Text Classification using Python or R

Text Classification using Python or R For this assignment, you will do the following: Dataset selection Download a labeled text classification CSV dataset from a reputable source (e.g., Kaggle or a government website). Briefly state the source and task. Provide a clear description of the dataset you selected. Model Development (Python or R) Write a Python or R program to develop an NLP text classification model, including these steps: Load and explore the data. Preprocess the text (e.g., lowercase, remove punctuation/stopwords). Convert text to numerical features (e.g., BoW, TF-IDF). Train a classification model (e.g., Naive Bayes). Evaluate the model on a test set, reporting accuracy, precision, recall, F1-score, and the confusion matrix. Interpretation and Documentation Briefly interpret your evaluation metrics and discuss any challenges or potential improvements, focusing on: Overall Performance: What does the accuracy score tell you about how well your model generally classifies the text data? Class-Specific Performance: Examine the precision and recall for each class. Are there any classes that your model struggles to classify correctly? How are these reflected in the confusion matrix (e.g., high false positives or false negatives)? Limitations and Improvements: Briefly discuss one potential limitation of your chosen approach (e.g., the feature extraction method or the model) and suggest one way you could potentially improve the model’s performance in the future. Submission Guidelines Include the CSV dataset and dataset description in your submission Copy and paste your well-commented Python or R code and the documented results, interpretation, and discussion directly into a Word or Google Document.