Classification-and-Clustering-Study

This is my final year Project. Here, we are going to use various data sets for Classification and Clustering and draw conclusions as to which model works best with which kind of data set. The File ClassificationStudy.ipynb is a Jupyter Notebook created to study Maple Leaves Ltd. Dataset. The Dataset has around more than 90 different species of plants along their textures, shapes and margins and classification has to be done accordingly. We use Four Different Classifiers- Random Forest, Linear Support Vector Machine, Decision Trees and Naive Bayes and compare the results using Confusion Matrix and Accuracy Score. After training all the models, it is observed that Random Forest gives the maximum accuracy. So, we then prepare a chart that will show the accuracy and time taken to train a Decision Tree Model by varying the number of Decision Trees used in the Random Forest Model.