🌟 Machine Learners: Machine Learning Group Assignments & ML 100 Min Challenge 🌟

Overview 📚

This repository contains solutions to various machine learning tasks completed by the Machine Learners team. The tasks are organized into four main categories:

1. Regression 📉 - Predicting continuous values (Dairy Goods Sales Dataset)
2. Classification 🔍 - Predicting discrete labels from input features (Amazon Products Dataset)
3. Unsupervised Learning 🔎 - Extracting meaningful patterns from unlabeled data (Customer Support on Twitter Dataset)
4. ML 100 Min Challenge ⏱️ - Solving multiple machine learning challenges in under 100 minutes

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Project Structure 📁

Machine-Learners/
├── Regression/                           # Contains regression models 📈
│   ├── dairy_dataset.csv                # Dataset for regression task (Dairy Goods Sales) 🧀
│   └── Regression_MachineLearners.ipynb  # Jupyter Notebook for regression task 📝
│
├── Classification/                       # Contains classification models 🛍️
│   ├── Amazon-Products.zip               # Raw dataset for classification (Amazon Products) 📦
│   └── Classification_T5.ipynb           # Jupyter Notebook for classification task 🧑‍💻
│
├── Unsupervised/                        # Contains unsupervised learning tasks 🧠
│   └── T5-Unsupervised.ipynb            # Jupyter Notebook for unsupervised learning task 🔍
│
├── 'ML Challenge'/                       # ML 100 Min Challenge folder ⏱️
│   ├── ML_Challenge1_T5.ipynb           # Jupyter Notebook for first ML challenge 🏆
│   ├── ML_Challenge2_T5.ipynb           # Jupyter Notebook for second ML challenge 🏅
│
└── README.md                            # This file 📄

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Team Members 👨‍💻👩‍💻

• 202418013 - Darshita Dwivedi
• 202418025 - Kelvi Bhesdadiya
• 202418057 - Eric Thomas
• 202418058 - Ujjwal Bhansali

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Subprojects Overview 🔍

1. Regression 📊

This subproject focuses on predicting continuous values using machine learning. We use a Dairy Goods Sales Dataset to apply regression models.

• dairy_dataset.csv: The dataset contains information on dairy product sales. The goal is to predict continuous values such as sales amounts.
• Regression_MachineLearners.ipynb: The Jupyter notebook where data is processed, various regression models are trained, and predictions are made on sales values in the dairy goods industry.

2. Classification 🏷️

This subproject aims to classify e-commerce products into categories based on product names. We use the Amazon Products Dataset for this task.

• Amazon-Products.zip: A dataset that contains product names and categories from Amazon.
• Classification_T5.ipynb: This notebook covers the steps of text cleaning, feature extraction (e.g., TF-IDF), and training classification models (e.g., Logistic Regression, Random Forest) to predict product categories.

3. Unsupervised Learning 🧠

The Unsupervised Learning subproject aims to identify meaningful patterns in unlabeled data. The dataset used involves customer support interactions on Twitter.

• T5-Unsupervised.ipynb: This notebook applies unsupervised learning techniques like clustering, dimensionality reduction, and pattern recognition to customer support interactions on Twitter.
• Dataset: Customer Support on Twitter

4. ML 100 Min Challenge ⏳

This folder contains solutions to the ML 100 Min Challenge, where we solve multiple machine learning tasks in under 100 minutes.

• ML_Challenge1_T5.ipynb: The first challenge in the ML 100 Min Challenge, where we apply a machine learning model to solve the problem.
• ML_Challenge2_T5.ipynb: The second challenge in the ML 100 Min Challenge, continuing from the first with a new dataset and task.

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How to Run the Project 🚀

1. Install Dependencies ⚙️

To run the notebooks, install the required dependencies. It is recommended to use a virtual environment:

pip install -r requirements.txt

The requirements.txt includes essential libraries such as:

• numpy
• pandas
• sklearn
• matplotlib
• seaborn
• plotly
• nltk

2. Running the Notebooks 💻

• Navigate to the respective folder (e.g., Regression, Classification, or Unsupervised) depending on your task.
• Open the relevant Jupyter Notebook (.ipynb) in a Jupyter notebook environment (e.g., JupyterLab or Google Colab).
• Execute the cells step-by-step to see the outcomes of each stage in the machine learning pipeline.

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Description of Files 🗂️

Regression Folder 📉

• dairy_dataset.csv: Contains data related to dairy goods sales, used for regression tasks.
• Regression_MachineLearners.ipynb: This notebook handles data analysis, model training, and sales predictions in the dairy goods sector.

Classification Folder 🛒

• Amazon-Products.zip: A dataset with product information such as names and categories for classification tasks.
• Classification_T5.ipynb: This notebook involves text preprocessing, feature extraction, and model training (Logistic Regression, Random Forest) to classify products.

Unsupervised Folder 🔍

• T5-Unsupervised.ipynb: Explores unsupervised learning techniques, such as clustering and dimensionality reduction, applied to customer support data.
• Dataset: Customer Support on Twitter

ML Challenge Folder ⏱️

• ML_Challenge1_T5.ipynb: Solution for the first ML challenge task.
• ML_Challenge2_T5.ipynb: Solution for the second ML challenge task.

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Acknowledgements 🙏

• Dataset Sources:

  • Amazon Products: Kaggle - Amazon Products Dataset
  • Dairy Goods Sales: Kaggle - Dairy Goods Sales Dataset
  • Customer Support on Twitter: Kaggle - Customer Support on Twitter

• Libraries Used:

  • numpy, pandas, sklearn, matplotlib, seaborn, plotly, nltk

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Future Work 🚀

• Classification: Experiment with deep learning models like CNNs or LSTMs to potentially enhance performance.
• ML Challenge: Continue tackling additional challenges and applying more advanced machine learning techniques.
• Regression: Incorporate additional features to improve the prediction accuracy.
• Unsupervised Learning: Test different clustering algorithms and dimensionality reduction techniques to better understand data patterns.