EPL Fantasy GW6 Recap and GW7 Algo Picks. Our Moneyball approach to the Fantasy EPL (team_id: 2122122)
In this post, I intend to cover the complexities involved in prediction probabilities of a multiple label dataset and walk through a real life example to show how this can be accomplished.
How long this IPL match would last? ML on Ball-By-Ball data. MS Dhoni may drag the match to the last over or Maxwell may finish the match too soon but it leaves footprint/data trail after every such innings.
Our Moneyball approach to the Fantasy EPL (team_id: 2122122) If this is the first time you land on one of my Fantasy EPL Blogs, you might want to check out Part1, Part2, Part3, Part4 and Part5 first to get ...
Neural Network & Time-series price prediction using hourly data. We’ll build a Deep Neural Network here that does some forecasting for us and use it to predict future price. Let us load the hourly frequency data.
Each student’s journey through a higher education institution creates lots of data. Use it to build models that support institutional and student success.
The most common metric used in Kaggle competitions. In this post, we will see what makes the log loss the number one choice. Before we start on the examples, let’s briefly explain what the log loss is.
Evaluation Metrics for Classification Models. I describe each evaluation metric and provide a binary classification example to facilitate comprehension.
What Are Humans Good For? Automation and Ingenuity. Automated processes free up human time and creativity. Maybe they’ll also make us better people.
In this analysis i’ll build a model that will predict whether a tumor is malignant or benign, based on data from a study on breast cancer. Classification algorithms will be used in the modelling process.
What makes it faster and more efficient. GBDT is so accurate that its implementations have been dominating major machine learning competitions.
Don’t do these things unless you want a biased model in production, making inaccurate and, at times, costly predictions. Despite the abundance of top quality machine learning (ML) practitioners and technological advancements, there is no dearth of real-life ML failures.
I will share some popular machine learning algorithms to predict the housing prices and the live model that I have built. My objective is to find a model that can generate a high accuracy of the housing prices, based on the available dataset, such that given a new property and with the required information, we will know whether the property is over or under-valued.
All the Way from Information Theory to Log Loss in Machine Learning. Entropy, cross-entropy, log loss, and the intuition behind.
In this blog, I’m going to create a few ML models using Scikit-learn library and we’ll compare the accuracy for each of them.
Yellowbrick — Analyze Your Machine Learning Model with Visualizations. A Python library for machine learning visualizations. This post is more of a practical application of Yellowbrick. We will quickly build a basic classification model and then use Yellowbrick tools to evaluate our model.
Improving the Performance of a Machine Learning Model. In this post, we will work on: How to improve the accuracy (both on positive and negative class); How to lean the focus of the model more towards the positive class.
However, an unexpected global pandemic locks me down in NYC and delays my plan. After staying home, I have been planning the next trip to Airbnb Boston with data science techniques. In this post, I will provide you with data visualization and machine learning.
We leveraged Machine Learning and the United Kingdom’s road accidents database to clarify these questions and specifically provide impact on two major areas.
The expected impact of data analytics on elections. Presidential elections in the United States are among the most talked about in the world. It is unquestionable that analysts will play an important role in the upcoming election, as do the data watchers team, which played an effective role in Obama’s election as president in 2008.