docs/01_project_definition/03_solution.md

Project Definition - Solution

1. Data Preprocessing:
Clean the unstructured text to reduce noise. This includes text preprocessing (tokenization, stop-word removal

2. Feature Engineering (Vectorization):
Vectorization using TF-IDF or Bag-of-Words) to convert unstructured text into a format suitable for standard algorithms.

3. Modeling:
Models: We will train and compare the following classifiers:

1. Naive Bayes: As our primary baseline due to its speed and effectiveness in text data.
2. Logistic Regression: For its interpretability in binary classification.
3. Support Vector Machines (SVM): To test performance in high-dimensional feature spaces.
4. K-Nearest Neighbors (KNN): 5. Decision Trees:
6. Random Forest: To evaluate ensemble methods against linear models.
7. Stochastic Gradient Descent (SGD): For efficient handling of large-scaled data.

4. Evaluation Plan:
To rigorously assess performance, we will look beyond simple accuracy. We will use a Confusion Matrix to calculate and analyze:

- Precision and Recall: To understand the trade-off between false positives and false negatives.
- F1-Score: To provide a single metric for model balance, which is critical if our subset retains any class imbalance.
- Training Time: We will also log the time required to train each model to quantitatively support our argument regarding computational efficiency.