RFM ANALYSIS AND K-MEANS CLUSTERING: A CASE STUDY ANALYSIS, CLUSTERING, AND PREDICTION ON RETAIL STORE TRANSACTIONS WITH PYTHON GUI

Name: RFM ANALYSIS AND K-MEANS CLUSTERING: A CASE STUDY ANALYSIS, CLUSTERING, AND PREDICTION ON RETAIL STORE TRANSACTIONS WITH PYTHON GUI
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SKU: 01387903

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1403469RFM ANALYSIS AND K-MEANS CLUSTERING: A CASE STUDY ANALYSIS, CLUSTERING, AND PREDICTION ON RETAIL STORE TRANSACTIONS WITH PYTHON GUIhttps://www.gandhi.com.mx/rfm-analysis-and-k-means-clustering-a-case-study-analysis-clustering-and-prediction-on-retail-store-transactions-with-python-gui/phttps://gandhi.vtexassets.com/arquivos/ids/168385/040cb13e-376c-4c95-99a3-eb550c40f895.jpg?v=638333668209400000246246MXNBALIGE PUBLISHINGInStock/Ebooks/1387903RFM ANALYSIS AND K-MEANS CLUSTERING: A CASE STUDY ANALYSIS, CLUSTERING, AND PREDICTION ON RETAIL STORE TRANSACTIONS WITH PYTHON GUI246246https://www.gandhi.com.mx/rfm-analysis-and-k-means-clustering-a-case-study-analysis-clustering-and-prediction-on-retail-store-transactions-with-python-gui/phttps://gandhi.vtexassets.com/arquivos/ids/168385/040cb13e-376c-4c95-99a3-eb550c40f895.jpg?v=638333668209400000InStockMXN99999DIEbook20221230005558407_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1230005558407_pThe 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 SKUs sold to 22.625 customers during one year./ppDataset 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./ppThe 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./p(*_*)1230005558407_In this case study, we will explore RFM (Recency, Frequency, Monetary) analysis and K-means clustering techniques for retail store transaction data. RFM analysis is a powerful method for understanding customer behavior by segmenting them based on their transaction history. K-means clustering is a popular unsupervised machine learning algorithm used for grouping similar data points. We will leverage these techniques to gain insights, perform customer segmentation, and make predictions on retail store transactions.The case study involves a retail store dataset that contains transaction records, including customer IDs, transaction dates, purchase amounts, and other relevant information. This dataset serves as the foundation for our RFM analysis and clustering.RFM analysis involves evaluating three key aspects of customer behavior: recency, frequency, and monetary value. Recency refers to the time since a customers last transaction, frequency measures the number of transactions made by a customer, and monetary value represents the total amount spent by a customer. By analyzing these dimensions, we can segment customers into different groups based on their purchasing patterns. Before conducting RFM analysis, we need to preprocess and transform the raw transaction data. This includes cleaning the data, aggregating it at the customer level, and calculating the recency, frequency, and monetary metrics for each customer. These transformed RFM metrics will be used for segmentation and clustering.Using the RFM metrics, we can apply clustering algorithms such as K-means to group customers with similar behaviors together. K-means clustering aims to partition the data into a predefined number of clusters based on their feature similarities. By clustering customers, we can identify distinct groups with different purchasing behaviors and tailor marketing strategies accordingly.K-means is an iterative algorithm that assigns data points to clusters in a way that minimizes the within-cluster sum of squares. It starts by randomly initializing cluster centers and then iteratively updates them until convergence. The resulting clusters represent distinct customer segments based on their RFM metrics. To determine the optimal number of clusters for our K-means analysis, we can employ elbow method. This method help us identify the number of clusters that provide the best balance between intra-cluster similarity and inter-cluster dissimilarity. Once the K-means algorithm has assigned customers to clusters, we can analyze the characteristics of each cluster. This involves examining the RFM metrics and other relevant customer attributes within each cluster. By understanding the distinct behavior patterns of each cluster, we can tailor marketing strategies and make targeted business decisions.Visualizations play a crucial role in presenting the results of RFM analysis and K-means clustering. We can create various visual representations, such as scatter plots, bar charts, and heatmaps, to showcase the distribution of customers across clusters and the differences in RFM metrics between clusters. These visualizations provide intuitive insights into customer segmentation. 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.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 GUIs interactive nature enhances the usability of the project and promotes effective decision-making based on the analysis results....1230005558407_BALIGE PUBLISHINGlibro_electonico_a04a9260-97db-3be3-8c9a-137465fff300_1230005558407;1230005558407_1230005558407Rismon HasiholanInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/f2ca928b-958d-498f-bb70-f76c6615d8f6-epub-fa8884f6-f37d-4a00-8a0c-a0a013e11566.epub2022-05-07T00:00:00+00:00BALIGE PUBLISHING