In this project, we classified a huge set of audios (~ 8000) of 4 classes which include different vehicles, groups of men, etc. Used extensive feature engineering techniques and also feature selection techniques to select and derived features in order to train our model. I have used XG-Boost Classifier as the data has too much variance. Finally got an accuracy score of 85.6% & prepared an end-to-end project with UI in order to record audio from the user & predict whether it is from one class (4 classes of vehicles).

In this project, we are predicting the hydraulic system failure 15-Minutes or 30-Minutes before in order to save the machine from total failure. Hereby, we're monitoring the condition of Hydraulic Systems. Overall accuracy achieved was 93.57% along with roc_auc_score of 96.58%.
Data: Open source data(604 MB) /Custom data to be made by our team too.

Used : Python, MYSQL, AWS GLUE, AWS S3, Pyspark In this project we have a developed an recommendation engine based on both Association Rule Learning (ARL) & Collaborative Filtering in order to curate the posts according to the users speciliaties. Here we have created an end-to-end data pipeline which does the whole ETL process and on the top of it we have used our in house modelling technique in order get the results as we need. Here we have taken an average lift score between 0.3 - 0.4

In this project, we classified huge set of audios (~ 8000) of 4 classes which includes different vehicles, group of men, etc. Used extensive feature engineering techniques and also feature selection techniques to select and derived features in order to train our model. I have used XG-Boost Classifier as the data has too much variance. Finally got an accuracy score of 85.6% & prepared end-to-end project with UI in order to record an audio from the user & predict whether it is from one each class (4 class of vehicles).