Predictions-using-DecisionTree

The goal here was to train a supervised learning model using scikit-learns DecisionTreeClassifier and then use it to generate predictions. The dataset used here was the Teaching Assistant Evaluation Dataset from UCI's Machine Learning Repository. First of all, the dataset was analyzed by generating various summary statistics like count, mean, median, variance, standard deviation, etc. and plotting the histograms for all of the features. A cumulative boxplot of all the features was also generated. Next, the features were differentiated into data and target variables. A supervised learning model was trained using scikit-learn's DecisionTreeClassifier which was then used to generate some predictions of data. A visual output of the trained model was also generated. This output was used to verify the predicted classes.