INFERENTIAL STATISTICS PROJECT

Hello, welcome to my blog. It’s been a while since my last post which is mostly due to some personal projects I have being doing, laziness :) and other factors. Anyway, I want to introduce another project I did for the Statistics with R specialization in this post. You can see it by following this link.

WATCHING THE OLYMPICS IN NIGERIA

Hello, welcome to my blog. This is the first of my articles from Jumia travel. Like I said in my previous post, I am in my kind of partnership with them so I will regularly post the articles they send to me. Hope you enjoy it.

PREDICTING CRITICS AND AUDIENCE SCORES FOR MOVIES

Hello, welcome to my blog. I know it’s been long since my last post – I apologize for that. I have been quite busy for the past few weeks with some projects and I have not had any time to write. One of the things that has kept me busy is some of the courses I have been taking on Coursera – particularly the Statistics with R specialization. In this post, I will present the project I did for one of the courses of this specialization.

AD OR NON-AD?

Hello, welcome to my blog. Apologies for the delay in writing this post, I have been a little preoccupied lately. Thankfully I am able to create time to write this post. In this post, I am going to address the problem of distinguishing images that are ads from non-ads. Concretely, given an image the goal is to determine if it’s an advertisement (“ad”) or not an advertisement (“non-ad”). I am going to use the R programming language for this demonstration. 

IMPLEMENTING ENSEMBLE METHODS WITH PYTHON

Hello, welcome to my blog. In my previous post I introduced the concept of ensemble classifiers. I also talked about their operation and two popular ensemble methods – Boosting & Random Forests.

In this post I want to demonstrate how to implement the two ensemble methods mentioned above using the GraphLab library in Python. I will use the same dataset – LendingClub dataset so we can compare the performance of the single tree model to the ensemble model.

ENSEMBLE CLASSIFIERS

Hello, welcome to my blog. Recently, I have been talking about two algorithms for classification namely logistic regression & decision trees. I also demonstrated how we can implement these algorithms using Python’s scikit-learn library.

Today, I want to talk about Ensemble classifiers. The fundamental idea behind ensemble classifiers is combining a set of classifiers to make one better classifier. Concretely, an ensemble classifier combines two or more classifiers (also called a weak learner or classifier) in order to make a stronger classifier (also called a strong learner or classifier). 

IMPLEMENTING DECISION TREES WITH PYTHON

Hello, welcome to my blog. In my previous post I introduced another classification algorithm called decision trees. In this post I want to demonstrate how to implement decision trees using the scikit-learn library in Python.