Movie Success Prediction Using Data Mining
Objective
The main aim of Movie Success Prediction Using Data Mining PHP is to propose a system that helps to predict the success of movies. This will predict whether the movie has been flop or hit or super hit based on various algorithms of data mining.
Project Overview
Now days, movies occupies a great rolein a world. Even in life of common people, movies play a significant part as internet. Thus movies have large influence in entertainment area among the public. Thus the influence of movies could leads to some goodness and badness. Movies might always focus on social issues, current political cases, spreading of awareness related to specific things etc…. For an educated or mature audience, movies could reflect unknown scenarios of society.Even thoughmovies bring out as many good things to society it also provides some bad things too. So it is necessary to identify which movie could teaches the best thing and which one is not good to take up. With the help of reviews that were made by movie viewer it is easy to decide whether that movie has hit or flop or super hit. This identification process has been carried with the help of data mining based mechanisms. A system has been designed to assign the weights and it develops the mathematically related models to predict the success of movies. For this, ancient data sets which are relate to the parameters that help for movie success has been used. By applying various mechanisms which are available in data mining fields the particular movie is identified as good or bad. This system would particularly helpful for organizations whose works on review conducting. This helps to avoid the false rating as well as better analyses over those reviews.
Proposed System
The proposed Movie Success Prediction system aims to predict the success of particular movie based on data mining technologies that are highly employed. This system uses the data set of the post that was made by viewers of movies. Algorithm has been developed in order to predict the success rate of a specific movie. In first step, parameters has been identified that enhances the movie success and weights are assigned. Thus prediction of success paves a way to design the model mathematically in order to automate the process. Finally, the performance has been evaluated. In addition, it is found that some ratings and predictions may exactly right but some were not correct. The term close means that the bin value which has predicted is adjacent to bin having actual value. Due to this, some movies has been predicted incorrectly. For example those movies with user rating of about 7 has rate as 6 more often. Thus confusion matrices are structured with the help of rating in order to bringing out misclassification. Incorrect and correct classifications were reported for each and every test data. Additionally correct or incorrect distance has identified based on the values that are noted as true.It includes the bins for classification that contains user ratings and gross earnings orderly in to make prediction accurately. On the other hand, approach named Support Vector Machine has also been used to obtain these same results.
Software Requirements
- Windows 7 or higher
- WAMP Server
- Notepad++
- My SQL 5.6
Hardware Requirements
- Processor – Dual Core
- Hard Disk – 50 GB
- Memory – 1GB RAM
Technology Used
- Data Mining
Leave a Reply