Naive Bayes Project Tutorial

• In this project we should follow the instructions carefully because this is a brief introduction to NLP and how to deal with sentiment analysis.

• Of course, we'll handle it with Naive Bayes.

• We'll have the opportunity to go deeper with sentiment analysis and NLP in its own future lesson.

🌱 How to start this project

You will not be forking this time, please take some time to read this instructions:

1. Create a new repository based on machine learning project by clicking here.
2. Open the recently created repostiroy on Gitpod by using the Gitpod button extension.
3. Once Gitpod VSCode has finished opening you start your project following the Instructions below.

🚛 How to deliver this project

Once you are finished creating your naive bayes model, make sure to commit your changes, push to your repository and go to 4Geeks.com to upload the repository link.

📝 Instructions

Naive sentiment analysis

This is a simple project using Naive Bayes Classifier and Scikit-learn to create a Google Play store reviews classifier (Sentiment Analysis) in Python. You will categorize user reviews as good or bad. The Naive Bayes classification technique is a simple and powerful classification task in machine learning. In this dataset, we use the 23 most popular mobile apps, and only two columns.

Step 1:

We have three columns: package name, review and polarity (0 = bad, 1 = good) Preprocess the data by eliminating the package name column and putting all reviews in lower case.

Step 2:

Separate target from feature, and split your data.

Step 3:

Vectorize your features and use Naive Bayes to classify the reviews as good or bad. We will not focus on hypertuning our model this time. This was an introduction project to sentiment analysis using Naive Bayes.

Step 4:

Use the app.py to create your pipeline.

Save your naive bayes classification model in the 'models' folder.

In your README file write a brief summary.

Solution guide: https://github.com/4GeeksAcademy/naive-bayes-project-tutorial/blob/main/solution_guide.ipynb