Customer Personality Analysis And Prediction Using Machine Learning With Python
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Customer Personality Analysis And Prediction Using Machine Learning With Python
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-07-01
Customer Personality Analysis And Prediction 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 2023-07-01 with Computers categories.
In this book, we embark on an exciting journey through the world of machine learning, where we explore the intricacies of working with datasets, visualizing their distributions, performing regression analysis, and predicting clusters. This book serves as a comprehensive guide for both beginners and experienced practitioners who are eager to delve into the realm of machine learning and discover the power of predictive analytics. Chapter 1 and Chapter 2 sets the stage by introducing the importance of data exploration. We learn how to understand the structure of a dataset, identify its features, and gain insights into the underlying patterns. Through various visualization techniques, we uncover the distribution of variables, detect outliers, and discover the relationships between different attributes. These exploratory analyses lay the foundation for the subsequent chapters, where we dive deeper into the realms of regression and cluster prediction. Chapter 3 focuses on regression analysis on number of total purchases, where we aim to predict continuous numerical values. By applying popular regression algorithms such as linear regression, random forest, naïve bayes, KNN, decision trees, support vector, Ada boost, gradient boosting, extreme gradient boosting, and light gradient boosting, we unlock the potential to forecast future trends and make data-driven decisions. Through real-world examples and practical exercises, we demonstrate the step-by-step process of model training, evaluation, and interpretation. We also discuss techniques to handle missing data, feature selection, and model optimization to ensure robust and accurate predictions. Chapter 4 sets our exploration of clustering customers, we embarked on a captivating journey that allowed us to uncover hidden patterns and gain valuable insights from our datasets. We began by understanding the importance of data exploration and visualization, which provided us with a comprehensive understanding of the distribution and relationships within the data. Moving forward, we delved into the realm of clustering, where our objective was to group similar data points together and identify underlying structures. By applying K-means clustering algorithm, we were able to unveil intricate patterns and extract meaningful insights. These techniques enabled us to tackle various real-world challenges, including customer personality analysis. Building upon the foundation of regression and cluster prediction, Chapter 5 delves into advanced topics, using machine learning models to predict cluster. We explore the power of logistic regression, random forest, naïve bayes, KNN, decision trees, support vector, Ada boost, gradient boosting, extreme gradient boosting, and light gradient boosting models to predict the clusters. Throughout the book, we emphasize a hands-on approach, providing practical code examples and interactive exercises to reinforce the concepts covered. By utilizing popular programming languages and libraries such as Python and scikit-learn, we ensure that readers gain valuable coding skills while exploring the diverse landscape of machine learning. Whether you are a data enthusiast, a business professional seeking insights from your data, or a student eager to enter the world of machine learning, this book equips you with the necessary tools and knowledge to embark on your own data-driven adventures. By the end of this journey, you will possess the skills and confidence to tackle real-world challenges, make informed decisions, and unlock the hidden potential of your data. So, let us embark on this exhilarating voyage through the intricacies of machine learning. Together, we will unravel the mysteries of datasets, harness the power of predictive analytics, and unlock a world of endless possibilities. Get ready to dive deep into the realm of machine learning and unleash the potential of your data. Welcome to the exciting world of predictive analytics!
Regression Segmentation Clustering And Prediction Projects With Python
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-02-25
Regression Segmentation Clustering And Prediction Projects 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 2022-02-25 with Computers categories.
PROJECT 1: TIME-SERIES WEATHER: FORECASTING AND PREDICTION WITH PYTHON Weather data are described and quantified by the variables of Earth's atmosphere: temperature, air pressure, humidity, and the variations and interactions of these variables, and how they change over time. Different spatial scales are used to describe and predict weather on local, regional, and global levels. The dataset used in this project contains weather data for New Delhi, India. This data was taken out from wunderground. It contains various features such as temperature, pressure, humidity, rain, precipitation, etc. The main target is to develop a prediction model accurate enough for forecasting temperature and predicting target variable (condition). Time-series weather forecasting will be done using ARIMA models. The machine learning models used in this project to predict target variable (condition) are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, and MLP classifier. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 2: HOUSE PRICE: ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON The dataset used in this project is taken from the second chapter of Aurélien Géron's recent book 'Hands-On Machine learning with Scikit-Learn and TensorFlow'. It serves as an excellent introduction to implementing machine learning algorithms because it requires rudimentary data cleaning, has an easily understandable list of variables and sits at an optimal size between being to toyish and too cumbersome. The data contains information from the 1990 California census. Although it may not help you with predicting current housing prices like the Zillow Zestimate dataset, it does provide an accessible introductory dataset for teaching people about the basics of machine learning. The data pertains to the houses found in a given California district and some summary stats about them based on the 1990 census data. Be warned the data aren't cleaned so there are some preprocessing steps required! The columns are as follows: longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, median_house_value, and ocean_proximity. The machine learning models used in this project used to perform regression on median_house_value and to predict it as target variable are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, and MLP classifier. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 3: CUSTOMER PERSONALITY ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON Customer Personality Analysis is a detailed analysis of a company’s ideal customers. It helps a business to better understand its customers and makes it easier for them to modify products according to the specific needs, behaviors and concerns of different types of customers. Customer personality analysis helps a business to modify its product based on its target customers from different types of customer segments. For example, instead of spending money to market a new product to every customer in the company’s database, a company can analyze which customer segment is most likely to buy the product and then market the product only on that particular segment. Following are the features in the dataset: ID = Customer's unique identifier; Year_Birth = Customer's birth year; Education = Customer's education level; Marital_Status = Customer's marital status; Income = Customer's yearly household income; Kidhome = Number of children in customer's household; Teenhome = Number of teenagers in customer's household; Dt_Customer = Date of customer's enrollment with the company; Recency = Number of days since customer's last purchase; MntWines = Amount spent on wine in the last 2 years; MntFruits = Amount spent on fruits in the last 2 years; MntMeatProducts = Amount spent on meat in the last 2 years; MntFishProducts = Amount spent on fish in the last 2 years; MntSweetProducts = Amount spent on sweets in the last 2 years; MntGoldProds = Amount spent on gold in the last 2 years; NumDealsPurchases = Number of purchases made with a discount; NumWebPurchases = Number of purchases made through the company's web site; NumCatalogPurchases = Number of purchases made using a catalogue; NumStorePurchases = Number of purchases made directly in stores; NumWebVisitsMonth = Number of visits to company's web site in the last month; AcceptedCmp3 = 1 if customer accepted the offer in the 3rd campaign, 0 otherwise; AcceptedCmp4 = 1 if customer accepted the offer in the 4th campaign, 0 otherwise; AcceptedCmp5 = 1 if customer accepted the offer in the 5th campaign, 0 otherwise; AcceptedCmp1 = 1 if customer accepted the offer in the 1st campaign, 0 otherwise; AcceptedCmp2 = 1 if customer accepted the offer in the 2nd campaign, 0 otherwise; Response = 1 if customer accepted the offer in the last campaign, 0 otherwise; and Complain = 1 if customer complained in the last 2 years, 0 otherwise. The target in this project is to perform clustering and predicting to summarize customer segments. In this project, you will perform clustering using KMeans to get 4 clusters. The machine learning models used in this project to perform regression on total number of purchase and to predict clusters as target variable are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM, Gradient Boosting, XGB, and MLP. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 4: CUSTOMER SEGMENTATION, CLUSTERING, AND PREDICTION WITH PYTHON In this project, you will develop a customer segmentation, clustering, and prediction to define marketing strategy. The sample dataset summarizes the usage behavior of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables. Following is the Data Dictionary for Credit Card dataset: CUSTID: Identification of Credit Card holder (Categorical); BALANCE: Balance amount left in their account to make purchases; BALANCEFREQUENCY: How frequently the Balance is updated, score between 0 and 1 (1 = frequently updated, 0 = not frequently updated); PURCHASES: Amount of purchases made from account; ONEOFFPURCHASES: Maximum purchase amount done in one-go; INSTALLMENTSPURCHASES: Amount of purchase done in installment; CASHADVANCE: Cash in advance given by the user; PURCHASESFREQUENCY: How frequently the Purchases are being made, score between 0 and 1 (1 = frequently purchased, 0 = not frequently purchased); ONEOFFPURCHASESFREQUENCY: How frequently Purchases are happening in one-go (1 = frequently purchased, 0 = not frequently purchased); PURCHASESINSTALLMENTSFREQUENCY: How frequently purchases in installments are being done (1 = frequently done, 0 = not frequently done); CASHADVANCEFREQUENCY: How frequently the cash in advance being paid; CASHADVANCETRX: Number of Transactions made with "Cash in Advanced"; PURCHASESTRX: Number of purchase transactions made; CREDITLIMIT: Limit of Credit Card for user; PAYMENTS: Amount of Payment done by user; MINIMUM_PAYMENTS: Minimum amount of payments made by user; PRCFULLPAYMENT: Percent of full payment paid by user; and TENURE: Tenure of credit card service for user. In this project, you will perform clustering using KMeans to get 5 clusters. The machine learning models used in this project to perform regression on total number of purchase and to predict clusters as target variable are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM, Gradient Boosting, XGB, and MLP. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.
Customer Segmentation Clustering And Prediction With Python
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-07-04
Customer Segmentation 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 2023-07-04 with Computers categories.
In this book, we conducted a customer segmentation, clustering, and prediction analysis using Python. We began by exploring the customer dataset, examining its structure and contents. The dataset contained various features such as demographic, behavioral, and transactional attributes. To ensure accurate analysis and modeling, we performed data preprocessing steps. This involved handling missing values, removing duplicates, and addressing any data quality issues that could impact the results. We also split the dataset into features (X) and the target variable (y) for prediction tasks. Since the dataset had features with different scales and units, we applied feature scaling techniques. This process standardized or normalized the data, ensuring that all features contributed equally to the analysis. We then performed regression analysis on the "PURCHASESTRX" feature, which represents the number of purchase transactions made by customers. To begin the regression analysis, we first prepared the dataset by handling missing values, removing duplicates, and addressing any data quality issues. We then split the dataset into features (X) and the target variable (y), with "PURCHASESTRX" being the target variable for regression. We selected appropriate regression algorithms for modeling, such as Linear Regression, Random Forest, Naïve Bayes, KNN, Decision Trees, Support Vector, Ada Boost, Catboost, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron regressors. After training and evaluation, we analyzed the performance of the regression models. We examined the metrics to determine how accurately the models predicted the number of purchase transactions made by customers. A lower MAE and RMSE indicated better predictive performance, while a higher R2 score indicated a higher proportion of variance explained by the model. Based on the analysis, we provided insights and recommendations. These could include identifying factors that significantly influence the number of purchase transactions, understanding customer behavior patterns, or suggesting strategies to increase customer engagement and transaction frequency. Next, we focused on customer segmentation using unsupervised machine learning techniques. K-means clustering algorithm was employed to group customers into distinct segments. The optimal number of clusters was determined using KElbowVisualizer. To gain insights into the clusters, we visualized them 3D space. Dimensionality PCA reduction technique wasused to plot the clusters on scatter plots or 3D plots, enabling us to understand their separations and distributions. We then interpreted the segments by analyzing their characteristics. This involved identifying the unique features that differentiated one segment from another. We also pinpointed the key attributes or behaviors that contributed most to the formation of each segment. In addition to segmentation, we performed clusters prediction tasks using supervised machine learning techniques. Algorithms such as Logistic Regression, Random Forest, Naïve Bayes, KNN, Decision Trees, Support Vector, Ada Boost, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron Classifiers were chosen based on the specific problem. The models were trained on the training dataset and evaluated using the test dataset. To evaluate the performance of the prediction models, various metrics such as accuracy, precision, recall, F1-score, and ROC-AUC were utilized for classification tasks. Summarizing the findings and insights obtained from the analysis, we provided recommendations and actionable insights. These insights could be used for marketing strategies, product improvement, or customer retention initiatives.
Customer Personality Exploratory Data Analysis
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Author : Snehi Nainesh Pachchigar
language : en
Publisher:
Release Date : 2022
Customer Personality Exploratory Data Analysis written by Snehi Nainesh Pachchigar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Businesses these days rely largely on targeted marketing to have a better possibility of expanding as well as retaining their customer base. Tech giants like Google, Facebook have grown their business model surrounding targeted ads which help businesses to grow. Customer Personality Analysis is a detailed analysis of a company's ideal set of customers. Here, a dataset of customer characteristics is used to understand and analyze the customer habits and preferences. The dataset includes customer attributes such as birth year, education, marital status, having children or not, income, the amount spent on various products. The thesis represents how the data is cleaned and preprocessed along with reducing dimensionality. The meat of the thesis lies in clustering of data and discovering and predicting their choices. To enable the same the data went through iteration of data preprocessing to enable getting the best results. For clustering, the agglomerative algorithm and k-means algorithm are being used which will help bucket customers into segments per their choices. The analysis aided with visualization will help to target customers based on their interests. The model and techniques used here can further be replicated to adjust to the different datasets and hence gain valuable insights.
Marketing Analysis And Prediction Using Machine Learning And Deep Learning With Python
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-02-12
Marketing Analysis And Prediction Using Machine Learning And Deep 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 2022-02-12 with Computers categories.
This data set was provided to students for their final project in order to test their statistical analysis skills as part of a MSc. in Business Analytics. It can be utilized for EDA, Statistical Analysis, and Visualizations. Following are the features in the dataset: ID = Customer's unique identifier; Year_Birth = Customer's birth year; Education = Customer's education level; Marital_Status = Customer's marital status; Income = Customer's yearly household income; Kidhome = Number of children in customer's household; Teenhome = Number of teenagers in customer's household; Dt_Customer = Date of customer's enrollment with the company; Recency = Number of days since customer's last purchase; MntWines = Amount spent on wine in the last 2 years; MntFruits = Amount spent on fruits in the last 2 years; MntMeatProducts = Amount spent on meat in the last 2 years; MntFishProducts = Amount spent on fish in the last 2 years; MntSweetProducts = Amount spent on sweets in the last 2 years; MntGoldProds = Amount spent on gold in the last 2 years; NumDealsPurchases = Number of purchases made with a discount; NumWebPurchases = Number of purchases made through the company's web site; NumCatalogPurchases = Number of purchases made using a catalogue; NumStorePurchases = Number of purchases made directly in stores; NumWebVisitsMonth = Number of visits to company's web site in the last month; AcceptedCmp3 = 1 if customer accepted the offer in the 3rd campaign, 0 otherwise; AcceptedCmp4 = 1 if customer accepted the offer in the 4th campaign, 0 otherwise; AcceptedCmp5 = 1 if customer accepted the offer in the 5th campaign, 0 otherwise; AcceptedCmp1 = 1 if customer accepted the offer in the 1st campaign, 0 otherwise; AcceptedCmp2 = 1 if customer accepted the offer in the 2nd campaign, 0 otherwise; Response = 1 if customer accepted the offer in the last campaign, 0 otherwise; Complain = 1 if customer complained in the last 2 years, 0 otherwise; and Country = Customer's location. The machine and deep learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will plot boundary decision, ROC, 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 Driven Dealings Development
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Author : Jesko Rehberg
language : en
Publisher:
Release Date : 2020-12-11
Data Driven Dealings Development written by Jesko Rehberg and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-11 with categories.
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
Data Analysis With Machine Learning For Psychologists
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Author : Chandril Ghosh
language : en
Publisher: Springer Nature
Release Date : 2022-10-17
Data Analysis With Machine Learning For Psychologists written by Chandril Ghosh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-17 with Psychology categories.
The power of data drives the digital economy of the 21st century. It has been argued that data is as vital a resource as oil was during the industrial revolution. An upward trend in the number of research publications using machine learning in some of the top journals in combination with an increasing number of academic recruiters within psychology asking for Python knowledge from applicants indicates a growing demand for these skills in the market. While there are plenty of books covering data science, rarely, if ever, books in the market address the need of social science students with no computer science background. They are typically written by engineers or computer scientists for people of their discipline. As a result, often such books are filled with technical jargon and examples irrelevant to psychological studies or projects. In contrast, this book was written by a psychologist in a simple, easy-to-understand way that is brief and accessible. The aim for this book was to make the learning experience on this topic as smooth as possible for psychology students/researchers with no background in programming or data science. Completing this book will also open up an enormous amount of possibilities for quantitative researchers in psychological science, as it will enable them to explore newer types of research questions.
Python For Marketing Research And Analytics
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Author : Jason S. Schwarz
language : en
Publisher: Springer Nature
Release Date : 2020-11-03
Python For Marketing Research And Analytics written by Jason S. Schwarz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-03 with Computers categories.
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.
Toward Personalized Online Shopping
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Author : Daniel Ringbeck
language : en
Publisher:
Release Date : 2019
Toward Personalized Online Shopping written by Daniel Ringbeck and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Consumer's personality traits have a strong influence on their shopping behavior. Hence, e-tailers, rather than merely targeting broad consumer segments, should tailor their shop to those personality traits. However, there is no guidance on how e-tailers can assess a consumer's personality without relying on self-reported data. This study shows how consumers' personality traits can be predicted solely from their online browsing behavior. In a large-scale study, we demonstrate that a machine learning algorithm can predict the personality traits Need for cognition, Need for arousal, Lay rationalism and each of the Big 5 personality traits with accuracy comparable to well-known studies relying on social media data. We also establish that our algorithm is reliable in its predicted probabilities and is capable of making predictions of multiple personality traits in real time. Our research shows that e-tailers can quickly determine a consumer's personality traits and then dynamically adjust their online shop accordingly.
Applying Machine Learning To Predict Online Customers Behaviour
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Author : Arif Furqon Nugraha Adz Zikri
language : en
Publisher:
Release Date : 2023
Applying Machine Learning To Predict Online Customers Behaviour written by Arif Furqon Nugraha Adz Zikri and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
The way of learning continues to develop and gradually changes the way people buy such as online learning sites. This has implications for the strategies used for marketing in order to increase users on their platform. As these changes change, predicting consumer behavior and choices is becoming a topic of interest to researchers and companies alike. Predicting consumer behavior patterns has been conventionally proven, especially among sales, to increase business growth and generate customer loyalty. The study analyzed customer behavior patterns on paid online learning sites using Google Data Analytics, while in February 2022, a digital report by Hootsuite recorded at least 44% of Internet usage in Indonesia for E-learning needs. The use of Machine Learning is increasingly glimpsed to provide convenience in various aspects of work. We use preprocessing oversampling and feature selection steps to improve classifier performance and scalability. In the case of this paper, the authors used Decision Tree, Random Forest, XG Boosting, KNN, and SVM algorithms through a different set of conditions and then proposed an understandable and highly accurate machine learning model. This paper intends to analyze consumer behavior patterns and understand the metrics that determine consumers in making decisions using sensitive analysis. We also provide marketing strategies and further propose the use of machine learning algorithms in predicting customer behavior patterns. Further results can be extended to various departments to evaluate performance on the platform to increase customer loyalty.