Coursera-Customer analytics

Predicting customer churn using scikit-learn

Analysing the content of an E-commerce database that contains list of purchases. Based on the analysis, I develop a model that allows to anticipate the purchases that will be made by a new customer, during the following year from its first purchase.

Customer life time analysis (CLV analysis). We are using Gamma-Gamma model to estimate average transaction value for each customer.

Customer targeting model to optimize promotion targeting, on simulated data from Starbucks. (work in progress)

Bootcamp Women in Data - Bogotá, COL

Exploration, visualization and implementation of machine learning models on customer, transnational other such data of Toyota, Universal Bank, Wayfair, etc (R, SAS and Alteryx)

Customer analytics project on PokemonGo

My Final Submission for the 'Santander Customer Transaction Prediction'. I have participated in this very tough and interesting competition on Kaggle a while ago and I finally got the time to put all the work together in this Repo.

Natural language processing for NPS comments. Supervised classification

Python script (and IPython notebook) to perform RFM analysis from customer purchase history data

Methods for doing customer analytics in R

Customer segmentation, price elasticity modelling and conversion modelling.

Analysis of an online retailer.

R-Analysis: Identifying high value customers and low value of customers using RFM modelling

Key: descriptive statistics and exploratory data analysis, forecasting (linear regressions, ARMIA, Prophet), and a Tableau dashboard that delivers customer insights such as RFM analysis.

Key: clustering, using logistic regression to build elasticity modeling for purchase probability, brand choice, and purchase quantity & deep neural network to build a black-box model to predict future customer behaviors.

The project concerns an international e-commerce company* based in the USA who want to discover key insights from their customer database. They want to use some of the most advanced machine learning techniques to study their customers.

Generated customer groups by giving each customer a quantitative score based on the Recency, Frequency & Monetary Value of their historical purchases using the K-Means Clustering algorithm.