# Performance Predictions of Sci-Fi Films via Machine Learning
## 1 Introduction
It was discovered that the number of individuals who voted for the picture, the number of critics who reviewed it, the number of Facebook likes it earned, the film's running length, and its overall box office profits all had a substantial impact on the film's IMDb rating. Dramas and biopics are often the highest-quality examples of their respective film genres. In [21], researchers employed regression techniques to develop a model that considers multiple factors, assigns a weight to each element, and predicts the success or failure of forthcoming films based on the factor's value. In [22], models were created to forecast the performance and ratings of a new film before its premiere. A revenue threshold was established based on heuristics to classify the film as successful or unsuccessful. The comments on teasers and trailers were collected from YouTube since they are quite helpful when rating a film. Natural Language Processing (NLP) was used to extract keywords from user evaluations, and those reviews were evaluated as positive or negative based on emotional analysis. A detailed comparison of the various machine learning models used to estimate the success rate of a movie was made in [23]. These models' accuracy and statistical significance were examined to determine which is the best predictor. There are also some observations about aspects that impact the success of movies. The analyzed models include regression models, machine learning models, a time series model, and a neural network, with the neural network exhibiting the highest accuracy at about 86%. In addition, as part of the testing, statistics regarding 2020's film releases were evaluated. The authors of [24] made sure that the success of casting a movie depends on many things, such as the type of movie, when it comes out, who is in it, and how much money it makes in total. Understanding the risks associated with a film's release, which might affect its success or failure, can be an important step in expanding the film industry. As a result, they offered an ensemble learning technique to assess such comprehension, in which predictions from previously guided attribute computations may be utilized to improve future success/failure accuracy. Diverse methods are employed in the literature to examine and compare the generated data. The dataset is used with the machine learning algorithms SVM, KNN, Naive Bayes,Boosting Ensemble Technique, Stacking Ensemble Technique, Voting Ensemble Technique, and MLP Neural Network to guess how well a movie will do at the box office. In [24], they used many algorithms and their trends to predict the outcome of a movie and showed that the suggested method is better than the current research when these algorithms and trends are used. In [25], their study uses ML to forecast a movie's performance before its release, reducing risk. The Twitter sentimental analysis calculates the movie's polarity and subjectivity from user evaluations. Machine learning systems anticipate IMDb ratings using those data. Decision tree regression, SVR, and random forest regression create a predictive model. The study compared three methods to anticipate movie success [25] accurately. They created two models in [26] to achieve their prediction goals. The first model, the rate prediction model (RPM), predicts user satisfaction with the product using an ensemble regression model. The second model is a temporal-product popularity model (T-PPM) that predicts the product's temporal popularity using a random forest classifier. They gathered a new dataset, TweetAMovie, to test the IMDb and Twitter model proposals [26]. In [27], multiple classification algorithms based on machine learning are used in our movie dataset for multiclassification. The primary purpose of this research is to compare the efficacy of various machine learning techniques. This study compared Multinomial Logistic Regression (MLR), Support Vector Machine (SVM), Bagging (BAG), Naive Bayes (NBS), and K-Nearest Neighbor (KNN).