Intent Detection - BERT

The objective of this project is to detect intent from texts. For this, a benchmark dataset is used, which includes 7 intents (Search Creative Work, get weather, Book Restaurant, Play Music, Add to Playlist, Rate Book, Search Screening Event) and 14 thousand samples. Transfer learning has been leveraged to train a machine learning model. The model takes the raw texts, which are tokenized and vectorized to feed into the pre-trained model.

March 21, 2017

Spam Detection - Spark, MLib

The objective of this project is to detect spam messages. The dataset includes tagged SMS messages. It contains 5,574 English SMS messages in total; tagged as ham or spam. Check the SMSSpamCollection dataset for details. MLlib with PySpark is used to build the model. Preprocessing steps are performed and feature engineering is applied using TF-IDF. Logistic regression, Random Forest and Naive Bayes are used for classifications. The best performance is achieved for the Logistic regression with 97% accuracy.

March 20, 2017

House Price Prediction - Regression

The objecive of this project is to predict house price from different features. The dataset includes 1460 instances and 80 features. The following algorithms are applied as on selected features from the data: Applied Algorithms: Linear Regression Decision Tree SVM Random Forest AdaBoost GradientBoost XGBoost Feature selection is performed using Parson Correlation. Feature imputation, encoding, and scaling is performed. The best performance achieved is R-square = 0.