SAPR.txt

Project 1: Sentiment Analysis on Product Reviews (Easy)

Description:
Build a sentiment analysis tool that classifies product reviews as positive, negative, or neutral using pre-processed text data and machine learning models.

Steps to Complete:

Dataset Collection (1 Day): Use a dataset from Kaggle like the Amazon product reviews dataset.

Data Cleaning and Pre-Processing (2 Days):
Remove noise, stopwords, and special characters.

Tokenize and lemmatize the text.

Use TF-IDF or Bag of Words for vectorization.

Train-Test Split (1 Day): Split the dataset into training and testing data.

Model Training (2 Days): Train models like Logistic Regression or Naive Bayes.

Evaluation and Visualization (1 Day): Analyze accuracy, precision, recall, and F1-score.

Deployment (Optional) (2 Days): Use Flask to build a simple web app for real-time predictions.

Time Allotment: 7-9 Days

What You Will Learn
Text data pre-processing techniques.
Feature extraction methods like TF-IDF.
Basics of classification models.
Evaluating model performance.