This repository contains a collection of projects developed using Jupyter Notebooks, focusing on testing various machine learning pipelines and neural network models. Some projects also involve a statistical machine learning approach, showcasing the versatility and depth of the analyses conducted.
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Project 1:
- Dataset: [Dataset Name 1]
- Description: This project explores different machine learning pipelines to predict [target variable] based on [features]. Various models such as Random Forest, SVM, and Gradient Boosting were tested to find the most suitable approach.
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Project 2:
- Dataset: [Dataset Name 2]
- Description: Utilizing neural network models, this project aims to classify [categories/classes] based on [features]. Different architectures and hyperparameters were tested to optimize the model performance.
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Project 3:
- Dataset: [Dataset Name 3]
- Description: Incorporating a statistical machine learning approach, this project delves into [specific analysis]. Techniques such as [technique 1] and [technique 2] were applied to gain insights and make predictions.
- /data: Contains the datasets used in the projects.
- /notebooks: Includes Jupyter Notebooks for each project, detailing the data exploration, model development, and results analysis.
- /models: Stores the trained models for future reference or deployment.
To explore the projects and run the Jupyter Notebooks locally, follow these steps:
- Clone the repository:
$ git clone https://github.com/yourusername/machine-learning-projects.git - Install the required dependencies:
$ pip install -r requirements.txt - Launch Jupyter Notebook:
$ jupyter notebook - Open the desired project notebook and run the cells sequentially.
- Python 3.x
- Jupyter Notebook
- NumPy
- Pandas
- Scikit-learn
- TensorFlow
- Keras
- Matplotlib
- Seaborn
This project is licensed under the MIT License - see the LICENSE.md file for details.