Machine-Learning-Models

Welcome to the Machine Learning Models, a curated collection of beginner-to-intermediate level ML classification and regression projects made by me. Each folder contains a self-contained project complete with dataset handling, model training, evaluation, and results visualization.

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🔍 Included Projects

🏠 House Price Prediction

Predicts house prices based on features using regression (Linear Regression, Ridge, Lasso).

➡️ View README

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🌸 IRIS Flower Classifier

Classic ML dataset for multi-class classification (Setosa, Versicolor, Virginica) using SVM.

➡️ View README

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🍄 Mushroom Classifier

Classifies mushrooms as edible or poisonous based on categorical attributes. Trained using:

• KNN
• Logistic Regression
• Random Forest

Best Models: Random Forest and KNN (Accuracy: 100%)

➡️ View README

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💬 SMS Spam Classifier

Detects whether a text message is spam or ham using text preprocessing and a Naive Bayes model.

➡️ View README

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📌 Goal

• Practice real-world ML pipelines
• Compare model performance
• Learn core concepts hands-on (EDA, preprocessing, metrics, model selection)
• Learn different ML Algorithms through hands-on-learning.

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🛠 Technologies Used

• Python
• scikit-learn
• pandas
• matplotlib / seaborn
• Google Colab

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👤 Author

• Name: Archangel
• GitHub: @archangel2006
• Email: 26.archangel@gmail.com

Feel free to ⭐️ this repository or fork it for your own use.