This paper proposes a Convolutional Neural Network (CNN) based method for automatic movie genre classification from poster images. The proposed CNN architecture utilizes a multi-layered structure trained on a large dataset of movie posters. The paper details the CNN design, training process, and data pre-processing techniques employed. Data pre-processing includes one-hot encoding genre labels, handling missing values, addressing data imbalance, and image re-sizing/normalization. The performance of the proposed CNN is evaluated and compared against established models like LeNet, AlexNet, VGG variants, ResNet-50, Logistic Regression, and Random Forest.

Prediction, Random Forest, neural networks, lenet, alexnet, vgg-16,vgg-19, resnet, logictTensorFlowion, adam, sgd optimizers, tensorflow, keras

In order to run the above files, download the necessary libraries using the command, " pip install [Libraryname] ". These codes might take huge amount of time to train as we have perfoemed several experimental analysis and also it can vary based on the computing powers.