CSCI 5502 Data Mining Final Project

• datasets: store all csv files generated from original TMDb datasets
• notebook: Jupyter notebooks (mainly used for analyzing correlations between attributes)
• src: source Python files of the core system
  • classification.py: implement classification approaches used in this project
  • clustering.py: implement clustering approaches used in this project
  • DBSCAN_tuning.py: a program can automatically tune parameters of DBSCAN algorithm
  • preprocessing.py: implement functions for preprocessing conveniently
  • movie_revenue_predictor.py: the main program
• src/utilities: program not relate to core functions
  • instagram_data.py: program to get Instagram hashtags

• Python 3
• Pandas
• scikit-learn
• Matplot
• TMDb datasets in the directory datasets

The main program is movie_revenue_predictor.py. You need to execute this program in the directory Movie_Revenue_Predictor. Then, the program can be executed by the instruction python3 src/movie_revenue_predictor.py. Before execution, you can modify the parameters mentioned below.

There are paramters can be changed to conduct different tests.

classification_method = 1 # 0: single classifier 1: boosting
plotting = False # plotting classification result or not
evaluation_method = 0 # 0: classification error 1: RMSE
test_times = 10 # how many rounds of tests

Users can tune parameters of classification and clustering methos in different classifiers. To get more detailed information, please take a look at scikit-learn.

Make sure that the TMDb datasets are in the directory datasets.