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1
Introduction
2
Parameters For Comparison
3
Promotion Problem Statement
4
Preprocessing Data
5
Logistic Regression
6
Support Vector Machine
7
K Nearest Neighbours
8
Naive Bayes
9
Decision Tree
10
Random Forest
Description:
Explore the application of classification algorithms in this comprehensive video lecture. Dive into comparisons between popular classification techniques, examining factors that influence their performance. Learn to evaluate algorithm effectiveness through a real-world case study on employee promotion prediction. Begin with an introduction to classification concepts, then progress through data preprocessing, logistic regression, support vector machines, k-nearest neighbors, Naive Bayes, decision trees, and random forests. Gain practical insights into implementing these algorithms for solving real-time classification problems across various domains.

Application of Classification Algorithms

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