INT234: Predictive Analytics Notes & Study Material
Predictive Analytics covers data preprocessing, exploratory data analysis, regression, classification, clustering, association rule mining, dimensionality reduction, neural networks, and ensemble methods. Students apply predictive models, evaluate their performance, and use methods to improve predictive accuracy.
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Available Study Materials
Handwritten Notes & Slides
Unit-by-unit lecture summaries and handwritten notes from class toppers.
Previous Year Papers (PYQs)
LPU Mid-Term (MTE) and End-Term (ETE) questions with solutions.
Official Syllabus & CA Pattern
Course structure, credits, weightage, and continuous evaluation criteria.
Course Overview & Evaluation Scheme
INT234 Unit-Wise Detailed Syllabus
Unit 1: SUPERVISED LEARNING: REGRESSION
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Unit 2: SUPERVISED LEARNING: CLASSIFICATION
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Unit 3: UNSUPERVISED LEARNING: CLUSTERING AND PATTERN DETECTION
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Unit 4: Dimensionality Reduction and Neural Networks
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Unit 5: MODEL PERFORMANCE
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Unit 6: Continuous Assessment
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What is the pass percentage & grading for INT234?
Passing criteria follow LPU academic regulations with combined evaluation across Continuous Assessments, Attendance, and End-Term Examination.