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The Lifecycle of Feature Engineering: From Raw Data to Model-Ready Inputs

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By Jayita Gulati on July 16, 2025 in Machine Learning Image by Editor In data science and machine learning, raw data is rarely suitable for direct consumption by algorithms. Feature engineering can impact model performance, sometimes even more than the choice of algorithm itself. Data audit : Identify variable types (e.g.,

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How to Work Smarter, Not Harder, with Artificial Intelligence

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Yet, navigating the world of AI can feel overwhelming, with its complex algorithms, vast datasets, and ever-evolving tools. Essential AI Skills Guide TL;DR Key Takeaways : Proficiency in programming languages like Python, R, and Java is essential for AI development, allowing efficient coding and implementation of algorithms.

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Hierarchical Clustering in Machine Learning: An In-Depth Guide

Pickl AI

This method uses distance metrics and linkage criteria to build dendrograms, revealing data structure. While computationally intensive, it excels in interpretability and diverse applications, with practical implementations available in Python for exploratory data analysis.

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Linear regression

Dataconomy

Understanding supervised learning In supervised learning, algorithms learn from training data that includes input-output pairs. Advantages of using linear regression Linear regression has several benefits, including: Its a straightforward method, facilitating exploratory data analysis.

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Winter Hackathon 2025 – Team DataDivas

Women in Big Data

The hackathon presented the perfect balance of challenge and engagement, allowing us to implement Python programming skills across the entire data science pipeline – from initial data cleaning and processing through exploratory data analysis to advanced machine learning model development and optimization.

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Data Scientist Job Description – What Companies Look For in 2025

Pickl AI

Key Responsibilities of a Data Scientist in India While the core responsibilities align with global standards, Indian data scientists often face unique challenges and opportunities shaped by the local market: Data Acquisition and Cleaning: Extracting data from diverse sources including legacy systems, cloud platforms, and third-party APIs.

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How To Learn Python For Data Science?

Pickl AI

Mathematical Foundations In addition to programming concepts, a solid grasp of basic mathematical principles is essential for success in Data Science. Mathematics is critical in Data Analysis and algorithm development, allowing you to derive meaningful insights from data.