Welcome to Classification-in-Machine-Learning 🚀 — a collection of notebooks, examples, and resources focused on understanding and applying machine learning classification techniques. Whether you’re a beginner exploring the basics 📘 or an enthusiast fine-tuning models for better performance ⚡, this repo is your guide.
Classification is a supervised learning task where the goal is to predict discrete labels (e.g., spam vs. not spam, cat vs. dog).
In simple terms: it's teaching a machine to put things in the right bucket 🎯.
Classification-in-Machine-Learning/
│
├── datasets/ # 📊 Sample datasets for experiments
├── notebooks/ # 📓 Jupyter notebooks with step-by-step tutorials
├── .gitignore # 🛠️ unpushed files
├── requirements.txt # 📦 Dependencies
└── README.md # 📝 You are here!
- 🌲 Decision Trees
- 🐦 Naive Bayes
- 📏 k-Nearest Neighbors (KNN)
- 🎯 Logistic Regression
- 🧩 Support Vector Machines (SVM)
- 🤖 Random Forests
- 🧠 Neural Network (basic classification)
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Clone the repo
git clone https://github.com/subhasish20/Classfication-in-Machine-Learning.git cd Classification-in-Machine-Learning -
Install dependencies
pip install -r requirements.txt
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Run a notebook
jupyter notebook notebooks/
- Load dataset
- Split into train/test
- Train a model
- Evaluate with accuracy, precision, recall, F1-score 📈
- Visualize results with confusion matrices 🔍
- ✅ Clean implementation of popular classification algorithms
- ✅ Ready-to-run Python notebooks with explanations
- ✅ Easy integration with custom datasets
- ✅ Visualizations for better intuition 🖼️
Here’s a sneak peek at what you’ll find inside:
- 📊 Decision boundary plots
- 🌀 Confusion matrices
Contributions are welcome! 🌟
Whether it’s fixing bugs 🪲, adding new algorithms ✍️, or improving documentation 📚 — every bit helps.
Fork ➡️ Code ➡️ Pull Request = 💡 Progress
Gratitude to the open-source ML community 💜 for providing the tools, libraries, and knowledge that make projects like this possible.