Natural Language Processing & Machine Learning

This repository contains a collection of end-to-end Natural Language Processing (NLP) and Machine Learning projects. It demonstrates a complete lifecycle of AI development—from raw text ingestion and advanced preprocessing to deep learning model architecture and production-ready deployment via APIs.

🛠️ Technical Ecosystem

• Core Languages: Python (NumPy, Pandas, Matplotlib, Seaborn)
• NLP Frameworks: SpaCy, NLTK, TextBlob, Regular Expressions (Regex)
• Machine Learning: Scikit-Learn (Linear & Logistic Regression, SVM, KNN, Random Forest, Decision Trees)
• Deep Learning: TensorFlow, Keras (ANN, CNN, LSTM)
• Deployment & Tools: Flask (REST APIs), PyPi Package Management, Git, VS Code, Jupyter Notebooks

---

🚀 Key Implementations

1. Advanced Text Preprocessing & Engineering

A modular approach to transforming unstructured text into machine-readable data:

• Text Cleaning: Automated pipelines for removing noise (URLs, mentions, stopwords) and expanding contractions.
• Linguistic Analysis: Tokenization, Part-of-Speech (POS) tagging, Lemmatization, and Stemming.
• Vectorization: Implementation of Bag-of-Words (BoW) and TF-IDF for statistical feature extraction.
• Embeddings: Utilizing Word2Vec and GloVe for semantic representation.

2. Supervised Learning & Classification

Classic machine learning architectures applied to real-world datasets:

• Sentiment Analysis: Benchmarking Logistic Regression and SVM on the IMDB Movie Reviews dataset.
• Spam Detection: High-accuracy binary classification of text communications.
• Multi-Label Tagging: Predicting multiple relevant tags for Stack Overflow technical posts.

3. Deep Learning & Generative Models

Leveraging neural networks for complex sequence modeling:

• Sequence Modeling (LSTM): Generative AI model for automated poetry creation.
• Social Analytics (CNN): Deep learning classifiers for Hate Speech detection and Disaster Tweet categorization.
• Entity Recognition: Custom NER models using SpaCy for automated Resume (CV) parsing and information extraction.

4. Software Engineering & Deployment

• Python Packaging: Developed and published a custom text-processing library to PyPi.
• Model-as-a-Service: Deployed machine learning models as web applications using the Flask framework, allowing for real-time inference via API endpoints.

---

📂 Project Structure

├── packages/             # Source code for custom PyPi packages
├── classic_ml/           # Regression and Classification implementations
├── deep_learning/        # ANN, CNN, and LSTM models
├── nlp_pipelines/        # SpaCy/NLTK preprocessing and NER scripts
├── deployment/           # Flask API and web application files
└── data/                 # Dataset references and cleaning scripts

⚙️ Installation

1. Clone the repository:

git clone https://github.com/RutujaKumbhar17/Natural-Language-Processing-in-Python.git

2. Install dependencies:

pip install -r requirements.txt

---

🤝 Connect with Me

I am always open to collaborating on open-source projects or discussing new opportunities in AI/ML.

• Portfolio: rutujakumbhar.netlify.app
• LinkedIn: Rutuja Kumbhar
• GitHub: @RutujaKumbhar17

---

Developed with ❤️ by Rutuja Kumbhar

---