This project aims to use clustering techniques to identify the different topics that research papers on heart disease are discussing, based on the title of each paper. The data is provided in a CSV file, and the goal is to use the titles of the research papers to form clusters that represent different topics.
- Python 3.x
- Pandas
- Numpy
The first step is to load the data from the CSV file into a Pandas dataframe and perform some initial data cleaning and preprocessing.
Next, the titles of the research papers need to be transformed into a numerical representation that can be used for clustering.
The transformed text data can then be used as input to a clustering algorithm, such as K-Means or Agglomerative Clustering. The algorithm will group the research papers into clusters based on the similarity of their titles.
The results of the clustering can be evaluated using metrics. These metrics will provide a measure of the quality of the clustering results and can be used to determine the optimal number of clusters.
This project demonstrates how clustering techniques can be used to identify the topics of research papers on heart disease based on their titles. The results of the clustering can be used to gain a better understanding of the research being done in the field and to identify areas of interest for further investigation.