VTU Notes | 18EC72 | BIG DATA ANALYTICS
VTU Module - 5 |
Module-5
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2018 Scheme | CSE Department
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18EC72 | BIG DATA ANALYTICS | Module-5 VTU Notes
Brief Summary of Key Data Analysis Topics:
1. Regression Analysis:
- What it is: A statistical technique to model the relationship between a dependent variable (what you want to predict) and one or more independent variables (what influences it).
- How it works: Fits a line or curve through the data points to capture the underlying trend and predicts values for the dependent variable based on the independent variables.
- Applications: Forecasting, understanding causal relationships, pricing analysis, risk assessment.
2. Finding Similar Items:
- What it is: Identifying items with similar characteristics or features to a target item.
- How it works: Uses distance metrics or machine learning algorithms to compare items based on features like product attributes, user preferences, or text content.
- Applications: Recommendation systems, personalized search, content clustering, anomaly detection.
3. Similarity of Sets and Collaborative Filtering:
- Similarity of Sets: Measuring the overlap between two sets of items or users.
- Collaborative Filtering: Recommending items to a user based on the preferences of similar users (user-based) or similar items (item-based).
- Applications: Recommendation systems, social network analysis, market segmentation.
4. Frequent Itemsets and Association Rule Mining:
- Frequent Itemsets: Identifying sets of items that frequently appear together in a dataset.
- Association Rule Mining: Discovering rules that relate the presence of one itemset to the presence of another, revealing hidden patterns and correlations.
- Applications: Market basket analysis, customer segmentation, cross-selling recommendations, fraud detection.
Remember:
- These are just snippets, each topic deserves deeper exploration.
- The choice of analysis technique depends on the specific data and question at hand.
- Combining these techniques can lead to powerful insights and advanced data-driven solutions.
I hope this summary helps! Feel free to ask if you have any further questions about specific topics.
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