This repository contains an implementation of text classification and prediction using Logistic Regression. The project applies Natural Language Processing (NLP) techniques to process text data and train a model for classification tasks.
- Implements text classification using Logistic Regression.
- Performs text prediction using n-gram models.
- Utilizes
scikit-learnandNLTKfor text processing and model training. - Processes text using tokenization, vectorization, and feature extraction.
- Evaluates model performance using accuracy, precision, and recall metrics.
The project works with a labeled text dataset containing:
- Text samples for training and testing.
- Preprocessing steps include tokenization, stopword removal, and vectorization.
- TF-IDF Vectorization: Converts text data into numerical feature representations.
- Logistic Regression Classifier: A simple yet effective model for binary or multi-class classification.
- Probability Estimation: Uses the sigmoid function to predict class probabilities.
- N-gram Language Model: Uses previous words to predict the next word.
- Feature Extraction: Computes frequency-based features for prediction.
- Logistic Regression: Predicts the most probable next word in a sequence.
- Data Preprocessing:
- Tokenization and text cleaning.
- Vectorization using TF-IDF or CountVectorizer.
- Model Training:
- Logistic Regression trained with cross-entropy loss.
- Optimization using Stochastic Gradient Descent (SGD) or other solvers.
- Validation & Evaluation:
- Accuracy, precision, recall, and F1-score for classification tasks.
- Sample text predictions and evaluation of model accuracy.
- Accuracy: Measures how well the classifier performs.
- Precision & Recall: Evaluates classification performance in imbalanced datasets.
- Confusion Matrix: Visualizes model performance on test data.
- The text classification model predicts the category of input text.
- The text prediction model generates the next word in a given sequence using n-grams and Logistic Regression.