To predict something useful from the datasets, we need to implement machine learning algorithms. Since, there are many types of algorithm like SVM, Bayes, Regression, etc. We will be using four algorithms- Dimensionality Reduction It is a very important algorithm as it is unsupervised i.e. it can implement raw data to structured data.
When you have a large dataset then there are the various cases when you are not getting the accurate machine learning models. Their predictions accuracy are not correct as you expected. There can be various reasons for it like Duplicates values e.t.c. One of the other reasons is Outliers. These are the values that don't contribute to the prediction but mainly affect the other descriptive statistic values like mean, median, e.t..c.
As a Data Scientist or Machine Learning Engineer, it is extremely important to be able to deploy our data science project as this would help to complete the data science life cycle. Traditional deployment of machine learning models with established framework such as Django or Flask may be a daunting and/or time-consuming task. This article is based on a video that I made on the same topic on the Data Professor YouTube channel (How to Build a Simple Machine Learning Web App in Python) in which you can watch it alongside reading this article. Today, we will be building a simple machine learning-powered web app for predicting the class label of Iris flowers as being setosa, versicolor and virginica. This will require the use of three Python libraries namely streamlit, pandas and scikit-learn. Let's take a look at the conceptual flow of the app that will include two major components: (1) the front-end and (2) back-end. In the front-end, the sidebar found on the left will accept input parameters pertaining to features (i.e.
How often do you think you're touched by data science in some form or another? Finding your way to this article likely involved a whole bunch of data science (whooaa). To simplify things a bit, I'll explain what data science means to me. "Data Science is the art of applying scientific methods of analysis to any kind of data so that we can unlock important information." If we unpack that, all data science really means is to answer questions by using math and science to go through data that's too much for our brains to process.