Supervised Machine Learning methods to classify short conversations extracted from Reddit

• 8 Classes based on conversation topics (Hockey, Movies, NBA, News, NFL, Politics, Soccer and WorldNews)

Data Cleaning and Feature Extraction:

• Label Encoding (Fit and Transform) & Decoding (Inverse Transform) using Scikit-Learn Proceprocessing label Encoder
• Lemmatization (using WordNetLemmatizer) - NLTK Package (Done to increase the accuracy)
• Term Frequency-Inverse Document Frequency (TF-IDF) approach - feature weighting

Reason to go with Random Forest Classifier:

• Performance in handling large datasets with higher dimensionality
• Efficiency in handling missing data issues
• Methods to balance errors in case of imbalanced classses

Reason to go with Stochastic Gradient Descent (SGD) Classifier:

• Efficiency
• Ease of implementation (numerous opprotunities for code tuning using hyper parameters)