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PYQs - (Predictive Data Modeling using Python)

  1. Explain association rules with example.Explain association rules with example.Association Rules in Data Science Association rules are a key concept in data mining, used to find interesting relationships between variables in large datasets. These rules are often used in market basket analysis, where the goal is to find associations between different items that customers purchase. The basic idea is to identify sets of items that frequently co-occur in transactions. Key Concepts and Terminology 1. Itemset: A collection of one or more items. For example, {milk, bread, butt
  2. Explain Python Libraries for Data Processing, Modeling and Data Visualization.Explain Python Libraries for Data Processing, Modeling and Data Visualization.Certainly! Leveraging the insights from "Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data" (Wiley, 2015) and Chirag Shah's "A Hands-On Introduction To Data Science" (Cambridge University, 2020), I will provide a detailed explanation of essential Python libraries used for data processing, modeling, and visualization in data science. Python Libraries for Data Processing, Modeling, and Data Visualization Data Processing Libraries 1. NumPy * Overview:
  3. Explain predictive, Descriptive, and Prescriptive data analysis. And also mention their difference.Explain predictive, Descriptive, and Prescriptive data analysis. And also mention their difference.Certainly! Let's dive deep into the three primary types of data analysis: predictive, descriptive, and prescriptive. Each type serves a distinct purpose in the data analysis lifecycle and offers unique insights. Predictive Data Analysis Predictive data analysis is focused on forecasting future events based on historical data. It uses statistical models and machine learning techniques to predict outcomes by identifying patterns and relationships in the data. Key Techniques: Regression Analysi
  4. Write a short notes on Global Innovation Social Network and Analysis.Write a short notes on Global Innovation Social Network and Analysis.Global Innovation Social Network and Analysis Introduction Global Innovation Social Network and Analysis (GISNA) is an interdisciplinary field combining elements from social network analysis, innovation studies, and data science. It aims to understand how innovation propagates through social networks on a global scale. GISNA involves mapping, measuring, and analyzing the patterns of interactions and information flows among individuals, organizations, and institutions to foster and diffuse inno
  5. Explain the use of logistic function in logistic regression in detail. List and explain the Types of Logistic regression.Explain the use of logistic function in logistic regression in detail. List and explain the Types of Logistic regression.Logistic Function in Logistic Regression Introduction Logistic regression is a statistical method used for binary classification problems, where the outcome variable is categorical and typically dichotomous (e.g., yes/no, true/false, 0/1). The logistic function, also known as the sigmoid function, is central to logistic regression as it maps any real-valued number into the (0, 1) interval, making it suitable for probability estimation. Logistic Function The logistic function is defined as: \
  6. Write short notes on ASM.Write short notes on ASM.ASM (Association Rule Mining) Introduction Association Rule Mining (ARM) is a key concept in data mining that focuses on finding interesting relationships or associations among a large set of data items. It is primarily used to discover patterns, correlations, or structures within transaction databases, relational databases, and other forms of data repositories. Objective The primary objective of Association Rule Mining is to identify strong rules discovered in databases using some measures