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- Effortless Data Exploration with Pandas Profiling
- Klib: Introduction, Features, and Advantages
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- An Introduction to Artificial Intelligence: A Beginner's Tutorial
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- Hospital analytics
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- Advantages of Marketing Analytics Certification
- Analytics in Healthcare and the Life Sciences
- What Is a Marketing Analyst? And How to Become One?
- How To Pursue A Career As A Financial Analyst?
- What is Marketing Analytics & Why It Matters?
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- Forest Analytics
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- Machine Learning Algorithms : Importance of Machine Learning Tools
- Machine Learning Engineer Roadmap
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- Everything You Need To know About First Machine Learning Model - Linear Regression In ML
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- Overfitting and Underfitting
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- Transform the Digital World with Machine Learning
Internet Of Things
- Why is IoT Dangerous?
- What is the Vulnerability of IoT?
- What is the Future of IoT in India?
- What after IoT?
- What are the Examples of IoT Devices?
- What are the Disadvantages and Limitations of IOT
- What is an IoT Attack?
- How Secure are IoT Devices?
- How Do I Protect my IoT Devices?
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Big Data & Analytics
- What is Anomaly Detection? Types, Models and Examples
- The Journey to Becoming a Data Analyst: A Step-by-Step Guide
- Data Analytics in the Digital Era: The Future of Work and Career Opportunities
- The Ethical Dilemma: Exploring the Implications of Data Analytics
- Data Analytics Case Studies: Real-World Examples of Business Insights and Success
- Unveiling Hidden Opportunities: Leveraging Data Analytics for Business Growth
- Unleashing the Power: Exploring the Best Data Analytics Tools for Unraveling Insights
- Future of Data Analytics : Unveiling Tomorrow
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Robotic Process Automation
Agile and Scrum Methodology
Industrial Revolution IR4.0
Interview Questions on Data Science
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- Lasso & Ridge Regression Interview Questions & Answers in 2023
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- Multiple Linear Regression Interview Questions & Answers
- Hierarchical Clustering Interview Questions & Answers
- CRISP-DM Interview Questions & Answers
- Moments of Business Decision
- Business Understanding
- Get to Know Everything About MLOps: What It Is, Why It Matters, and How to Implement It.
- How to become an MLOps Engineer?
- What is MLOps?
- What Differs Between MLOps Engineers & DevOps?
- Get To Know The Difference Between MLOps vs Data Engineering Here
- KNN Classifier
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- ML Ops
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Meet the Author : Mr. Bharani Kumar
Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.
The same concept underlies Relationship Mining, Market Basket Analysis, and Affinity Analysis: how are two entities connected to one another and is there any reliance between them.
Probabilistic 'if-then' statements are what association rules are. The following procedures are used to create the statements that exhibit genuine dependency the best.
if the statement's Antecedent is a portion of it.
The next sentence is referred to be Consequent.
Percentage / Number of transactions in which IF / Antecedent & THEN / Consequent appear in the data
Drawbacks of Support:
Percentage of If/Antecedent transactions that also have the Then/Consequent item set.
P (Consequent | Antecedent) = P(C & A) / P(A)
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Drawbacks of Confidence:
- Carries the same drawback as of Support
- It does not capture the true dependency - How good is the dependency between entities which have high Support?
Lift Ratio is a measure describing the ratio between dependency and independency between entities.
Formula: Confidence / Benchmark Confidence
Note: Benchmark Confidence assumes independence between
Antecedent & Consequent:
P(C|A) = P(C & A) / P(A) = P(C) X P(A) /P(A) = P(C)
Threshold - 1:
A rule that is helpful in locating subsequent item sets is one where lift > 1. The aforementioned rule is far superior to choosing random transactions.
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