Can sales of vanilla ice cream overtake chocolate?

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Table of contents:

Introduction

Monte Carlo simulation is a great forecasting tool for sales, asset returns, project ROI, and more.

In a previous article, I provide a practical introduction of how monte Carlo simulations can be used in a business setting to predict a range of possible business outcomes and their associated probabilities.

In this article, we will tackle the challenge of correlated variables in Monte Carlo simulations. We will look into 4 appropriate approaches for handling the correlation. …


Crossover/recombination oversampling adds novelty to a dataset and can score well on classification metrics vs. SMOTE and random oversampling

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Image by liyuanalison at Pixabay

TL;DR — There are many ways to oversample imbalanced data, other than random oversampling, SMOTE, and its variants. In a classification dataset generated using scikit-learn’s make_classification default settings, samples generated using crossover operations outperform SMOTE and random oversampling on the most relevant metrics.

Table of contents

Introduction

Many of us have been in the situation of working on a predictive model with an imbalanced dataset.

The most popular approaches to handling the imbalance include:


Assess probabilities of various business outcomes

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Photo by Mark de Jong on Unsplash

Monte Carlo simulation is a computational technique that can be used for a wide range of functions such as solving some of the more difficult mathematical problems as well as risk management.

We will go through 2 examples to demonstrate how Monte Carlo simulations can help you quantify risks in your next project or business decision.

Example 1: Sales Offer From a Wholesaler

Suppose you have an innovative product that you have been selling for the past year.


Model Interpretability

Tree-based ensembles and other popular algorithms often lead to counter-intuitive predictions when kept unchecked

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Photo by Jose Vega from Pexels

Table of Contents:

Introduction

Gradient boosted trees have been widely used to win several competitions on Kaggle. It is no surprise that for most tabular datasets you are working with, you would likely find XGBoost or another implementation of boosted decision trees as the model with the best cross-validation score on your metric(s).

Question — How many times have you deployed a gradient boosted trees model with a supposedly good cross-validation score, but your end users questioned the validity of predictions? …

About

Bassel Karami

Leading a data science team building retail analytics for shopping malls in the MENA region. MSc Econometrics | CFA, FRM, and CMA.

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