The E-commerce Transaction Database Analysis project aims to provide valuable business insights to an e-commerce firm by analyzing transaction data from 2019. The analysis includes various aspects such as revenue differences between new and old customers, predicting customer lifetime value, identifying sales bundles, and segmenting customers using both heuristic and machine learning techniques. Leveraging transaction, tax, and spend datasets, this project serves as a foundation for deriving actionable business strategies.
- TBF (to be filled)
Python: Programming language used for data analysis and modeling.
- pandas: Library for data manipulation and analysis.
- scikit-learn: Library for machine learning algorithms and evaluation metrics.(Kmeans, LogisticRegression, SVM)
- mlxtend: apriori algorithm
- matplotlib and seaborn: Libraries for data visualization.
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📖 Background Study Study on E-commerce Industry and Business Objectives
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👓 Data Exploration & EDA Analysis: Gain insights into the structure and content of the dataset.
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⌛ Feature Engineering: Preprocess and transform the data to create relevant features for modeling.
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📊 Model Training: Build and train classification models to predict product bundles based on transactional data.
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✔️ Model Evaluation: Assess the performance of the trained models using metrics such as accuracy and confusion matrix.
The dataset used for this analysis comprises transactional data from the public Kaggle Database: Marketing Insights for E-Commerce Company