Description
Objectives:
• Gain experience with sklearn using text data
• Gain experience with text classification
Turn in:
• This program should be created in a notebook (Jupyter, Google, or Kaggle)
• Print to pdf and upload your pdf to eLearning and your Portfolio
Instructions:
1. Go to Kaggle.com or any other resource. Find a text classification data set that interests you. I
suggest a smaller data set. Divide into train/test. Create a graph showing the distribution of the
target classes. Describe the data set and what the model should be able to predict.
2. Try Naïve Bayes, Logistic Regression, Neural Networks using sklearn.
3. Write up your analysis of the performance of various approaches
Grading Rubric:
– Your grade is not determined by the accuracy achieved, but by how much work and thought you
put into it
