How to detect and treat outliers

Read Time: 6 min In this article, we are going to understand in-depth and detail about outlier-Statistical and Programming approaches, how to detect and treat outliers so that they won’t screw up the model performance. So read this article till the end, you will get to know how important outliers are while data-preprocessing. Table of content Why study outlier? … Read more

Frequently Asked Interview Questions on Data Analysis

Read Time: 4 min This post mainly focuses on interview questions asked in the data science interview. The primary purpose of this post is to help you to understand and learn concepts through questions. So let’s prepare together! 1. Why normalization is needed? Is it always necessary to normalize the data? Answer: Normalization brings all features in the same … Read more

Let the confusion matrix solve your confusion

Read Time: 5 min Whenever machine learning is used for classification or prediction problems, it’s equally important to focus on it’s evaluation metrics which will let you know, how well your model is performing. In this article, we will discuss how to evaluate performance of classification algorithms using confusion matrix. Table of content What is classification What is confusion … Read more

Bias Variance trade off

Read Time: 3 min To solve the mystery of bias-variance trade off, we need to first understand what is meant by bias and variance in data science. Why it is so important for analysis of machine learning model. Does it create any issue while training or testing? If so, what are the techniques to handle those problems. let’s dive … Read more

Regularization Techniques

Read Time: 4 min As you know, literal meaning of regularization is to manage or control things. Machine learning model also demands regularization sometimes. Through this post, you will be able to know about what is regularization in machine learning, why does machine learning model need it , different regularization techniques like L1 and L2 regularization methods , dropout … Read more

Part 2: Machine learning model deployment on Microsoft Azure

Read Time: 3 min In previous post, we learned to design machine learning model on Azure notebook, how to create workspace on Azure and how to register trained model on cloud. “Part 2: Machine learning model deployment on Microsoft Azure ” is continuation of part 1, where we will learn how to deploy the model on Azure container instance. … Read more

Build ML model using Microsoft Azure: Part 1

Read Time: 4 min You might have worked on various data processing techniques, machine learning algorithms from understanding the business requirement, building the machine learning model to model’s validation process. Through this series, we are going to learn how to build ML model from scratch using Microsoft Azure Services i.e. on cloud for fraud detection. In part-1 of this … Read more

Techniques to handle class imbalance

Read Time: 6 min In following post I have explained class imbalance problem in classification models and techniques to handle class imbalance problem using python. Table of content What is the class imbalance? Why class imbalance is an issue? How to handle class imbalance (methods) Use case study with fraud detection data At first we need to understand what … Read more

How to choose error metrics for Classification and Regression

Read Time: 6 min In the first place, we need to understand, what is error metrics and why is it important to choose right one for any machine learning model. What is error metrics? After building machine learning model, we need to check it’s validity that how much accurate our prediction or classification is. Evaluation or error metrics plays … Read more

Data Analysis (Part-3): Feature Engineering

Read Time: 6 min So, Here we are on part-3 of Data Analysis: Feature Engineering At first we need to understand, what does it mean by feature engineering? Feature Engineering is a part of data analysis where using domain knowledge of data, features are transformed or generated or extracted to improve the model performance. Let’s dive in deeper! Feature … Read more

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