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.
Here is a list of the projects currently in this repository:
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- A project focused on building a recommendation engine to suggest movies to users, likely using techniques like collaborative filtering or content-based filtering.
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- 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.
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- 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.
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.
To run these projects on your local machine, please follow these steps:
- Clone the repository:
git clone https://github.com/your-username/Machine-Learning-Projects.git
cd your-repo-name- Navigate to a project directory:
cd Machine-Learning-Projects/1_Movie Recommender- Install dependencies: It is highly recommended to create a virtual environment first.
pip install -r requirements.txt-
Run the Jupyter Notebook : If you want to train the model yourself, run the
code.ipynbnotebook -
Run the application:
streamlit run file_name.pyAryan Gupta
📍 Bhilai, Chhattisgarh
🔗 GitHub Profile
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