Overview / Usage

Many use pricey GPUs to shorten training and inference times for machine learning on industrial datasets that are getting bigger and bigger. Our project's demonstration will demonstrate how you may speed up your machine learning workflow utilising Intel's optimised libraries, which are both affordable and streamlined.

This reference notebook will show you how to use multiple accelerated Python libraries included in the Intel® AI Analytics Toolkit to improve your ML workflow's training cycles, prediction throughput, and accuracy (AI Kit). The principal libraries we'll use in this notebook are:

1.Intel® Distribution of Modin*

2.Intel® Extension for Scikit-learn*

3.Intel® Daal4py

4.XGBoost Optimized for Intel® Architecture

5.Intel® Extension for Pytorch

6.Intel®Optimization for Tensorflow

My code predicts the sentiments underlying tweets received in real time using tweepy and categorise them as positive, negative, or neutral, this script first performs EDA before preprocessing numerous datasets to train a bidirectional LSTM model.

Methodology / Approach

Live Twitter Sentimental Analysis

To use the 'tweepy' API, you need to create an account with Twitter Developer. After creating the account, go to 'Get Started' option and navigate to the 'Create an app' option. After you create the app, note down the below required credentials from there.

Fetching data from twitter:-

To get started, you’ll need to do the following things:

Set up a Twitter account if you don’t have one already. Using your Twitter account, you will need to apply for Developer Access and then create an application that will generate the API credentials that you will use to access Twitter from Python. Import the tweepy package.

Technologies Used

Streamlit, Tweepy API(Twitter developer),Google colab, python

Documents and Presentations

Repository

https://github.com/MohamedFarhun/IntelOneAPICodeMaven_Safe-Secure