Project 1: Quora-Question-Similarity: Project Overview

• It is a binary classification problem, for a given pair of questions we need to predict if they are duplicate or not.
• Identify which questions asked on Quora are duplicates of questions that have already been asked.
• This could be useful to instantly provide answers to questions that have already been answered.
• We are tasked with predicting whether a pair of questions are duplicates or not.
• Optimized Logistic Regressor,Linear SVM, and XGBoost to reach the best model.
• Performance metrics: log-loss

Project 2: Personalized-Cancer-Diagosis: Project Overview

• Personalized Medicine Redefining Cancer Treatment is a real world data set from Kaggle
• Memorial Sloan Kettering Cancer Center (MSKCC) launched this competition, accepted by the NIPS 2017 Competition Track, because we need your help to take personalized medicine to its full potential
• Classify the given genetic variations/mutations based on evidence from text-based clinical literature.
• Optimized Naive Bayes,Logistic Regression,Linear SVM and Random Forest Regressors to reach the best model.

Project 3: Netflix-Movie-Recommendation: Project Overview

• The given problem is a Recommendation problem
• Predict the rating that a user would give to a movie that he has not yet rated
• For a given movie and user we need to predict the rating would be given by him/her to the movie.
• Applied Surprise model,SVD(Singular value decomposition),SVDpp,xgboost regressor,item-item,user-user similarity,Matrix Factorization
• Performance metrics: Minimize the difference between predicted and actual rating (RMSE and MAPE)

Project 4: FaceRecoginition-Web-App: Project Overview

• To classify the gender by uploading a image and model will predict the gender whether Male or Female
• Object detection using Haar Casacade
• For the preprocess images, we will extract features from the images, ie. computing Eigen images using principal component analysis
• Developed web server gateway interphase in flask by rendering HTML CSS and bootstrap in the frontend and in the backend written in Python
• Optimized Linear SVM algorithm using GridsearchCV to reach the best model
• Integrating the machine learning model to Flask App.
• Built a client facing API using flask