Customer Segmentation Project
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Updated
Feb 22, 2024 - Python
Customer Segmentation Project
Using a Kernel SVM model to determine which consumer demographics to target and their likelihood of purchasing a product based on viewing a social media ad. Achieved an accuracy score of 95%!
Using fuzzy c-means and k-means to analyze customer personality data
Using a KNN model to determine which consumer demographics to target and their likelihood of purchasing a product based on viewing a social media ad. Achieved an accuracy score of 95%!
This repository contains a dashboard created using Tableau and you will find details about customer analysis method
Dataset Northwind Mini Project Data Engineering
The KPMG Data Analytics Virtual Internship is designed to help you gain a practical insight into the work we do at KPMG and it is an opportunity for you to build your career skills and experience.
Detailed analysis of a company’s ideal customers, helps a business to better understand its customers and makes it easier for them to modify products according to the specific needs, behaviors and concerns of different types of customers
KYC: know your customer. In this repository you will know the phases of B2C customer analysis and customer segmentation using k-means and RFM analysis
Used sales data from CSV file to create tables and which were later combined to form an interactive and comprehensive dashboard in Tableau
This repository showcases the outcomes of an Exploratory Data Analysis (EDA), including visualisation, conducted on the comprehensive Amazon Review Data (2018) dataset, consisting of nearly 233.1 million records and occupying approximately 128 gigabytes (GB) of data storage, using MongoDB and PySpark.
This repo is about analytics for optimizing your business's top customers and products.
SegmentWise: Unveiling Customer Insights for Exploratory Data Analysis (EDA) and Customer Segmentation
Leveraging K-Means clustering for insightful customer segmentation, enabling businesses to tailor products to specific customer types.
This project showcases how to perform Recency, Frequency, and Monetary (RFM) analysis using the powerful Polars DataFrame library in Python.
Personal projects 1 & 2
This repository contains a data science project aimed at analyzing customer behavior and classifying them based on their likelihood to accept marketing campaigns. Additionally, the project involves clustering customers into different segments for targeted marketing strategies.
Customer Analytics in R
Bank Marketing Campaign Analysis to highlight the customers profile and targeted campaign success factors
The pilot project was a part of virtual internship by codebasics
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