Data Science and Machine Learning has been the latest talk right now and companies are looking for data scientists and machine learning engineers to handle their data and make significant contributions to them. Whenever data is given to data scientists, they must take the right steps to process them and ensure that the transformed data can be used to train various machine learning models optimally while ensuring maximum efficiency. It is often found that the data that is present in real-world is oftentimes incomplete and inaccurate along with containing a lot of outliers which some machine learning models cannot handle, leading to suboptimal training performance. It is also important to note that there might be duplicate rows or columns in the data which must be dealt with before giving it to machine learning models. Addressing these issues along with many others can be crucial, especially when one wants to improve model performance and generalizing ability of the model.
In this project we will be working with a data set, indicating whether or not a particular internet user clicked on an Advertisement. We will try to create a model that will predict whether or not they will click on an ad based off the features of that user. Welcome to this project on predict Ads Click in Apache Spark Machine Learning using Databricks platform community edition server which allows you to execute your spark code, free of cost on their server just by registering through email id. In this project, we explore Apache Spark and Machine Learning on the Databricks platform. I am a firm believer that the best way to learn is by doing.
Problem Statement A target marketing campaign for a bank was undertaken to identify a segment of customers who are likely to respond to an insurance product. Here, the target variable is whether or not the customers bought insurance product and it depends on factors like Product usage in three months, demographics, transaction patterns as like deposit amount, checking account, a branch of the bank, Residential information (like urban, rural) and so on.
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Either to identify the best model or to understand the nuances of the model with different changes to the data or the hyperparameters -- you would want to perform numerous machine learning experiments. The results could be interesting that enable the process of model selection. As part of my job, I usually have to perform several ML experiments which can be -- (say) to test the effectiveness of dimensionality reduction techniques, text preprocessing techniques (in case of an NLP model), or simple things like playing with the size of the test set. Either way, you might have to run a single code multiple times and record all the observations for comparison later. This is slightly different from the hyperparameter tuning process, and our aim is to identify the technique that best suits our problem.
The Data Science Blogathon by Analytics Vidhya began with a simple mission: To bring together a large community of data science enthusiasts to share their knowledge with the world. With 4000 articles under our belt on various topics such as Data Science, Machine Learning, Deep Learning, Data Lakes, and Data Engineering published by over 700 authors who are avid data science enthusiasts, students, professionals and researchers from across the globe. We bring to you the 20th edition of the Data Science Blogathon. This month's Data Science Blogathon brings you more rewards for you through our special referral programme. Yes, you read that right!
Unsupervised learning algorithms are "unsupervised" because you let them run without direct supervision. You feed the data into the algorithm, and the algorithm figures out the patterns. The following picture shows the differences between three of the most popular unsupervised learning algorithms: Principal Component Analysis, k-Means clustering and Hierarchical clustering. The three are closely related, because data clustering is a type of data reduction; PCA can be viewed as a continuous counterpart of K-Means (see Ding & He, 2004).
Hello Guys, This blog contains all you need to know about regularization. This blog is all about mathematical intuition behind regularization and its Implementation in python.This blog is intended specially for newbies who are finding regularization difficult to digest. For any machine learning enthusiast, understanding the mathematical intuition and background working is more important then just implementing the model. I am new to world of blogging so If anyone encounters any problem whether conceptual or language-related please comment below. Back in the days, when I came across regularization it became difficult for me to to get mathematical intuition behind it.
This article was written by Prashant Gupta. One of the major aspects of training your machine learning model is avoiding overfitting. The model will have a low accuracy if it is overfitting. This happens because your model is trying too hard to capture the noise in your training dataset. By noise we mean the data points that don't really represent the true properties of your data, but random chance.