tool in identifying the best parameter combinations, resulting in more accurate predictions and meaningful customer segmentation. In conclusion, this data science project demonstrated the power of machine learning techniques in sales analysis, forecasting, and customer segmentation. The insights and recommendations generated from the models can provide valuable guidance for businesses seeking to optimize sales strategies, target marketing efforts, and make data-driven decisions to achieve growth and success. The project showcases the importance of leveraging advanced analytical methods to unlock hidden patterns and unleash the full potential of data for business success.

Download or read book Data Science Using Python and R written by Chantal D. Larose and published by John Wiley & Sons. This book was released on 2019-04-09 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.

Download or read book FOUR PROJECTS PREDICTION AND FORECASTING USING MACHINE LEARNING WITH PYTHON written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2022-05-25 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt: PROJECT 1: GOLD PRICE ANALYSIS AND FORECASTING USING MACHINE LEARNING WITH PYTHON The challenge of this project is to accurately predict the future adjusted closing price of Gold ETF across a given period of time in the future. The problem is a regression problem, because the output value which is the adjusted closing price in this project is continuous value. Data for this study is collected from November 18th 2011 to January 1st 2019 from various sources. The data has 1718 rows in total and 80 columns in total. Data for attributes, such as Oil Price, Standard and Poor’s (S&P) 500 index, Dow Jones Index US Bond rates (10 years), Euro USD exchange rates, prices of precious metals Silver and Platinum and other metals such as Palladium and Rhodium, prices of US Dollar Index, Eldorado Gold Corporation and Gold Miners ETF were gathered. The dataset has 1718 rows in total and 80 columns in total. Data for attributes, such as Oil Price, Standard and Poor’s (S&P) 500 index, Dow Jones Index US Bond rates (10 years), Euro USD exchange rates, prices of precious metals Silver and Platinum and other metals such as Palladium and Rhodium, prices of US Dollar Index, Eldorado Gold Corporation and Gold Miners ETF were gathered. To perform forecasting based on regression adjusted closing price of gold, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Naïve Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, and MLP regression. The machine learning models used predict gold daily returns as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, XGB classifier, MLP classifier, and Extra Trees classifier. Finally, you will plot boundary decision, distribution of features, feature importance, predicted values versus true values, confusion matrix, learning curve, performance of the model, and scalability of the model. PROJECT 2: WIND POWER ANALYSIS AND FORECASTING USING MACHINE LEARNING WITH PYTHON Renewable energy remains one of the most important topics for a sustainable future. Wind, being a perennial source of power, could be utilized to satisfy our power requirements. With the rise of wind farms, wind power forecasting would prove to be quite useful. It contains various weather, turbine and rotor features. Data has been recorded from January 2018 till March 2020. Readings have been recorded at a 10-minute interval. A longterm wind forecasting technique is thus required. The attributes in the dataset are as follows: ActivePower, AmbientTemperature, BearingShaftTemperature, Blade1PitchAngle, Blade2PitchAngle, Blade3PitchAngle, ControlBoxTemperature, GearboxBearingTemperature, GearboxOilTemperature, GeneratorRP, GeneratorWinding1Temperature, GeneratorWinding2Temperature, HubTemperature, MainBoxTemperature, NacellePosition, ReactivePower, RotorRPM, TurbineStatus, WTG, WindDirection, and WindSpeed. To perform forecasting based on regression active power, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Naïve Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, and MLP regression. To perform clustering, you will use K-Means algorithm. The machine learning models used predict categorized active power as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, 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: MACHINE LEARNING FOR CONCRETE COMPRESSIVE STRENGTH ANALYSIS AND PREDICTION WITH PYTHON Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. These ingredients include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate. The actual concrete compressive strength (MPa) for a given mixture under a specific age (days) was determined from laboratory. This dataset is in raw form (not scaled). There are 1030 observations, 9 attributes, 8 quantitative input variables, and 1 quantitative output variable in dataset. The attributes in the dataset are as follows: Cement (component 1); Blast Furnace Slag (component 2); Fly Ash (component 3); Water (component 4); Superplasticizer (component 5); Coarse Aggregate; Fine Aggregate (component 7); Age; and Concrete compressive strength. To perform regression on concrete compressive strength, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Naïve Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, and MLP regression. To perform clustering, you will use K-Means algorithm. The machine learning models used predict clusters as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, 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 4: DATA SCIENCE FOR SALES ANALYSIS, FORECASTING, CLUSTERING, AND PREDICTION WITH PYTHON The dataset used in this project is from Walmart which is a renowned retail corporation that operates a chain of hypermarkets. Walmart has provided a data combining of 45 stores including store information and monthly sales. The data is provided on weekly basis. Walmart tries to find the impact of holidays on the sales of store. For which it has included four holidays’ weeks into the dataset which are Christmas, Thanksgiving, Super bowl, Labor Day. In this project, you are going to analyze, forecast weekly sales, perform clustering, and predict the resulting clusters. The dataset covers sales from 2010-02-05 to 2012-11-01. Following are the attributes in the dataset: Store - the store number; Date - the week of sales; Weekly_Sales - sales for the given store; Holiday_Flag - whether the week is a special holiday week 1 – Holiday week 0 – Non-holiday week; Temperature - Temperature on the day of sale; Fuel_Price - Cost of fuel in the region; CPI – Prevailing consumer price index; and Unemployment - Prevailing unemployment rate. To perform regression on weekly sales, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Naïve Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, and MLP regression. To perform clustering, you will use K-Means algorithm. The machine learning models used predict clusters as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, 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 DATA SCIENCE FOR GROCERIES MARKET ANALYSIS CLUSTERING AND PREDICTION WITH PYTHON GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2022-05-03 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this data science project is to analyze and predict customer behavior in the groceries market using Python and create a graphical user interface (GUI) using PyQt. The project encompasses various stages, starting from exploring the dataset and visualizing the distribution of features to RFM analysis, K-means clustering, predicting clusters with machine learning algorithms, and implementing a GUI for user interaction. The first step in this project involves exploring the dataset. We load the dataset containing information about customers' purchases in the groceries market and examine its structure. We check for missing values and perform data preprocessing if necessary, ensuring the dataset is ready for analysis. This initial exploration allows us to gain a better understanding of the data and its characteristics. Following the dataset exploration, we conduct exploratory data analysis (EDA). This step involves visualizing the distribution of different features within the dataset. By creating histograms, box plots, scatter plots, and other visualizations, we gain insights into the patterns, trends, and relationships within the data. EDA helps us identify outliers, understand feature distributions, and uncover potential correlations between variables. After the EDA phase, we move on to RFM analysis. RFM stands for Recency, Frequency, and Monetary analysis. In this step, we calculate three key metrics for each customer: recency (how recently a customer made a purchase), frequency (how often a customer made purchases), and monetary value (how much a customer spent). RFM analysis allows us to segment customers based on their purchasing behavior, identifying high-value customers and those who require re-engagement strategies. Once we have the clusters, we can utilize machine learning algorithms to predict the cluster for new or unseen customers. We train various models, including logistic regression, support vector machines, decision trees, k-nearest neighbors, random forests, gradient boosting, naive Bayes, adaboost, XGBoost, and LightGBM, on the clustered data. These models learn the patterns and relationships between customer features and their assigned clusters, enabling us to predict the cluster for new customers accurately. To evaluate the performance of our models, we utilize metrics such as accuracy, precision, recall, and F1-score. These metrics allow us to measure the models' predictive capabilities and compare their performance across different algorithms and preprocessing techniques. By assessing the models' performance, we can select the most suitable model for cluster prediction in the groceries market analysis. In addition to the analysis and prediction components, this project aims to provide a user-friendly interface for interaction and visualization. To achieve this, we implement a GUI using PyQt, a Python library for creating desktop applications. The GUI allows users to input new customer data and predict the corresponding cluster based on the trained models. It provides visualizations of the analysis results, including cluster distributions, confusion matrices, and decision boundaries. The GUI allows users to select different machine learning models and preprocessing techniques through radio buttons or dropdown menus. This flexibility empowers users to explore and compare the performance of various models, enabling them to choose the most suitable approach for their specific needs. The GUI's interactive nature enhances the usability of the project and promotes effective decision-making based on the analysis results. In conclusion, this project combines data science methodologies, including dataset exploration, visualization, RFM analysis, K-means clustering, predictive modeling, and GUI implementation, to provide insights into customer behavior and enable accurate cluster prediction in the groceries market. By leveraging these techniques, businesses can enhance their marketing strategies, improve customer targeting and retention, and ultimately drive growth and profitability in a competitive market landscape. The project's emphasis on user interaction and visualization through the GUI ensures that businesses can easily access and interpret the analysis results, making informed decisions based on data-driven insights.

Download or read book Mastering Predictive Analytics with Python written by Joseph Babcock and published by Packt Publishing Ltd. This book was released on 2016-08-31 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exploit the power of data in your business by building advanced predictive modeling applications with Python About This Book Master open source Python tools to build sophisticated predictive models Learn to identify the right machine learning algorithm for your problem with this forward-thinking guide Grasp the major methods of predictive modeling and move beyond the basics to a deeper level of understanding Who This Book Is For This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move from a conceptual understanding of advanced analytics to an expert in designing and building advanced analytics solutions using Python. You're expected to have basic development experience with Python. What You Will Learn Gain an insight into components and design decisions for an analytical application Master the use Python notebooks for exploratory data analysis and rapid prototyping Get to grips with applying regression, classification, clustering, and deep learning algorithms Discover the advanced methods to analyze structured and unstructured data Find out how to deploy a machine learning model in a production environment Visualize the performance of models and the insights they produce Scale your solutions as your data grows using Python Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis In Detail The volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations. In Mastering Predictive Analytics with Python, you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to life Style and approach This book emphasizes on explaining methods through example data and code, showing you templates that you can quickly adapt to your own use cases. It focuses on both a practical application of sophisticated algorithms and the intuitive understanding necessary to apply the correct method to the problem at hand. Through visual examples, it also demonstrates how to convey insights through insightful charts and reporting.

Download or read book THREE DATA SCIENCE PROJECTS FOR RFM ANALYSIS K MEANS CLUSTERING AND MACHINE LEARNING BASED PREDICTION WITH PYTHON GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2022-05-11 with total page 627 pages. Available in PDF, EPUB and Kindle. Book excerpt: PROJECT 1: RFM ANALYSIS AND K-MEANS CLUSTERING: A CASE STUDY ANALYSIS, CLUSTERING, AND PREDICTION ON RETAIL STORE TRANSACTIONS WITH PYTHON GUI The dataset used in this project is the detailed data on sales of consumer goods obtained by ‘scanning’ the bar codes for individual products at electronic points of sale in a retail store. The dataset provides detailed information about quantities, characteristics and values of goods sold as well as their prices. The anonymized dataset includes 64.682 transactions of 5.242 SKU's sold to 22.625 customers during one year. Dataset Attributes are as follows: Date of Sales Transaction, Customer ID, Transaction ID, SKU Category ID, SKU ID, Quantity Sold, and Sales Amount (Unit price times quantity. For unit price, please divide Sales Amount by Quantity). This dataset can be analyzed with RFM analysis and can be clustered using K-Means algorithm. The machine learning models used in this project 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 2: DATA SCIENCE FOR GROCERIES MARKET ANALYSIS, CLUSTERING, AND PREDICTION WITH PYTHON GUI RFM analysis used in this project can be used as a marketing technique used to quantitatively rank and group customers based on the recency, frequency and monetary total of their recent transactions to identify the best customers and perform targeted marketing campaigns. The idea is to segment customers based on when their last purchase was, how often they've purchased in the past, and how much they've spent overall. Clustering, in this case K-Means algorithm, used in this project can be used to place similar customers into mutually exclusive groups; these groups are known as “segments” while the act of grouping is known as segmentation. Segmentation allows businesses to identify the different types and preferences of customers/markets they serve. This is crucial information to have to develop highly effective marketing, product, and business strategies. The dataset in this project has 38765 rows of the purchase orders of people from the grocery stores. These orders can be analyzed with RFM analysis and can be clustered using K-Means algorithm. The machine learning models used in this project 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 3: ONLINE RETAIL CLUSTERING AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI The dataset used in this project is a transnational dataset which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers. You will be using the online retail transnational dataset to build a RFM clustering and choose the best set of customers which the company should target. In this project, you will perform Cohort analysis and RFM analysis. You will also perform clustering using K-Means to get 5 clusters. The machine learning models used in this project 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 Data Science Essentials in Python written by Dmitry Zinoviev and published by Pragmatic Bookshelf. This book was released on 2016-08-10 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python. Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects. Keep Python data-science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data. This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data. Work with relational and non-relational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of medium-scale projects that are fun to work on and look good on your resume. Keep this handy quick guide at your side whether you're a student, an entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn't want to memorize every function and option. What You Need: You need a decent distribution of Python 3.3 or above that includes at least NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. A great distribution that meets the requirements is Anaconda, available for free from www.continuum.io. If you plan to set up your own database servers, you also need MySQL (www.mysql.com) and MongoDB (www.mongodb.com). Both packages are free and run on Windows, Linux, and Mac OS.

Download or read book Data Science for Marketing Analytics written by Mirza Rahim Baig and published by Packt Publishing Ltd. This book was released on 2021-09-07 with total page 637 pages. Available in PDF, EPUB and Kindle. Book excerpt: Turbocharge your marketing plans by making the leap from simple descriptive statistics in Excel to sophisticated predictive analytics with the Python programming language Key FeaturesUse data analytics and machine learning in a sales and marketing contextGain insights from data to make better business decisionsBuild your experience and confidence with realistic hands-on practiceBook Description Unleash the power of data to reach your marketing goals with this practical guide to data science for business. This book will help you get started on your journey to becoming a master of marketing analytics with Python. You'll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects. You'll learn to think like a data scientist, build your problem-solving skills, and discover how to look at data in new ways to deliver business insights and make intelligent data-driven decisions. As well as learning how to clean, explore, and visualize data, you'll implement machine learning algorithms and build models to make predictions. As you work through the book, you'll use Python tools to analyze sales, visualize advertising data, predict revenue, address customer churn, and implement customer segmentation to understand behavior. By the end of this book, you'll have the knowledge, skills, and confidence to implement data science and machine learning techniques to better understand your marketing data and improve your decision-making. What you will learnLoad, clean, and explore sales and marketing data using pandasForm and test hypotheses using real data sets and analytics toolsVisualize patterns in customer behavior using MatplotlibUse advanced machine learning models like random forest and SVMUse various unsupervised learning algorithms for customer segmentationUse supervised learning techniques for sales predictionEvaluate and compare different models to get the best outcomesOptimize models with hyperparameter tuning and SMOTEWho this book is for This marketing book is for anyone who wants to learn how to use Python for cutting-edge marketing analytics. Whether you're a developer who wants to move into marketing, or a marketing analyst who wants to learn more sophisticated tools and techniques, this book will get you on the right path. Basic prior knowledge of Python and experience working with data will help you access this book more easily.

Download or read book Data Mining for Business Analytics written by Galit Shmueli and published by John Wiley & Sons. This book was released on 2019-10-14 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R

Download or read book Modeling Techniques in Predictive Analytics with Python and R written by Thomas W. Miller and published by FT Press. This book was released on 2014-09-29 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Download or read book Data Driven Dealings Development written by Jesko Rehberg and published by . This book was released on 2020-12-11 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: When I was starting my Python data analysis journey I was missing books or tutorials which really cover all the topics involved when trying to conduct a sales analysis successfully. Especially when you are a complete newbie conducting an analysis from A-Z without any (or not sufficient) pre-knowledge is difficult, because most books only cover specific parts of the whole project, and it is challenging to put all the pieces of the puzzle together. That is my main motivation for writing this book: you to have one guideline in hand which leads all the way through your whole sales analysis project, from installing all the necessary Python libraries, cleaning the data, effectively training the Machine Learning (ML) models and deploying the results to your colleagues in an intelligible way. The topics covered in this book are: Explorative Data Analysis (EDA)Feature Engineering and Clustering (Unsupervised Machine Learning) Predicting of future sales using statistical modelling, Prophet, and Long-Short-Term Memory (LSTM) using deep learning techniques (Tensorflow/ Keras) Market Basket Analysis using the Apriori Algorithm and Spark Recommend products to our customers using Scikit-Learn, Pandas, Tensorflow, and Turicreate Stac

Download or read book Advancement of Intelligent Computational Methods and Technologies written by O.P. Verma and published by CRC Press. This book was released on 2024-06-30 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: The compiled volume originates from the notable contributions presented at the 1st International Conference on Advancementof Intelligent Computational Methods and Technologies (AICMT2023), which took place in a hybrid format on June 27, 2023,at Delhi Technical Campus, Greater Noida, Uttar Pradesh, India. This comprehensive collection serves as an exploration into the dynamic domain of intelligent computational methods and technologies, offering insights into the latest and upcoming trends in computation methods. AICMT2023’s scope encompasses the evolutionary trajectory of computational methods, addressing pertinent issues in real time implementation, delving into the emergence of new intelligent technologies, exploring next-generation problem-solving methodologies, and other interconnected areas. The conference is strategically designed to spotlight current research trendswithin the field, fostering a vibrant research culture and contributing to the collective knowledge base.

Download or read book Data Science and Machine Learning written by Dirk P. Kroese and published by CRC Press. This book was released on 2019-11-20 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

Download or read book Mastering Python for Data Science written by Samir Madhavan and published by Packt Publishing Ltd. This book was released on 2015-08-31 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the world of data science through Python and learn how to make sense of data About This Book Master data science methods using Python and its libraries Create data visualizations and mine for patterns Advanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learning Who This Book Is For If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed. What You Will Learn Manage data and perform linear algebra in Python Derive inferences from the analysis by performing inferential statistics Solve data science problems in Python Create high-end visualizations using Python Evaluate and apply the linear regression technique to estimate the relationships among variables. Build recommendation engines with the various collaborative filtering algorithms Apply the ensemble methods to improve your predictions Work with big data technologies to handle data at scale In Detail Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving. This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science. Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods. Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics. Style and approach This book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios.

Download or read book Data Science Projects with Python written by Stephen Klosterman and published by Packt Publishing Ltd. This book was released on 2019-04-30 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain hands-on experience with industry-standard data analysis and machine learning tools in Python Key FeaturesTackle data science problems by identifying the problem to be solvedIllustrate patterns in data using appropriate visualizationsImplement suitable machine learning algorithms to gain insights from dataBook Description Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to critically examine datasets with summary statistics and graphs, and extract the insights you seek to derive. You will build your knowledge as you prepare data using the scikit-learn package and feed it to machine learning algorithms such as regularized logistic regression and random forest. You’ll discover how to tune algorithms to provide the most accurate predictions on new and unseen data. As you progress, you’ll gain insights into the working and output of these algorithms, building your understanding of both the predictive capabilities of the models and why they make these predictions. By then end of this book, you will have the necessary skills to confidently use machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data. What you will learnInstall the required packages to set up a data science coding environmentLoad data into a Jupyter notebook running PythonUse Matplotlib to create data visualizationsFit machine learning models using scikit-learnUse lasso and ridge regression to regularize your modelsCompare performance between models to find the best outcomesUse k-fold cross-validation to select model hyperparametersWho this book is for If you are a data analyst, data scientist, or business analyst who wants to get started using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of Python and data analytics will help you get the most from this book. Familiarity with mathematical concepts such as algebra and basic statistics will also be useful.

Download or read book Introduction to Data Science written by Laura Igual and published by Springer. This book was released on 2017-02-22 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.

Download or read book Python for Marketing Research and Analytics written by Jason S. Schwarz and published by Springer Nature. This book was released on 2020-11-03 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to quantitative marketing with Python. The book presents a hands-on approach to using Python for real marketing questions, organized by key topic areas. Following the Python scientific computing movement toward reproducible research, the book presents all analyses in Colab notebooks, which integrate code, figures, tables, and annotation in a single file. The code notebooks for each chapter may be copied, adapted, and reused in one's own analyses. The book also introduces the usage of machine learning predictive models using the Python sklearn package in the context of marketing research. This book is designed for three groups of readers: experienced marketing researchers who wish to learn to program in Python, coming from tools and languages such as R, SAS, or SPSS; analysts or students who already program in Python and wish to learn about marketing applications; and undergraduate or graduate marketing students with little or no programming background. It presumes only an introductory level of familiarity with formal statistics and contains a minimum of mathematics.