In this blog-post ,I will go through the process of creating a machine learning model for suv cars dataset. The dataset provides information regarding the age ,gender and Estimated Salary.
Outlier Detection with RNN Autoencoders. Utilising a reconstruction autoencoder model to detect anomalies in time series data.
Real Estate Sale Prices, Regression, and Classification: Data Science is the Future of Fortune Telling. Well algorithms and machine learning are a perfect example of modern fortune telling in practice.
Logistic Regression Math & Geometrical Intuition with Example. Logistic Regression is a Classifier which is used to solve the classification problems. As it’s technically dependent on the Linear Regression & Logit function is a method for a classification problem.
The most important part of any Machine Learning Model is to know how good or accurate your model is. Okay, so I am a budding Data Scientist and I start building models.
A special case of coinciding SVC and LR decision boundaries. Support Vector Classifiers (SVC) and Logistic Regression (LR) can align to the extent that they can be the exact same thing.
What is Confusion matrix? How are other metrics derived? Why are these metrics important? This is my second article for ‘Challenges faced as an absolute beginner in Machine Learning” series. If you want to learn about bias-variance trade-off then go through this article where I have tried to explain the concept in layman’s term. In such scenarios , Confusion matrix comes into play. Confusion matrix got its name from the fact that it makes it easier to identify if the classification model is getting confused or not.
Analyzing League of Legends - League of Legends (LoL) is a multiplayer online video game developed and published by Riot Games. I won’t bore you with the rules but…
We will discuss different feature engineering techniques to solve a text-based supervised classification problem. We are using a real-world dataset of BBC News and will solve a multi-class text classification problem.
Machine learning is a smart alternative to analyzing vast amounts of data. Based on the tasks performed and the nature of the output, you can classify machine learning models into three types: