This repository contains a data analysis and predictive modeling project that focuses on predicting shopper behavior and analyzing online shoppers' intention to make a purchase. The project utilizes the popular "Online Shoppers Intention" dataset from the UCI Machine Learning Repository.
Key Features:
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Exploratory data analysis: Gain insights into the dataset through summary statistics, visualization of numerical and categorical variables, and correlation analysis.
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Predictive modeling: Apply various machine learning algorithms, including Decision Trees, C5.0 Boosted Trees, Naive Bayes, Ensemble models, and K-Nearest Neighbors (KNN) Classification, to predict revenue generation and shopper behavior.
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Evaluation metrics: Assess the performance of the predictive models using accuracy, confusion matrices, and other relevant evaluation metrics.
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Extensive documentation: The project is implemented in R Markdown, providing clear explanations, code annotations, and visualizations to enhance understanding and reproducibility.
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Comprehensive workflow: From data preprocessing and exploratory analysis to model training and evaluation, the project follows a step-by-step approach, demonstrating a complete data science workflow.
Whether you're interested in analyzing online shopping trends, predicting revenue generation, or exploring machine learning techniques, this repository serves as a valuable resource. It provides a detailed and well-documented project that can be used as a reference for similar data analysis tasks or as a starting point for further research and experimentation.
Get started with shopper prediction and uncover valuable insights from online shopping data with this machine learning project!