Lookalike model using the Locality Sensitive Hashing algorithm to find similar users and increase the click rate compared to the default rate.
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Updated
Dec 9, 2023 - Python
Lookalike model using the Locality Sensitive Hashing algorithm to find similar users and increase the click rate compared to the default rate.
Customer analytics and segmentation project using K-Means clustering, EDA, and lookalike modeling. Assignment for Zeotap Data Scientist position. Analyzes 200 customers and 1,000 transactions with 5-cluster segmentation (DB Index: 1.05).
Includes EDA, Predictive models and some actionable insights of E-Commerce Transactions.
This repository contains a comprehensive data science project analyzing eCommerce transaction data, implementing customer segmentation, and developing a lookalike model. The project showcases EDA, clustering techniques, and recommendation systems using Python.
Performed exploratory data analysis (EDA), built predictive models, and derived actionable insights.
ecommerce-Transactions-Dataset using Python, Pandas, NumPy,Scikit-learn,Power BI, Matplotlib, Seaborn,Machine Learning algorithms like K-Means clustering,Classification models like Logistic Regression, Random Forest
This repository contains the solutions for the exploratory data analysis (EDA), building a lookalike model, and performing customer segmentation using clustering techniques.
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