Machine-Learning

Movie Prediction Algorithm and Dataset

Steps

• After preprocessing/cleaning the data there were around 2000 data points.
• The main task was to predict the IMDB rating of a movie.
• This was considered as a classification problem by taking 10 classes 1-10 i.e the rating.
• There were initially many features which was then reduced using the domain knowledge finally only 9 features was taken into consideration, the filtered and processed data is saved in the after csv.csv file.
• All the models are pickled in the models folder.

Results

• Error= 0

Model	Accuracy (%)
K Nearest Neighbours	37.52
Logitic Regression	40.9
SVC	36.35

• Error= +/- 1

Model	Accuracy (%)
K Nearest Neighbours	80.90
Logitic Regression	85.09
SVC	83.91