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🧠 Machine-Learning-Projects

         

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📖 About This Repository

My Machine Learning portfolio, showcasing various models and techniques on diverse datasets. Each folder is a standalone project with its own code, data, and detailed README.

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🚀 Projects Included

Here is a list of the projects currently in this repository:

1. Movie Recommender System

   • A project focused on building a recommendation engine to suggest movies to users, likely using techniques like collaborative filtering or content-based filtering.

2. Email Spam Classifier

   • A classic Natural Language Processing (NLP) project to classify emails as "spam" or "ham" (not spam). This project likely involves text preprocessing and a classification model like Naive Bayes or Logistic Regression.

3. Heart Disease Predictor

   • A Streamlit web app that uses multiple machine learning models (Logistic Regression, SVM, Random Forest) to predict the likelihood of heart disease based on patient data. Features single and bulk prediction modes.

4. Laptop Price Predictor

   • A regression-based machine learning project that predicts laptop prices based on specifications such as brand, processor type, RAM, storage, GPU, screen size, and operating system. It also includes an interactive Streamlit web application for real-time price prediction.

5. IPL Win Predictor

   • A ball-by-ball win probability predictor for IPL second-innings chases. Trained on historical IPL data (2008–2019) across 8 active franchises, using a Logistic Regression pipeline with features like runs left, balls left, wickets in hand, current run rate etc.

6. Book Recommender System

   • A dual-mode book discovery app built with Popularity-Based and Collaborative Filtering. Trained on the Book-Crossing dataset, it surfaces trending titles on the home page and recommends 5 similar books for any title the user selects -- all wrapped in a clean Streamlit UI.

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💻 Technologies Used

This repository primarily uses Python and Jupyter Notebooks. Key libraries used across these projects may include:

• Pandas: For data manipulation and analysis.
• NumPy: For numerical operations.
• Scikit-learn (sklearn): For building, training, and evaluating machine learning models.
• Matplotlib / Seaborn: For data visualization.
• NLTK / spaCy: For natural language processing tasks.

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📦 Getting Started

To run these projects on your local machine, please follow these steps:

1. Clone the repository:

    git clone https://github.com/your-username/Machine-Learning-Projects.git
    cd your-repo-name

2. Navigate to a project directory:

    cd Machine-Learning-Projects/1_Movie Recommender

3. Install dependencies: It is highly recommended to create a virtual environment first.

    pip install -r requirements.txt

4. Run the Jupyter Notebook : If you want to train the model yourself, run the code.ipynb notebook

5. Run the application:

    streamlit run file_name.py

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👨‍💻 Author

Aryan Gupta
📍 Bhilai, Chhattisgarh
🔗 GitHub Profile

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⭐ Support

If you like this project, leave a ⭐ and share it with others!