# Performance Predictions of Sci-Fi Films via Machine Learning
## 1 Introduction
The researchers suggested machine learning strategies to predict movie popularity [13]. They used and compared the following classifiers: Logistic Regression, Simple Logistic, Multilayer Perceptron, J48, Nave Bayes, and PART. Their data source is IMDB, and they categorized the movies as Terrible, Poor, Average, and Excellent based on their IMDB rating, which is the same technique used by [14]. In [14], they offered a machine learning strategy based on intrinsic features, employing C4.5, PART, and correlation coefficients to predict movie popularity categorization, whereas [15] proposed a data mining approach to \begin{table}
\begin{tabular}{c c} \hline \hline
**Movie** & **Genres** \\ \hline About Time & Comedy, Drama, Fantasy, Romance, Sci-Fi \\ Alien & Horror, Sci-Fi \\ A Clockwork Orange & Crime, Drama, Sci-Fi \\ Boss Level & Action, Mystery, Thriller, Sci-Fi \\ Coherence & Drama, Horror, Mystery, Thriller, Sci-Fi \\ Dune & Action, Adventure, Sci-Fi \\ Inception & Action, Adventure, Thriller, Sci-Fi \\ Iron Sky & Action, Adventure, Comedy, Sci-Fi \\ Mac and Me & Adventure, Family, Fantasy, Sci-Fi \\ Maggie & Drama, Horror, Sci-Fi \\ Megaforce & Action, Sci-Fi \\ Okja & Action, Adventure, Drama, Sci-Fi \\ Only & Drama, Romance, Sci-Fi \\ The Day & Action, Drama, Horror, Sci-Fi, Thriller \\ The Martian & Adventure, Drama, Sci-Fi \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Science fiction movies genres. assess and forecast movie ratings. Using data mining techniques, such as neural networks, regression, and decision trees, researchers devised a way to predict how much money a movie will make at the box office and estimate its profit [16]. Opus Data gathered the dataset used in this article. The dataset was then supplemented with IMDB, IDMB, Metacritic, and Mojo information. They created a mathematical model [17] to predict movie ratings and success for Hollywood and Bollywood films. The films were categorized as Flop, Neutral, or Hit. For classification, the KNN method was utilized. The information was gathered from IMDB and social media networks, such as Facebook, Instagram, and Twitter. In [18], the writers projected a film's profitability to aid early-stage production finance decisions. Using social network analysis and text mining techniques, the team presents a system that can automatically extract information on a movie's cast, narrative, and release date from several data sources. In comparison to other methods, their data confirmed the system's effectiveness. The feature selection strategy they employed considerably improved the forecast's accuracy. Through an analysis of the industry's most influential driving forces, they wanted to create a decision-making tool that may be utilized. Using a mathematical model, in [19], they could accurately anticipate the performance of upcoming films at the box office. Budget, cast, director, producer, set location, narrative writer, movie release date, competing movies released simultaneously, music, release location, and target audience are just a few factors considered when evaluating a film's success. They developed a model by evaluating how distinct traits interact with one another. Each important component was assigned a weight, and the prediction was developed based on them. In addition, they illustrated the approach's prescriptive potential by demonstrating how it may be used to select a set of income-maximizing actors. Instead of depending on the opinions of critics and others, the researchers in [20] provide a method for forecasting a film's performance at the box office before its release. This paper presents an approach for estimating an IMDb rating using the IMDb Movie Dataset. Several algorithms were included in the study's analysis, but Random forest generated the most accurate predictions.