This project leverages data mining techniques to predict student placement outcomes. The methodology involves segmenting the data into two primary categories and utilizing clustering algorithms for analysis and prediction. Below are the key components of the project:
-
Data Segmentation:
- Historic Data: Consists of students' CGPA scores.
- Current Data: Includes students' IQ test results.
-
Algorithm Design:
- Utilized K-Means Clustering for initial data grouping.
- Applied Fuzzy C-Means Clustering for refined and overlapping group predictions.
By integrating these data segments and clustering methods, the algorithm calculates the likelihood of student placement effectively.