lookalike-model

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

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.

Performed exploratory data analysis (EDA), built predictive models, and derived actionable insights.

This repository contains the solutions for the exploratory data analysis (EDA), building a lookalike model, and performing customer segmentation using clustering techniques.

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.