The goal of this project is to predict the success of a movie before it is released, by analyzing various features including budget, genres, production companies, and more. The dataset used in the project includes information on over 7,000 movies, containing features such as budget, genre, homepage, keywords, original language, production companies, release date, revenue, and more.

The dataset consists of information on over 7,000 movies, ranging from different genres, languages, budgets, revenue, and other features, including:

• Budget: Cost of making the movie
• Genres: A list of the genres that the movie belongs to (i.e., Avatar is a movie that has several genres, some of which are action, adventure, science)
• Homepage: URL of the movie's official website
• Id: Unique identifier for each movie
• Keywords: A list of keywords associated with the movie
• Original Language: Original language in which the movie was made
• Original Title: Original title of the movie
• Overview: General plot description
• Production Companies: A list of production companies involved in making the movie
• Production Countries: A list of countries where the movie was produced
• Release Date: Date when the movie was first released
• Revenue: Profit generated by the movie
• Runtime: Movie duration in minutes
• Spoken Languages: A list of languages spoken in the movie
• Status: Current status of the movie (e.g., Released, Post Production, Rumored)
• Tagline: Tagline associated with the movie
• Title: Title of the movie
• Vote Average: Average movie rating from 0-10
• Vote Count: Number of voters for the average movie rating

The following steps were taken to preprocess the data for building a movie recommendation system:

1. Data Gathering: The data was obtained from two CSV files.
2. Data Splitting: The data was split into training and testing sets.
3. Handling Outliers: Outliers were identified and replaced with their respective upper and lower limits.
4. Handling Missing Values: Missing values were replaced with the mean value of their respective columns.
5. Encoding Language Column: The language column was encoded using an ordinal encoder.
6. Preprocessing Title Column: The title column was preprocessed using lemmatization and sentiment analysis.
7. Preprocessing Original Title Column: The original title column was preprocessed using lemmatization, stop words removal, and punctuation removal.
8. Preprocessing Tagline Column: The tagline column was preprocessed using sentiment analysis.
9. Handling Columns in JSON Form: Relevant information from JSON columns was extracted.
10. One Hot Encoding: Two columns were one hot encoded.
11. Word2vector: The word2vector technique was used to create feature vectors for columns containing text data.
12. Scaling: Numeric columns of X_train ('budget', 'viewer count', 'revenue', 'runtime', 'vote count', 'year', 'month', 'day') were standardized.
13. Feature Selection: Features were selected using Pearson's Correlation Coefficient, KBest, and a combination of Pearson's Correlation Coefficient and KBest.

Pre-Processing of Production Companies and Production Countries Columns: The production companies and production countries columns were preprocessed and one hot encoded. The preprocessing steps were performed to improve the quality of the data and to make it more suitable for machine learning.

• The dataset was analyzed to identify the most relevant features for predicting movie revenue. • The following features were selected: budget, viewer count, runtime, vote count, and vote average.

• Six different models were trained to predict movie revenue: Support Vector Regression (SVR), Ridge Regression, Principal Components Regression (PCR), Elastic Net Regression, Partial Least Squares Regression (PLS), and Polynomial Regression. • SVR had the highest accuracy with an R-squared score of 0.60.

• Hyperparameter tuning improved the performance of three machine learning models: SVM, logistic regression, and gradient boosting.
• For SVM, the best hyperparameters were found to be kernel='rbf', C=2.2, and gamma='scale'. This resulted in an accuracy of 0.762.
• For logistic regression, the best hyperparameters were found to be penalty='l2', C=0.2, and solver='newton-cg'. This resulted in an accuracy of 0.74299.
• For gradient boosting, the best hyperparameters were found to be n_estimators=44, max_depth=2, and learning_rate=0.3. This resulted in an accuracy of 0.7495881383855024.
• Overall, the hyperparameter tuning significantly improved the performance of all three models. The best parameters for each model are summarized in the table below.

1. Clone this repository to your local machine
2. Install Python 3.x
3. Install required packages using pip install -r requirements.txt

• Mina Lotfy
• Michel Magdy
• Mina Anis
• Mary saad
• Mina Kalifa
• Kirollos Aziz

• Pattern Phase_1 (Regression) Report
• Pattern Phase_2 (Classification) Report