Consumer Sentiments Prediction System by OneAPI 2023
- 0 Collaborators
This project targets to create Consumer Sentiments Prediction System Usnig ONEAPI patched intelx-scikitlearn library which is build using XBGOOST pipelining and ansemble methods of knn and random forest algorithms to perform outstanding accuracy for predictions. ...learn more
Project status: Published/In Market
Intel Technologies
oneAPI
Overview / Usage
Building a predictive model that can analyze social media activity to determine consumer sentiment towards a particular content. The model should take into account various features of the social media data, such as the latest news, resources, topic of the media resources, popularity on the social media.
Methodology / Approach
Using Machine Learning Ensemble Methods we have accomplished the 90% accuracy with XGBoost Pipelining.
Step 1 :
Data Analysis And Preprocessed Data.
Step 2 :
Created pipeline for every type of output as per title and headline.
Splitted pipeline as per KNeighborsRegressor & RandomForestRegressor.
Step 3 :
Processed the every model fit it in pipeline.
Step 4 :
Got the time takes to the complete computations for fitting the model.
Step 5 :
Predicted the data And Acquired the prediction accuracy.
Technologies Used
OneAPI , Daal4Py, Intelx-scikit-learn, scikitlearn,