Headless super light forms management tool for Developers
In this project, we have to create a predictive model which allows the company to maximize the profit of the next marketing campaign
Marketing Mixed Modelling using PyMC3-Marketing
📊This project applies unsupervised machine learning techniques to segment customers based on spending behavior and demographic patterns. By leveraging K-Means clustering, we uncover hidden customer groups that can be useful for targeted marketing strategies. It’s a classic EDA + ML combo ideal for aspiring data scientists
Predictive Analysis in R (Logistic Regression Analysis)
Marketing analysis using Python and Pandas to analyse sales trends, customer behaviour, and product performance for data-driven decision-making.
Analyze customer behavior using SQL and Python to extract insights on purchase patterns, sentiment analysis, and marketing effectiveness.
Course of business intelligence bootcamp by Dibimbing
2022 Q1 Marketing Report
Analysis of Ad AB Test results using Python. Conducted comprehensive data analysis, including sanity checks, to evaluate ad campaign performance and derive key insights
Online Learning Platform Data Analysis and Tableau Dashboard
Analysis using Power BI
This project performs Exploratory Data Analysis (EDA) on a customer segmentation dataset to uncover insights into customer demographics, spending behaviors, and transaction patterns. The goal is to guide targeted marketing strategies by identifying key customer segments.
Data Analysis for Business and Marketing use cases
"RFM Analysis" is a part of Marketing Analysis and is used to analyze customer value, thereby helping businesses to analyze each customer group they have, from there marketing campaigns or special care.
Week 6, Day 1: Tableau Lab, where you will learn to load and integrate datasets into Tableau, create insightful visualizations like barplots and treeplots, and assemble interactive dashboards. The project guides you through analyzing customer data, including gender distribution, employment status, and state breakdowns.
This project applies KMeans clustering to segment customers based on their credit card usage patterns. Using features like balance, purchase frequency, credit limit, and payments, the model identifies distinct customer groups.
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