VTU Notes | 18EC72 | BIG DATA ANALYTICS

VTU Module - 5 |

Module-5

Brief Summary of Key Data Analysis Topics:

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.

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.

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.

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.

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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