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
.