• I am currently working on Machine Learning,Deep Learning Use-Cases

• I am addicted to learning and growing every day

• I am currently learning Computer Vision,Image Classification,Object Detection etc.

• Build an end-to-end data science pipelines from data-validation to model deployment in an fully automated approach that has helped in reducing 40% tasks of the project.

Project:-:ballot_box_with_check:

☑️Wafer Fault Detection-The inputs of various sensors for different wafers have been provided. In electronics, a wafer (also called a slice or substrate) is a thin slice of semiconductor used for the fabrication of integrated circuits. The goal is to build a machine learning model which predicts whether a wafer needs to be replaced or not(i.e., whether it is working or not) based on the inputs from various sensors. There are two classes: +1 and -1.

• +1 means that the wafer is faulty and it needs to be replaced.

• -1 means that the the wafer is in a working condition and it doesn’t need to be replaced.

Project Link:-https://waferfaultdetect.herokuapp.com/

Project Demo:-

☑️Cement Strength Prediction:-To build a regression model to predict the concrete compressive strength based on the different features in the training data.

Project Link:-https://cementstrengthpred.herokuapp.com/

Project Demo:-

Important Repository:-

☑️WaferFaultDetectionProject

☑️CementStrengthPred

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