Toxic Comments Classification - Build a multi-headed model that predicts probabilities of different types of toxicity such as threats, obscenity, insults, and identity-based in the comments.

1. Metrics: AUC ROC

2. Models used: Logistic Regression and RNN (LSTM/GRU)

3. Approach:

   1. Used Matplotlib and Seaborn to visualize distribution of target classes, correlation of categorical variables using confusion matrix and frequently used words using WordCloud.

   2. Created more features and visualized those features to understand whether longer comments are more toxic, etc

   3. Vectorized the words using TF-IDF and used LogisticRegression with additional features created from step 2 and without those features.

   4. Corrected spelling mistakes using FastText embeddings and trained a RNN network with LSTM and GRU cells using Keras.

StackOverFlow Tag Prediction - Suggest tags based on the title and question text (multi-class classification).

1. Metrics: Mean F1 Score

2. Models used: Logistic Regression with OneVsRest Classifier, SGDClassifier with hinge loss

3. Approach:

   1. Used Matplotlib and Seaborn to visualize distribution of tags, tags per question and most frequent tags.

   2. Preprocessed the data using NLTK to remove stopwords, special characters, HTML tags and SnowballStemmer to stem the words. Saved the cleaned data to SQLite DB.

   3. Vectorized the words using TF-IDF and used LogisticRegression with One Vs Rest Classifier and SGDClassifier with hinge loss. Used GridSearch to tune hyperparameters.

Quora Question Pair Similarity – Identify whether a given pair of questions is similar (binary classification).

1. Metrics: LogLoss

2. Models used: Random Model, Logistic Regression, Linear SVM and XGBoost.

Santander Product Recommendation – Build a recommendation system so Santander can cross sell products to their existing customers (multi-class classification).

1. Metrics: LogLoss

2. Models used: XGBoost, Matrix Factorization Techniques – SVD

3. Approach:

   1. Explained the approach in a blog.

   2. Used Matplotlib and Seaborn to visualize products distribution by various features.

   3. Converted the dataset to be used as a multi-class classification problem.

   4. Created new feature lags and statistics metrics such as min, max and standard deviations.

   5. Modeled using XGBoost and Matrix Factorization techniques such as SVD.