🚀 End-to-End Machine Learning Solutions for Real-World Problems

A curated collection of end-to-end Machine Learning,NLP and Data Science projects. This repository demonstrates production-grade engineering, industry-relevant problem-solving, and scalable AI pipelines.

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🛠️ Tech Stack

📂 Table of Contents

• 📌 Overview
• 📊 Projects
  • 📖 Book Recommender
  • 🤖 End-to-End Chatbot
  • 👗 Fashion Recommendation
  • 🏏 IPL Predictions
  • 🎬 Movie Recommendation
  • 🎵 Music Recommender
  • 📝 Next Word Generator
  • 💬 WhatsApp Chat Analyzer
  • 📺 YouTube Chaptering
• ⚙️ Installation
• 🤝 Contributing
• 📝 License
• 🌟 Why This Repository?
• 📬 Contact

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📌 Overview

Each project in this repository is designed with:

✅ Production-grade architecture
✅ Modular & scalable codebases
✅ State-of-the-art ML & NLP algorithms
✅ Real-world problem statements
✅ Well-documented workflows
✅ Visualizations, dashboards, and APIs where applicable

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📊 Projects

Project	Description	Key Tech
📖 Book Recommender	Collaborative filtering-based book recommendation engine	Python, Pandas, Scikit-learn
🤖 End-to-End Chatbot	Intent-based chatbot with Streamlit UI	NLTK, Scikit-learn, Streamlit
👗 Fashion Recommendation	Visual similarity recommender using CNN embeddings	TensorFlow, Keras, VGG16
🏏 IPL Predictions	Predictive modeling for IPL match outcomes	Pandas, Scikit-learn, Matplotlib
🎬 Movie Recommendation	NLP-powered movie recommendation system	NLTK, Pandas, Scikit-learn
🎵 Music Recommender	Hybrid recommender using Spotify API	Spotipy, Pandas, Scikit-learn
📝 Next Word Generator	LSTM-based next-word prediction model	TensorFlow, Keras, Numpy
💬 WhatsApp Chat Analyzer	Sentiment analysis & chat summarization	NLTK, Transformers, TextBlob
📺 YouTube Chaptering	Automated video chaptering via topic modeling	YouTube API, NMF, LDA, Scikit-learn

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🔍 Detailed Project Descriptions

📖 Book Recommender

Personalized book recommendations via collaborative filtering and cosine similarity.

• 🔧 Features: Popularity-based filtering, personalized suggestions, data serialization.
• 💻 Tech Stack: Pandas, Scikit-learn, NumPy

🤖 End-to-End Chatbot

Conversational AI with dynamic intent recognition and custom responses.

• 🔧 Features: Streamlit UI, NLTK preprocessing, Logistic Regression classifier.
• 💻 Tech Stack: NLTK, Scikit-learn, Streamlit

👗 Fashion Recommendation

Recommending visually similar clothing items using deep learning embeddings.

• 🔧 Features: VGG16 feature extraction, cosine similarity, visual output generation.
• 💻 Tech Stack: TensorFlow, Keras, PIL, Matplotlib

🏏 IPL Predictions

Predicting IPL match outcomes using statistical modeling and feature engineering.

• 🔧 Features: Data preprocessing, logistic regression, match progression visualization.
• 💻 Tech Stack: Pandas, Scikit-learn, Matplotlib

🎬 Movie Recommendation

Content-based movie recommender utilizing NLP-driven feature engineering.

• 🔧 Features: Tag creation, vectorization, cosine similarity-based recommendations.
• 💻 Tech Stack: NLTK, Scikit-learn, Pandas

🎵 Music Recommender

Hybrid recommendation engine leveraging Spotify's API & audio features.

• 🔧 Features: Content-based + popularity-based recommendations, Spotify API integration.
• 💻 Tech Stack: Spotipy, Pandas, Scikit-learn

📝 Next Word Generator

Predicts the next word in a sentence using LSTM networks.

• 🔧 Features: Tokenization, sequence padding, language modeling.
• 💻 Tech Stack: TensorFlow, Keras, Numpy

💬 WhatsApp Chat Analyzer

Sentiment analysis and chat summarization for WhatsApp conversations.

• 🔧 Features: Sentiment detection, text summarization, clustering, emoji filtering.
• 💻 Tech Stack: NLTK, TextBlob, Transformers, Scikit-learn

📺 YouTube Chaptering

Auto-generates video chapters using transcript-based topic modeling.

• 🔧 Features: Transcript parsing, NMF/LDA topic models, chapter segmentation.
• 💻 Tech Stack: YouTube API, Scikit-learn, Pandas, NMF, LDA

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Setup & Installation

1. Clone the repository:

   git clone https://github.com/Dash10107/ml-nlp-portfolio.git
   cd ml-nlp-portfolio

2. Run the notebooks:

   • Open any .ipynb file in Visual Studio Code or Jupyter Notebook.
   • Follow the instructions in each notebook to run the code and see results.

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Contributing

Contributions are welcome! Please open an issue or submit a pull request for improvements, bug fixes, or new features.

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License

This repository is licensed under the MIT License.

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⭐️ Why This Repo?

• End-to-End Solutions: Each project is complete, from data ingestion to model deployment.
• Real-World Impact: Projects solve practical problems in recommendation, prediction, and NLP.
• Clean & Modular Code: Easy to understand, extend, and deploy.
• Impressive Portfolio: Demonstrates expertise in AI, ML, NLP, and software engineering.

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Connect with me on LinkedIn or GitHub for collaborations and opportunities!

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