Skip to content

Latest commit

 

History

History
32 lines (18 loc) · 2.22 KB

README.md

File metadata and controls

32 lines (18 loc) · 2.22 KB
Raw

AliHaider5577-Turkiye-Student-Evaluation-Analysis

The project successfully identified clusters of similar student evaluations, providing insights into student perceptions of courses and instructors. These insights can be used by educators and administrators to improve course content, teaching methods, and overall student satisfaction.

Data Source

Data is collected from Kaggle. Here is the link of dataset https://www.kaggle.com/datasets/onsrajhi/turkiye-student-evaluation-analysis-clustering

Key Steps

  • Exploratory Data Analysis (EDA): Performed EDA to understand the distribution of the data and identify any patterns or correlations among the variables.

  • Dimensionality Reduction: Applied Principal Component Analysis (PCA) to reduce the dimensionality of the data while retaining most of the variance.

  • Optimal Number of Clusters: Used the Elbow Method and Dendrograms to determine the optimal number of clusters for K-Means and Agglomerative Hierarchical Clustering.

  • Clustering Techniques: Implemented three clustering algorithms: K-Means, Agglomerative Hierarchical Clustering, and DBSCAN to segment the data into meaningful groups.

  • Cluster Analysis: Analyzed and compared the profiles of the clusters generated by different algorithms to interpret the results and derive insights.

Key Technologies

  • Python: Used for data analysis and clustering.

  • Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Scipy.

  • Visualization: Employed various visualization techniques to represent the data distribution, clustering results, and dendrogram.

  • Clustering Techniques: Implemented three clustering algorithms: K-Means, Agglomerative Hierarchical Clustering, and DBSCAN to segment the data into meaningful groups.

  • Cluster Analysis: Analyzed and compared the profiles of the clusters generated by different algorithms to interpret the results and derive insights.

Outcome

The project successfully identified clusters of similar student evaluations, providing insights into student perceptions of courses and instructors. These insights can be used by educators and administrators to improve course content, teaching methods, and overall student satisfaction.