Data Science For Sales Analysis Forecasting Clustering And Prediction With Python
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Data Science For Sales Analysis Forecasting Clustering And Prediction With Python
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-07-28
Data Science For Sales Analysis Forecasting Clustering And Prediction With Python written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-28 with Computers categories.
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.
Four Projects Prediction And Forecasting Using Machine Learning With Python
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-05-25
Four Projects Prediction And Forecasting Using Machine Learning With Python written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-25 with Computers categories.
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.
Data Science For Groceries Market Analysis Clustering And Prediction With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-05-03
Data Science For Groceries Market Analysis Clustering And Prediction With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-03 with Computers categories.
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.
Advancement Of Intelligent Computational Methods And Technologies
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Author : O.P. Verma
language : en
Publisher: CRC Press
Release Date : 2024-06-30
Advancement Of Intelligent Computational Methods And Technologies written by O.P. Verma and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-30 with Computers categories.
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.
Three Data Science Projects For Rfm Analysis K Means Clustering And Machine Learning Based Prediction With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-05-11
Three Data Science Projects For Rfm Analysis K Means Clustering And Machine Learning Based Prediction With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-11 with Computers categories.
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.
Supermarket Sales Analysis And Prediction Using Machine Learning With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-04-15
Supermarket Sales Analysis And Prediction Using Machine Learning With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-15 with Computers categories.
The dataset used in this project consists of the growth of supermarkets with high market competitions in most populated cities. The dataset is one of the historical sales of supermarket company which has recorded in 3 different branches for 3 months data. Predictive data analytics methods are easy to apply with this dataset. Attribute information in the dataset are as follows: Invoice id: Computer generated sales slip invoice identification number; Branch: Branch of supercenter (3 branches are available identified by A, B and C); City: Location of supercenters; Customer type: Type of customers, recorded by Members for customers using member card and Normal for without member card; Gender: Gender type of customer; Product line: General item categorization groups - Electronic accessories, Fashion accessories, Food and beverages, Health and beauty, Home and lifestyle, Sports and travel; Unit price: Price of each product in $; Quantity: Number of products purchased by customer; Tax: 5% tax fee for customer buying; Total: Total price including tax; Date: Date of purchase (Record available from January 2019 to March 2019); Time: Purchase time (10am to 9pm); Payment: Payment used by customer for purchase (3 methods are available – Cash, Credit card and Ewallet); COGS: Cost of goods sold; Gross margin percentage: Gross margin percentage; Gross income: Gross income; and Rating: Customer stratification rating on their overall shopping experience (On a scale of 1 to 10). In this project, you will perform predicting rating using machine learning. 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.
Machine Learning And Modeling Techniques In Financial Data Science
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Author : Chen, Haojun
language : en
Publisher: IGI Global
Release Date : 2025-01-22
Machine Learning And Modeling Techniques In Financial Data Science written by Chen, Haojun and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-22 with Business & Economics categories.
The integration of machine learning and modeling in finance is transforming how data is analyzed, enabling more accurate predictions, risk assessments, and strategic planning. These advanced techniques empower financial professionals to uncover hidden patterns, automate complex processes, and enhance decision-making in volatile markets. As industries increasingly rely on data-driven insights, the adoption of these tools contributes to greater efficiency, reduced uncertainty, and competitive advantage. This technological shift not only drives innovation within financial sectors but also supports broader economic stability and growth by improving forecasting and mitigating risks. Machine Learning and Modeling Techniques in Financial Data Science provides an updated review and highlights recent theoretical advances and breakthroughs in professional practices within financial data science, exploring the strategic roles of machine learning and modeling techniques across various domains in finance. It offers a comprehensive collection that brings together a wealth of knowledge and experience. Covering topics such as algorithmic trading, financial technology (FinTech), and natural language processing (NLP), this book is an excellent resource for business professionals, leaders, policymakers, researchers, academicians, and more.
Data Analytics In Finance
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Author : Huijian Dong
language : en
Publisher: CRC Press
Release Date : 2025-04-30
Data Analytics In Finance written by Huijian Dong and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-30 with Computers categories.
Data Analytics in Finance covers the methods and application of data analytics in all major areas of finance, including buy-side investments, sell-side investment banking, corporate finance, consumer finance, financial services, real estate, insurance, and commercial banking. It explains statistical inference of big data, financial modeling, machine learning, database querying, data engineering, data visualization, and risk analysis. Emphasizing financial data analytics practices with a solution- oriented purpose, it is a “one-stop-shop” of all the major data analytics aspects for each major finance area. The book paints a comprehensive picture of the data analytics process including: Statistical inference of big data Financial modeling Machine learning and AI Database querying Data engineering Data visualization Risk analysis Each chapter is crafted to provide complete guidance for many subject areas including investments, fraud detection, and consumption finance. Avoiding data analytics methods widely available elsewhere, the book focuses on providing data analytics methods specifically applied to key areas of finance. Written as a roadmap for researchers, practitioners, and students to master data analytics instruments in finance, the book also provides a collection of indispensable resources for the readers’ reference. Offering the knowledge and tools necessary to thrive in a data-driven financial landscape, this book enables readers to deepen their understanding of investments, develop new approaches to risk management, and apply data analytics to finance.
5 Five Data Science Projects For Analysis Classification Prediction And Sentiment Analysis With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-04-29
5 Five Data Science Projects For Analysis Classification Prediction And Sentiment Analysis With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-29 with Computers categories.
PROJECT 1: SUPERMARKET SALES ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI The dataset used in this project consists of the growth of supermarkets with high market competitions in most populated cities. The dataset is one of the historical sales of supermarket company which has recorded in 3 different branches for 3 months data. Predictive data analytics methods are easy to apply with this dataset. Attribute information in the dataset are as follows: Invoice id: Computer generated sales slip invoice identification number; Branch: Branch of supercenter (3 branches are available identified by A, B and C); City: Location of supercenters; Customer type: Type of customers, recorded by Members for customers using member card and Normal for without member card; Gender: Gender type of customer; Product line: General item categorization groups - Electronic accessories, Fashion accessories, Food and beverages, Health and beauty, Home and lifestyle, Sports and travel; Unit price: Price of each product in $; Quantity: Number of products purchased by customer; Tax: 5% tax fee for customer buying; Total: Total price including tax; Date: Date of purchase (Record available from January 2019 to March 2019); Time: Purchase time (10am to 9pm); Payment: Payment used by customer for purchase (3 methods are available – Cash, Credit card and Ewallet); COGS: Cost of goods sold; Gross margin percentage: Gross margin percentage; Gross income: Gross income; and Rating: Customer stratification rating on their overall shopping experience (On a scale of 1 to 10). In this project, you will perform predicting rating using machine learning. 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: DETECTING CYBERBULLYING TWEETS USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI As social media usage becomes increasingly prevalent in every age group, a vast majority of citizens rely on this essential medium for day-to-day communication. Social media’s ubiquity means that cyberbullying can effectively impact anyone at any time or anywhere, and the relative anonymity of the internet makes such personal attacks more difficult to stop than traditional bullying. On April 15th, 2020, UNICEF issued a warning in response to the increased risk of cyberbullying during the COVID-19 pandemic due to widespread school closures, increased screen time, and decreased face-to-face social interaction. The statistics of cyberbullying are outright alarming: 36.5% of middle and high school students have felt cyberbullied and 87% have observed cyberbullying, with effects ranging from decreased academic performance to depression to suicidal thoughts. In light of all of this, this dataset contains more than 47000 tweets labelled according to the class of cyberbullying: Age; Ethnicity; Gender; Religion; Other type of cyberbullying; and Not cyberbullying. The data has been balanced in order to contain ~8000 of each class. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, LSTM, and CNN. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 3: HIGHER EDUCATION STUDENT ACADEMIC PERFORMANCE ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI The dataset used in this project was collected from the Faculty of Engineering and Faculty of Educational Sciences students in 2019. The purpose is to predict students' end-of-term performances using ML techniques. Attribute information in the dataset are as follows: Student ID; Student Age (1: 18-21, 2: 22-25, 3: above 26); Sex (1: female, 2: male); Graduated high-school type: (1: private, 2: state, 3: other); Scholarship type: (1: None, 2: 25%, 3: 50%, 4: 75%, 5: Full); Additional work: (1: Yes, 2: No); Regular artistic or sports activity: (1: Yes, 2: No); Do you have a partner: (1: Yes, 2: No); Total salary if available (1: USD 135-200, 2: USD 201-270, 3: USD 271-340, 4: USD 341-410, 5: above 410); Transportation to the university: (1: Bus, 2: Private car/taxi, 3: bicycle, 4: Other); Accommodation type in Cyprus: (1: rental, 2: dormitory, 3: with family, 4: Other); Mother's education: (1: primary school, 2: secondary school, 3: high school, 4: university, 5: MSc., 6: Ph.D.); Father's education: (1: primary school, 2: secondary school, 3: high school, 4: university, 5: MSc., 6: Ph.D.); Number of sisters/brothers (if available): (1: 1, 2:, 2, 3: 3, 4: 4, 5: 5 or above); Parental status: (1: married, 2: divorced, 3: died - one of them or both); Mother's occupation: (1: retired, 2: housewife, 3: government officer, 4: private sector employee, 5: self-employment, 6: other); Father's occupation: (1: retired, 2: government officer, 3: private sector employee, 4: self-employment, 5: other); Weekly study hours: (1: None, 2: <5 hours, 3: 6-10 hours, 4: 11-20 hours, 5: more than 20 hours); Reading frequency (non-scientific books/journals): (1: None, 2: Sometimes, 3: Often); Reading frequency (scientific books/journals): (1: None, 2: Sometimes, 3: Often); Attendance to the seminars/conferences related to the department: (1: Yes, 2: No); Impact of your projects/activities on your success: (1: positive, 2: negative, 3: neutral); Attendance to classes (1: always, 2: sometimes, 3: never); Preparation to midterm exams 1: (1: alone, 2: with friends, 3: not applicable); Preparation to midterm exams 2: (1: closest date to the exam, 2: regularly during the semester, 3: never); Taking notes in classes: (1: never, 2: sometimes, 3: always); Listening in classes: (1: never, 2: sometimes, 3: always); Discussion improves my interest and success in the course: (1: never, 2: sometimes, 3: always); Flip-classroom: (1: not useful, 2: useful, 3: not applicable); Cumulative grade point average in the last semester (/4.00): (1: <2.00, 2: 2.00-2.49, 3: 2.50-2.99, 4: 3.00-3.49, 5: above 3.49); Expected Cumulative grade point average in the graduation (/4.00): (1: <2.00, 2: 2.00-2.49, 3: 2.50-2.99, 4: 3.00-3.49, 5: above 3.49); Course ID; and OUTPUT: Grade (0: Fail, 1: DD, 2: DC, 3: CC, 4: CB, 5: BB, 6: BA, 7: AA). The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, and XGB classifier. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 4: COMPANY BANKRUPTCY ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI The dataset was collected from the Taiwan Economic Journal for the years 1999 to 2009. Company bankruptcy was defined based on the business regulations of the Taiwan Stock Exchange. Attribute information in the dataset are as follows: Y - Bankrupt?: Class label; X1 - ROA(C) before interest and depreciation before interest: Return On Total Assets(C); X2 - ROA(A) before interest and % after tax: Return On Total Assets(A); X3 - ROA(B) before interest and depreciation after tax: Return On Total Assets(B); X4 - Operating Gross Margin: Gross Profit/Net Sales; X5 - Realized Sales Gross Margin: Realized Gross Profit/Net Sales; X6 - Operating Profit Rate: Operating Income/Net Sales; X7 - Pre-tax net Interest Rate: Pre-Tax Income/Net Sales; X8 - After-tax net Interest Rate: Net Income/Net Sales; X9 - Non-industry income and expenditure/revenue: Net Non-operating Income Ratio; X10 - Continuous interest rate (after tax): Net Income-Exclude Disposal Gain or Loss/Net Sales; X11 - Operating Expense Rate: Operating Expenses/Net Sales; X12 - Research and development expense rate: (Research and Development Expenses)/Net Sales X13 - Cash flow rate: Cash Flow from Operating/Current Liabilities; X14 - Interest-bearing debt interest rate: Interest-bearing Debt/Equity; X15 - Tax rate (A): Effective Tax Rate; X16 - Net Value Per Share (B): Book Value Per Share(B); X17 - Net Value Per Share (A): Book Value Per Share(A); X18 - Net Value Per Share (C): Book Value Per Share(C); X19 - Persistent EPS in the Last Four Seasons: EPS-Net Income; X20 - Cash Flow Per Share; X21 - Revenue Per Share (Yuan ¥): Sales Per Share; X22 - Operating Profit Per Share (Yuan ¥): Operating Income Per Share; X23 - Per Share Net profit before tax (Yuan ¥): Pretax Income Per Share; X24 - Realized Sales Gross Profit Growth Rate; X25 - Operating Profit Growth Rate: Operating Income Growth; X26 - After-tax Net Profit Growth Rate: Net Income Growth; X27 - Regular Net Profit Growth Rate: Continuing Operating Income after Tax Growth; X28 - Continuous Net Profit Growth Rate: Net Income-Excluding Disposal Gain or Loss Growth; X29 - Total Asset Growth Rate: Total Asset Growth; X30 - Net Value Growth Rate: Total Equity Growth; X31 - Total Asset Return Growth Rate Ratio: Return on Total Asset Growth; X32 - Cash Reinvestment %: Cash Reinvestment Ratio X33 - Current Ratio; X34 - Quick Ratio: Acid Test; X35 - Interest Expense Ratio: Interest Expenses/Total Revenue; X36 - Total debt/Total net worth: Total Liability/Equity Ratio; X37 - Debt ratio %: Liability/Total Assets; X38 - Net worth/Assets: Equity/Total Assets; X39 - Long-term fund suitability ratio (A): (Long-term Liability+Equity)/Fixed Assets; X40 - Borrowing dependency: Cost of Interest-bearing Debt; X41 - Contingent liabilities/Net worth: Contingent Liability/Equity; X42 - Operating profit/Paid-in capital: Operating Income/Capital; X43 - Net profit before tax/Paid-in capital: Pretax Income/Capital; X44 - Inventory and accounts receivable/Net value: (Inventory+Accounts Receivables)/Equity; X45 - Total Asset Turnover; X46 - Accounts Receivable Turnover; X47 - Average Collection Days: Days Receivable Outstanding; X48 - Inventory Turnover Rate (times); X49 - Fixed Assets Turnover Frequency; X50 - Net Worth Turnover Rate (times): Equity Turnover; X51 - Revenue per person: Sales Per Employee; X52 - Operating profit per person: Operation Income Per Employee; X53 - Allocation rate per person: Fixed Assets Per Employee; X54 - Working Capital to Total Assets; X55 - Quick Assets/Total Assets; X56 - Current Assets/Total Assets; X57 - Cash/Total Assets; X58 - Quick Assets/Current Liability; X59 - Cash/Current Liability; X60 - Current Liability to Assets; X61 - Operating Funds to Liability; X62 - Inventory/Working Capital; X63 - Inventory/Current Liability X64 - Current Liabilities/Liability; X65 - Working Capital/Equity; X66 - Current Liabilities/Equity; X67 - Long-term Liability to Current Assets; X68 - Retained Earnings to Total Assets; X69 - Total income/Total expense; X70 - Total expense/Assets; X71 - Current Asset Turnover Rate: Current Assets to Sales; X72 - Quick Asset Turnover Rate: Quick Assets to Sales; X73 - Working capitcal Turnover Rate: Working Capital to Sales; X74 - Cash Turnover Rate: Cash to Sales; X75 - Cash Flow to Sales; X76 - Fixed Assets to Assets; X77 - Current Liability to Liability; X78 - Current Liability to Equity; X79 - Equity to Long-term Liability; X80 - Cash Flow to Total Assets; X81 - Cash Flow to Liability; X82 - CFO to Assets; X83 - Cash Flow to Equity; X84 - Current Liability to Current Assets; X85 - Liability-Assets Flag: 1 if Total Liability exceeds Total Assets, 0 otherwise; X86 - Net Income to Total Assets; X87 - Total assets to GNP price; X88 - No-credit Interval; X89 - Gross Profit to Sales; X90 - Net Income to Stockholder's Equity; X91 - Liability to Equity; X92 - Degree of Financial Leverage (DFL); X93 - Interest Coverage Ratio (Interest expense to EBIT); X94 - Net Income Flag: 1 if Net Income is Negative for the last two years, 0 otherwise; and X95 - Equity to Liabilitys. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, and XGB classifier. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 5: DATA SCIENCE FOR RAIN CLASSIFICATION AND PREDICTION WITH PYTHON GUI This dataset contains about 10 years of daily weather observations from many locations across Australia. RainTomorrow is the target variable to predict. You will determine rain or not in the next day. This column is Yes if the rain for that day was 1mm or more. Observations were drawn from numerous weather stations. The daily observations are available from http://www.bom.gov.au/climate/data. The dataset contains 23 attributes. Some of them are as follows: About some of them are: DATE - The date of observation; LOCATION - The common name of the location of the weather station; MINTEMP - The minimum temperature in degrees celsius; MAXTEMP - The maximum temperature in degrees celsius; RAINFALL - The amount of rainfall recorded for the day in mm; EVAPORATION - The so-called Class A pan evaporation (mm) in the 24 hours to 9am; SUNSHINE - The number of hours of bright sunshine in the day; WINDGUESTDIR - The direction of the strongest wind gust in the 24 hours to midnight; WINDGUESTSPEED- The speed (km/h) of the strongest wind gust in the 24 hours to midnight; and WINDDIR9AM - Direction of the wind at 9am. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, and XGB classifier. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy.
Data Science And Its Applications
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Author : Aakanksha Sharaff
language : en
Publisher: CRC Press
Release Date : 2021-08-17
Data Science And Its Applications written by Aakanksha Sharaff and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-17 with Computers categories.
The term "data" being mostly used, experimented, analyzed, and researched, "Data Science and its Applications" finds relevance in all domains of research studies including science, engineering, technology, management, mathematics, and many more in wide range of applications such as sentiment analysis, social medial analytics, signal processing, gene analysis, market analysis, healthcare, bioinformatics etc. The book on Data Science and its applications discusses about data science overview, scientific methods, data processing, extraction of meaningful information from data, and insight for developing the concept from different domains, highlighting mathematical and statistical models, operations research, computer programming, machine learning, data visualization, pattern recognition and others. The book also highlights data science implementation and evaluation of performance in several emerging applications such as information retrieval, cognitive science, healthcare, and computer vision. The data analysis covers the role of data science depicting different types of data such as text, image, biomedical signal etc. useful for a wide range of real time applications. The salient features of the book are: Overview, Challenges and Opportunities in Data Science and Real Time Applications Addressing Big Data Issues Useful Machine Learning Methods Disease Detection and Healthcare Applications utilizing Data Science Concepts and Deep Learning Applications in Stock Market, Education, Behavior Analysis, Image Captioning, Gene Analysis and Scene Text Analysis Data Optimization Due to multidisciplinary applications of data science concepts, the book is intended for wide range of readers that include Data Scientists, Big Data Analysists, Research Scholars engaged in Data Science and Machine Learning applications.