Machine Learning Classification Projects

This repository contains multiple Machine Learning classification projects implemented in Python using popular libraries like scikit-learn, pandas, seabon, matplotlib, and numpy. The projects demonstrate how to preprocess data, train classification models, and evaluate their performance using metrics like accuracy, precision, recall, and F1-score.

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Projects Included

1. Iris Classifier

   • Classifies iris flowers into species based on features like sepal length, sepal width, petal length, and petal width.
   • Models used: Logistic Regression,KNN,SVM,Naive_bayes etc.

2. Spam detection

   • Classifies Spam or not-spam.
   • Model used: MultinomialNB.
   • Includes data preprocessing, feature encoding, and model evaluation.

3. Titanic Survival Prediction

   • Classifies based on passengers data

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Features

• Data Preprocessing: Handling missing values, encoding categorical features, scaling numerical features.
• Model Training: Implemented multiple classifiers to compare performance.
• Evaluation Metrics: Accuracy, Precision, Recall, F1-score.
• Notebook Format: All projects are provided in Jupyter Notebooks for easy experimentation.

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How to Run

1. Clone the repository: bash https://github.com/shihabstdio/ml_spam_detection_classification_model/tree/main