Data Science - Analytics Siksha

Data Analysis and Hypothesis Testing Using a Banking Dataset

Introduction In this analysis, we will explore a banking dataset, preprocess the data, build a classification machine learning model, and perform hypothesis testing to validate insights. The goal is to classify customers based on their likelihood of subscribing to a term deposit and to use statistical tests to support our findings. Step 1:

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Understanding Hypothesis Testing in Data Science and Machine Learning

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Hypothesis testing is a crucial statistical method used to make informed decisions based on data. In data science and machine learning, it plays a significant role in validating assumptions, testing new models, and optimizing business strategies. By understanding hypothesis testing, we can distinguish between genuine patterns in data and random noise, ensuring

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Understanding the Confusion Matrix in Machine Learning with Real-Life Examples

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Machine learning models are widely used in various fields, from healthcare and finance to spam detection and fraud prevention. However, evaluating the performance of these models is crucial to ensure their effectiveness. One of the most commonly used tools for assessing classification models is the confusion matrix. It provides a comprehensive way to

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Understanding Sampling Distribution: A Financial and analytical Perspective

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Vishal Sikka works as a data analytics consultant at a prominent investment bank. He has over 10 years of experience working in the finance sector. Vishal Sikka has shared his rich experience in this blog. Read and learn how to analyze financial results to make your data work for you. Introduction Imagine you are

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Linear Regression in Machine Learning: A Comprehensive Guide

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Linear regression is one of the most fundamental and widely used algorithms in machine learning. Whether predicting housing prices, stock market trends, or customer spending, linear regression provides a powerful yet simple way to model the relationship between variables. In this article, we will dive into linear regression and provide engaging, real-world examples from domains

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A Beginner’s Guide to Natural Language Processing (NLP)

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Contents: INTRODUCTION What is Natural Language Processing? How Does NLP Work? Real-Life Applications of NLP Why Should you Learn NLP? Getting Started with NLP Challenges in NLP Conclusion Natural Language Processing (NLP) is a fascinating field of artificial intelligence (AI), linguistics, and computer science. It enables machines to understand, interpret, and respond to human language

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What is Annotation?

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In programming, annotation refers to adding metadata to code. Metadata is supplementary information about code that doesn’t affect its execution but provides context, clarity, or guidance to the developer or external tools. In Python, annotations are often used to: Specify types of variables, arguments, and return values. Provide additional context for functions, variables, or classes.

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New Horizon Generative AI : Revolutionizing Technology and Creativity

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Introduction Generative AI is a type of artificial intelligence that is used to create new texts, audio, images, videos and other types of multimedia content. Unlike traditional AI, which is used for data analysis and predictions, generative AI learns patterns from the training data and generates new data. Advanced models like GANs and Transformer-based architecture

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