Classification Algorithms analysis

Implementing Decision Tree and compared with other classification algorithms in sklearn library.

Structure

Main programming files are:

main.py preprocessing.py data_splitting.py classification.py 
decision_tree.py my_decision_tree.py naive_bayes.py svm.py

Input files are

amazon_cells_labelled.txt imdb_labelled.txt yelp_labelled.txt

Extra files:

• rawFtAccuracy.png and reducedFtAccuracy.png saved output bar charts
• HW 3 Report.pdf output:
• console ouput: each algorithm's time and accuracy perfomance
• bar charts to visualize the output data

Usage

1. Run the main.py file
2. give the input amazon_cells_labelled.txt imdb_labelled.txt yelp_labelled.txt when see the prompt

Result bar charts

Using raw feature matrix:

After Applying feature selection algorithm (result_chart/top K frequent words)

Compare the performance on time:

License and Authority

Leon Cai (https://github.com/LeonCai1)