Movie success prediction using Data mining

In this machine learning project, we need to build up a mathematical model for predicting the success class, for example, flop, hit, the super hit of the films. For doing this we need to build up a system in which the historical data of each component such as actor, actress, director, music that influences the success or failure of a movie is given. Then depend on different edges determined based on the basis of descriptive statistics of a dataset of each component will give flop, hit, super hit label. So, in this article, we are going to explore how to implement Movie success prediction using data mining. I hope you’re excited to see and learn the process of making the project. So, without any further delay, let get started!

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Project description:

Data mining: Data mining is likewise called Knowledge discovery, Knowledge extraction, data/pattern analysis, data gathering, and so on.

Data mining is used to find unseen, valid and useful patterns in huge data sets.

The strategy manages various phases of the undertaking which comprises data collection, data preprocessing, generating training and testing dataset, model generation, prediction, and outcomes. These all strategies keep us from getting any superfluous information which further keeps our results increasingly applicable and precise for the forecast.

Database: Any Database to store the data set.

PHP: The PHP Hypertext Preprocessor (PHP) is a programming language that permits web designers to make a dynamic substance that collaborates with databases. PHP is fundamentally utilized for creating web-based applications.

Project Implementation:

Steps to achieve a movie success prediction using data mining are:

1. Initially, we need to collect the dataset, here we are collecting the data from the IMDB website.
2. Before applying the data mining technique, we need to pre-process the data. Data cleaning is the way toward getting ready data for analysis by evacuating or altering information that is inaccurate, fragmented, duplicated, or improperly formatted.
3. After preprocessing, the next step is to Generate Training and Test Dataset, for this process, we can use models like naive Bayes classifier and so on.
4. The next step is data analysis, which selected all attributes and analysis based on various elements that help us to assemble the most precise result for further stages like IMDB rating, critic rating, and so forth.
5. Now we have the Model Generation stage, modeling is a simplified, scientifically formalized approach to rough reality and alternatively to make forecasts from this estimation.
6. The last step is the Prediction, Prediction in information mining is to recognize information focuses simply on the portrayal of another related information esteem. By using prediction, we can derive the relationship between a thing we know and a thing we want to predict.
7. This application discovers the survey of the new picture. Because of this framework, the user can easily decide to choose whether to book a ticket ahead of time or not.

Requirement

-Programming language -PHP, any database

-WAMP Server

- Notepad++ or Netbeans IDE

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Movie success prediction using Data mining