Fix my Django Application using PCA/Sklearn Model to predict new input

Dear sir. Your project attracted my attention at first glance, because I've extensive experience in PCA & Sklearn Programming. I'm really confident about your project, and very eager to join your project. If we have a chance to cooperate, I'll do my best to provide wonderful result. Looking forward to your response. Best Regards.

First:- Chatbot --> This can be integrated by any Facebook page, Slack etc. Its main aim is to reduce the human effort. For example, if this Chatbot is integrated with company page so it can easily answer you some basic questions about the company. Second:- Word Predictor --> This will be integrated with my Chatbot and it's an algorithm is such that it will learn according to previous input given and then It will suggest the next question and next word accordingly. Third:- Comment Checker --> This has a huge set of words inbuilt with its degree and level and it will analyze each comment and according to that it can give the report or if any inappropriate comment is taken it can forward to the authority. Speech Recognisation Train the database using deep learning, doing language model and lexical model for Arabic , adding functions to increase different types of noise at different levels as the picture shows. Then we notice the results.

understood Based on my understanding of your problem, here is how I would solve it (BTW, it sounds like you already know all this, not sure why you need someone else to do it): The parameters from the PCA need to be stored (these are just the eigenvectors of the covariance matrix, but since you're using sklearn, you can use its dump/load). Then upon new data entry (real-time prediction), the PCA model should be loaded, and transform is called on it before using the trained network. It's a matrix multiplication so it shouldn't have impact on your prediction performance.

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