This is a util module to help with movie revenue prediction
Project description
Movie Revenue Prediction 🎬
Mission: Given the characteristics of a movie (director, actors, budget…), predict the revenue it will generateDataset: Imdb (link).
🚩Data
350 k+ movies Multiple countries and languages Data fetched from www.themoviedb.org.
Technology: Tensorflow, Steamlit, Python, NLP
🚩 Zagros PDE Team 🌄
Amir Shamsa Syed Aaquib Hussain Mehdi Paydayesh Abdurraouf Aljaber
🚩Learn more
Link to the detialed documnetation
Link to the final presentation
💖This has been a cool project 😆 in this bootcamp!
Requirements
The major libraries used in these projects are:
- numpy,
- pandas,
- seaborn,
- matplotlib,
- missingno,
- random,
- re
- nltk
- sklearn
- tensorflow
- xgboost
- lightgbm
rand_state=100 RANDOMSEED = 100 DISPLAY_WIDTH = 400 DISPLAYMAX_COLUMNS = 25 #endregion
#region settings random.seed(RANDOMSEED) pd.set_option('display.width', DISPLAY_WIDTH) pd.set_option('display.max_columns', DISPLAYMAX_COLUMNS) import warnings warnings.filterwarnings('ignore') warnings.filterwarnings(action='once')
#endregion
File structure
Part 0: importing libararies
Part 1: define functions (methods)
Part 2: define processing functions (methods)
Part 3: QCs
Part 4: defining the features and targets
Part 5: making the pipeline
Part 6: cross validation and bagging regressor
Part 7: model selection
Part 8: gridSearch and hyperparameters testing
Part 9: TPOT testing
Part 10: stacking
Part 11: model performance and learning curve
Part 12: movie revenue prediction
Part 13: model B - creating a model to find similar movies using KNN
Part 14: model C - Creating a model to predict the movie popularity
Part 15: model D - scraping new movie data for testing the model
Project details
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