Pinned Repositories
Breast-Cancer-Classification
Breast Cancer Classification
Deep-Neural-Networks
Image Analysis: Classification of hand poses in the American sign language
House-Prices-Advanced-Regression
House Prices: Advanced Regression Techniques
Sentiment-analysis-algorithm
Dependency parsing was used to extract relevant information from a review in order to predict the sentiment of a given aspect term. Different machine learning models such as Naïve Bayes, Logistic Regression, Support Vector Classifier and Neural Networks were used to make predictions. A maximum accuracy score of 0.74 on the test dataset was achieved using Bidirectional LSTM model.
Text-matching-System-for-Question-Matching
Three different methods namely TFIDF, word average embedding method and inverse document frequency method were used to build a text matching system. The systems were tested on the first 100 questions which were duplicate. A maximum accuracy score of 77% and 67% in top5 and top 2 matches was obtained using average word model.
Toxic-Comment-Classification
Text classification using Convolutional Neural Network and Glove Embeddings
Wine-Quality-Prediction
ankita1112's Repositories
ankita1112/House-Prices-Advanced-Regression
House Prices: Advanced Regression Techniques
ankita1112/Breast-Cancer-Classification
Breast Cancer Classification
ankita1112/Sentiment-analysis-algorithm
Dependency parsing was used to extract relevant information from a review in order to predict the sentiment of a given aspect term. Different machine learning models such as Naïve Bayes, Logistic Regression, Support Vector Classifier and Neural Networks were used to make predictions. A maximum accuracy score of 0.74 on the test dataset was achieved using Bidirectional LSTM model.
ankita1112/Toxic-Comment-Classification
Text classification using Convolutional Neural Network and Glove Embeddings
ankita1112/Deep-Neural-Networks
Image Analysis: Classification of hand poses in the American sign language
ankita1112/Text-matching-System-for-Question-Matching
Three different methods namely TFIDF, word average embedding method and inverse document frequency method were used to build a text matching system. The systems were tested on the first 100 questions which were duplicate. A maximum accuracy score of 77% and 67% in top5 and top 2 matches was obtained using average word model.
ankita1112/Wine-Quality-Prediction