Time-Series Sales Forecasting and Prediction Using Machine Learning with Tkinter PDF Download

Author: Rismon Hasiholan Sianipar
Publisher: Independently Published
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
This project leverages the power of data visualization and exploration to provide a comprehensive understanding of sales trends over time. Through an intuitive GUI built with Tkinter, users can seamlessly navigate through various aspects of their sales data. The journey begins with a detailed visualization of the dataset. This critical step allows users to grasp the overall structure, identify trends, and spot outliers. The application provides a user-friendly interface to interact with the data, offering an informative visual representation of the sales records. Moving forward, users can delve into the distribution of features within the dataset. This feature distribution analysis provides valuable insights into the characteristics of the sales data. It enables users to identify patterns, anomalies, and correlations among different attributes, paving the way for more accurate forecasting and prediction. One of the central functionalities of this application lies in its ability to perform sales forecasting using machine learning regressors. By employing powerful regression models, such as Random Forest Regressor, KNN regressor, Support Vector Regressor, AdaBoost regressor, Gradient Boosting Regressor, MLP regressor, Lasso regressor, and Ridge regressor, the application assists users in predicting future sales based on historical data. This empowers businesses to make informed decisions and plan for upcoming periods with greater precision. The application takes sales forecasting a step further by allowing users to fine-tune their models using Grid Search. This powerful optimization technique systematically explores different combinations of hyperparameters to find the optimal configuration for the machine learning models. This ensures that the models are fine-tuned for maximum accuracy in sales predictions. In addition to sales forecasting, the application addresses the critical issue of customer churn prediction. It identifies customers who are likely to churn based on a combination of features and behaviors. By employing a selection of machine learning models and Grid Search such as Random Forest Classifier, Support Vector Classifier, and K-Nearest Neighbors Classifier, Linear Regression Classifier, AdaBoost Classifier, Support Vector Classifier, Gradient Boosting Classifier, Extreme Gradient Boosting Classifier, and Multi-Layer Perceptron Classifier, the application provides a robust framework for accurately predicting which customers are at risk of leaving. The project doesn't just stop at prediction; it also includes functionalities for evaluating model performance. Users can assess the accuracy, precision, recall, and F1-score of their models, allowing them to gauge the effectiveness of their forecasting and customer churn predictions. Furthermore, the application incorporates an intuitive user interface with widgets such as menus, buttons, listboxes, and comboboxes. These elements facilitate seamless interaction and navigation within the application, ensuring a user-friendly experience. To enhance user convenience, the application also supports data loading from external sources. It enables users to import their sales datasets directly into the application, streamlining the analysis process. The project is built on a foundation of modular and organized code. Each functionality is encapsulated within separate classes, promoting code reusability and maintainability. This ensures that the application is robust and can be easily extended or modified to accommodate future enhancements.

Author: Vivian Siahaan
Publisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 274

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Book Description
This project leverages the power of data visualization and exploration to provide a comprehensive understanding of sales trends over time. Through an intuitive GUI built with Tkinter, users can seamlessly navigate through various aspects of their sales data. The journey begins with a detailed visualization of the dataset. This critical step allows users to grasp the overall structure, identify trends, and spot outliers. The application provides a user-friendly interface to interact with the data, offering an informative visual representation of the sales records. Moving forward, users can delve into the distribution of features within the dataset. This feature distribution analysis provides valuable insights into the characteristics of the sales data. It enables users to identify patterns, anomalies, and correlations among different attributes, paving the way for more accurate forecasting and prediction. One of the central functionalities of this application lies in its ability to perform sales forecasting using machine learning regressors. By employing powerful regression models, such as Random Forest Regressor, KNN regressor, Support Vector Regressor, AdaBoost regressor, Gradient Boosting Regressor, MLP regressor, Lasso regressor, and Ridge regressor, the application assists users in predicting future sales based on historical data. This empowers businesses to make informed decisions and plan for upcoming periods with greater precision. The application takes sales forecasting a step further by allowing users to fine-tune their models using Grid Search. This powerful optimization technique systematically explores different combinations of hyperparameters to find the optimal configuration for the machine learning models. This ensures that the models are fine-tuned for maximum accuracy in sales predictions. In addition to sales forecasting, the application addresses the critical issue of customer churn prediction. It identifies customers who are likely to churn based on a combination of features and behaviors. By employing a selection of machine learning models and Grid Search such as Random Forest Classifier, Support Vector Classifier, and K-Nearest Neighbors Classifier, Linear Regression Classifier, AdaBoost Classifier, Support Vector Classifier, Gradient Boosting Classifier, Extreme Gradient Boosting Classifier, and Multi-Layer Perceptron Classifier, the application provides a robust framework for accurately predicting which customers are at risk of leaving. The project doesn't just stop at prediction; it also includes functionalities for evaluating model performance. Users can assess the accuracy, precision, recall, and F1-score of their models, allowing them to gauge the effectiveness of their forecasting and customer churn predictions. Furthermore, the application incorporates an intuitive user interface with widgets such as menus, buttons, listboxes, and comboboxes. These elements facilitate seamless interaction and navigation within the application, ensuring a user-friendly experience. To enhance user convenience, the application also supports data loading from external sources. It enables users to import their sales datasets directly into the application, streamlining the analysis process. The project is built on a foundation of modular and organized code. Each functionality is encapsulated within separate classes, promoting code reusability and maintainability. This ensures that the application is robust and can be easily extended or modified to accommodate future enhancements. You can download the dataset from: http://viviansiahaan.blogspot.com/2023/09/time-series-sales-forecasting-and.html.

Author: Vivian Siahaan
Publisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 267

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Book Description
This "Data Visualization, Time-Series Forecasting, and Prediction using Machine Learning with Tkinter" project is a comprehensive and multifaceted application that leverages data visualization, time-series forecasting, and machine learning techniques to gain insights into bitcoin data and make predictions. This project serves as a valuable tool for financial analysts, traders, and investors seeking to make informed decisions in the stock market. The project begins with data visualization, where historical bitcoin market data is visually represented using various plots and charts. This provides users with an intuitive understanding of the data's trends, patterns, and fluctuations. Features distribution analysis is conducted to assess the statistical properties of the dataset, helping users identify key characteristics that may impact forecasting and prediction. One of the project's core functionalities is time-series forecasting. Through a user-friendly interface built with Tkinter, users can select a stock symbol and specify the time horizon for forecasting. The project supports multiple machine learning regressors, such as Linear Regression, Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Multi-Layer Perceptron, Lasso, Ridge, AdaBoost, and KNN, allowing users to choose the most suitable algorithm for their forecasting needs. Time-series forecasting is crucial for making predictions about stock prices, which is essential for investment strategies. The project employs various machine learning regressors to predict the adjusted closing price of bitcoin stock. By training these models on historical data, users can obtain predictions for future adjusted closing prices. This information is invaluable for traders and investors looking to make buy or sell decisions. The project also incorporates hyperparameter tuning and cross-validation to enhance the accuracy of these predictions. These models employ metrics such as Mean Absolute Error (MAE), which quantifies the average absolute discrepancy between predicted values and actual values. Lower MAE values signify superior model performance. Additionally, Mean Squared Error (MSE) is used to calculate the average squared differences between predicted and actual values, with lower MSE values indicating better model performance. Root Mean Squared Error (RMSE), derived from MSE, provides insights in the same units as the target variable and is valued for its lower values, denoting superior performance. Lastly, R-squared (R2) evaluates the fraction of variance in the target variable that can be predicted from independent variables, with higher values signifying better model fit. An R2 of 1 implies a perfect model fit. In addition to close price forecasting, the project extends its capabilities to predict daily returns. By implementing grid search, users can fine-tune the hyperparameters of machine learning models such as Random Forests, Gradient Boosting, Support Vector, Decision Tree, Gradient Boosting, Extreme Gradient Boosting, Multi-Layer Perceptron, and AdaBoost Classifiers. This optimization process aims to maximize the predictive accuracy of daily returns. Accurate daily return predictions are essential for assessing risk and formulating effective trading strategies. Key metrics in these classifiers encompass Accuracy, which represents the ratio of correctly predicted instances to the total number of instances, Precision, which measures the proportion of true positive predictions among all positive predictions, and Recall (also known as Sensitivity or True Positive Rate), which assesses the proportion of true positive predictions among all actual positive instances. The F1-Score serves as the harmonic mean of Precision and Recall, offering a balanced evaluation, especially when considering the trade-off between false positives and false negatives. The ROC Curve illustrates the trade-off between Recall and False Positive Rate, while the Area Under the ROC Curve (AUC-ROC) summarizes this trade-off. The Confusion Matrix provides a comprehensive view of classifier performance by detailing true positives, true negatives, false positives, and false negatives, facilitating the computation of various metrics like accuracy, precision, and recall. The selection of these metrics hinges on the project's specific objectives and the characteristics of the dataset, ensuring alignment with the intended goals and the ramifications of false positives and false negatives, which hold particular significance in financial contexts where decisions can have profound consequences. Overall, the "Data Visualization, Time-Series Forecasting, and Prediction using Machine Learning with Tkinter" project serves as a powerful and user-friendly platform for financial data analysis and decision-making. It bridges the gap between complex machine learning techniques and accessible user interfaces, making financial analysis and prediction more accessible to a broader audience. With its comprehensive features, this project empowers users to gain insights from historical data, make informed investment decisions, and develop effective trading strategies in the dynamic world of finance. You can download the dataset from: http://viviansiahaan.blogspot.com/2023/09/data-visualization-time-series.html.

Author: Jason Brownlee
Publisher: Machine Learning Mastery
ISBN:
Category : Mathematics
Languages : en
Pages : 359

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Book Description
Time series forecasting is different from other machine learning problems. The key difference is the fixed sequence of observations and the constraints and additional structure this provides. In this Ebook, finally cut through the math and specialized methods for time series forecasting. Using clear explanations, standard Python libraries and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement forecasting models for time series data.

Author: Francesca Lazzeri
Publisher: John Wiley & Sons
ISBN: 1119682363
Category : Computers
Languages : en
Pages : 224

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Book Description
Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting. Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to: Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models’ performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.

Author: Marco Peixeiro
Publisher: Simon and Schuster
ISBN: 1638351473
Category : Computers
Languages : en
Pages : 454

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Book Description
Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting. In Time Series Forecasting in Python you will learn how to: Recognize a time series forecasting problem and build a performant predictive model Create univariate forecasting models that account for seasonal effects and external variables Build multivariate forecasting models to predict many time series at once Leverage large datasets by using deep learning for forecasting time series Automate the forecasting process Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You’ll explore interesting real-world datasets like Google’s daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow. About the technology You can predict the future—with a little help from Python, deep learning, and time series data! Time series forecasting is a technique for modeling time-centric data to identify upcoming events. New Python libraries and powerful deep learning tools make accurate time series forecasts easier than ever before. About the book Time Series Forecasting in Python teaches you how to get immediate, meaningful predictions from time-based data such as logs, customer analytics, and other event streams. In this accessible book, you’ll learn statistical and deep learning methods for time series forecasting, fully demonstrated with annotated Python code. Develop your skills with projects like predicting the future volume of drug prescriptions, and you’ll soon be ready to build your own accurate, insightful forecasts. What's inside Create models for seasonal effects and external variables Multivariate forecasting models to predict multiple time series Deep learning for large datasets Automate the forecasting process About the reader For data scientists familiar with Python and TensorFlow. About the author Marco Peixeiro is a seasoned data science instructor who has worked as a data scientist for one of Canada’s largest banks. Table of Contents PART 1 TIME WAITS FOR NO ONE 1 Understanding time series forecasting 2 A naive prediction of the future 3 Going on a random walk PART 2 FORECASTING WITH STATISTICAL MODELS 4 Modeling a moving average process 5 Modeling an autoregressive process 6 Modeling complex time series 7 Forecasting non-stationary time series 8 Accounting for seasonality 9 Adding external variables to our model 10 Forecasting multiple time series 11 Capstone: Forecasting the number of antidiabetic drug prescriptions in Australia PART 3 LARGE-SCALE FORECASTING WITH DEEP LEARNING 12 Introducing deep learning for time series forecasting 13 Data windowing and creating baselines for deep learning 14 Baby steps with deep learning 15 Remembering the past with LSTM 16 Filtering a time series with CNN 17 Using predictions to make more predictions 18 Capstone: Forecasting the electric power consumption of a household PART 4 AUTOMATING FORECASTING AT SCALE 19 Automating time series forecasting with Prophet 20 Capstone: Forecasting the monthly average retail price of steak in Canada 21 Going above and beyond

Author: Changquan Huang
Publisher: Springer Nature
ISBN: 3031135849
Category : Mathematics
Languages : en
Pages : 377

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Book Description
This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.

Author: Greg Rafferty
Publisher: Packt Publishing Ltd
ISBN: 1800566522
Category : Computers
Languages : en
Pages : 270

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Book Description
Create and improve high-quality automated forecasts for time series data that have strong seasonal effects, holidays, and additional regressors using Python Key Features Learn how to use the open-source forecasting tool Facebook Prophet to improve your forecasts Build a forecast and run diagnostics to understand forecast quality Fine-tune models to achieve high performance, and report that performance with concrete statistics Book Description Prophet enables Python and R developers to build scalable time series forecasts. This book will help you to implement Prophet's cutting-edge forecasting techniques to model future data with higher accuracy and with very few lines of code. You will begin by exploring the evolution of time series forecasting, from the basic early models to the advanced models of the present day. The book will demonstrate how to install and set up Prophet on your machine and build your first model with only a few lines of code. You'll then cover advanced features such as visualizing your forecasts, adding holidays, seasonality, and trend changepoints, handling outliers, and more, along with understanding why and how to modify each of the default parameters. Later chapters will show you how to optimize more complicated models with hyperparameter tuning and by adding additional regressors to the model. Finally, you'll learn how to run diagnostics to evaluate the performance of your models and see some useful features when running Prophet in production environments. By the end of this Prophet book, you will be able to take a raw time series dataset and build advanced and accurate forecast models with concise, understandable, and repeatable code. What you will learn Gain an understanding of time series forecasting, including its history, development, and uses Understand how to install Prophet and its dependencies Build practical forecasting models from real datasets using Python Understand the Fourier series and learn how it models seasonality Decide when to use additive and when to use multiplicative seasonality Discover how to identify and deal with outliers in time series data Run diagnostics to evaluate and compare the performance of your models Who this book is for This book is for data scientists, data analysts, machine learning engineers, software engineers, project managers, and business managers who want to build time series forecasts in Python. Working knowledge of Python and a basic understanding of forecasting principles and practices will be useful to apply the concepts covered in this book more easily.

Author: Joos Korstanje
Publisher: Apress
ISBN: 9781484271490
Category : Computers
Languages : en
Pages : 296

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Book Description
Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook’s open-source Prophet model, and Amazon’s DeepAR model. Rather than focus on a specific set of models, this book presents an exhaustive overview of all the techniques relevant to practitioners of forecasting. It begins by explaining the different categories of models that are relevant for forecasting in a high-level language. Next, it covers univariate and multivariate time series models followed by advanced machine learning and deep learning models. It concludes with reflections on model selection such as benchmark scores vs. understandability of models vs. compute time, and automated retraining and updating of models. Each of the models presented in this book is covered in depth, with an intuitive simple explanation of the model, a mathematical transcription of the idea, and Python code that applies the model to an example data set. Reading this book will add a competitive edge to your current forecasting skillset. The book is also adapted to those who have recently started working on forecasting tasks and are looking for an exhaustive book that allows them to start with traditional models and gradually move into more and more advanced models. What You Will Learn Carry out forecasting with Python Mathematically and intuitively understand traditional forecasting models and state-of-the-art machine learning techniques Gain the basics of forecasting and machine learning, including evaluation of models, cross-validation, and back testing Select the right model for the right use case Who This Book Is For The advanced nature of the later chapters makes the book relevant for applied experts working in the domain of forecasting, as the models covered have been published only recently. Experts working in the domain will want to update their skills as traditional models are regularly being outperformed by newer models.

Author: Vivian Siahaan
Publisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 463

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Book Description
Stock trading and financial instrument markets offer significant opportunities for wealth creation. The ability to predict stock price movements has long intrigued researchers and investors alike. While some theories, like the Efficient Market Hypothesis, suggest that consistently beating the market is nearly impossible, others contest this viewpoint. Stock price prediction involves forecasting the future value of a given stock. In this project, we focus on the S&P 500 Index, which consists of 500 stocks from various sectors of the US economy and serves as a key indicator of US equities. To tackle this task, we utilize the Yahoo stock price history dataset, which contains 1825 rows and 7 columns including Date, High, Low, Open, Close, Volume, and Adj Close. To enhance our predictions, we incorporate technical indicators such as daily returns, Moving Average Convergence-Divergence (MACD), Relative Strength Index (RSI), Simple Moving Average (SMA), lower and upper bands, and standard deviation. In this book, for the forecasting task, we employ various regression algorithms including Linear Regression, Random Forest Regression, Decision Tree Regression, Support Vector Regression, Naïve Bayes Regression, K-Nearest Neighbor Regression, Adaboost Regression, Gradient Boosting Regression, Extreme Gradient Boosting Regression, Light Gradient Boosting Regression, Catboost Regression, MLP Regression, Lasso Regression, and Ridge Regression. These models aim to predict the future Adj Close price of the stock based on historical data. In addition to stock price prediction, we also delve into predicting stock daily returns using machine learning models. We utilize 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. These models are trained to predict the direction of daily stock returns (positive or negative) based on various features and technical indicators. To assess the performance of these machine learning models, we evaluate several important metrics. Accuracy measures the overall correctness of the predictions, while recall quantifies the ability to correctly identify positive cases (upward daily returns). Precision evaluates the precision of positive predictions, and the F1 score provides a balanced measure of precision and recall. Additionally, we consider macro average, which calculates the average metric value across all classes, and weighted average, which provides a balanced representation considering class imbalances. To enhance the user experience and facilitate data exploration, we develop a graphical user interface (GUI). The GUI is built using PyQt and offers an interactive platform for users to visualize and interact with the data. It provides features such as plotting boundary decisions, visualizing feature distributions and importance, comparing predicted values with true values, displaying confusion matrices, learning curves, model performance, and scalability analysis. The GUI allows users to customize the analysis by selecting different models, time periods, or variables of interest, making it accessible and user-friendly for individuals without extensive programming knowledge. The combination of exploring the dataset, forecasting stock prices, predicting daily returns, and developing a GUI creates a comprehensive framework for analyzing and understanding stock market trends. By leveraging machine learning algorithms and evaluating performance metrics, we gain valuable insights into the accuracy and effectiveness of our predictions. The GUI further enhances the accessibility and usability of the analysis, enabling users to make data-driven decisions and explore the stock market with ease.