Supervised Learning - Classification 📊

This repository contains implementations of the Naive Bayes and K-Nearest Neighbors (KNN) algorithms from scratch in Python using Numpy and Pandas for the task of classification.

Overview ℹ️

In this project, we implement two classical supervised learning algorithms: Naive Bayes and K-Nearest Neighbors (KNN). These algorithms are widely used for classification tasks in machine learning. The purpose of this project is to provide a clear understanding of how these algorithms work and to demonstrate their implementation from scratch using Python.

Implementation Details 🛠️

Libraries Used 📚

• Numpy: For numerical computing.
• Pandas: For data manipulation and analysis.
• Matplotlib: For data visualization.

Data Preprocessing 🔄

The provided data is preprocessed before applying the classification algorithms. This includes dropping unnecessary columns, filling missing values, and converting categorical variables into numerical ones.

Naive Bayes Classifier

• The Naive Bayes classifier is implemented to predict the class labels for the given test data.
• It computes the class probabilities and the mean and variance for each feature and class.
• The predicted labels are based on the maximum posterior probability.

K-Nearest Neighbors Classifier (KNN) 🤝

• The KNN classifier is implemented to predict the class labels for the given test data.
• It calculates the distances between the test samples and the training samples.
• The predicted labels are based on the majority class among the k nearest neighbors.

Usage 🚀

1. Clone the repository:

   git clone https://github.com/your_username/supervised-learning-classification.git

2. Install the required libraries:

   pip install numpy pandas matplotlib

3. Run the Python script:

   jupyter notebook FSM_SUPERVISED1.ipynb

Files 📁

• main.py: Contains the main script for preprocessing data and applying classification algorithms.
• train.csv: Training dataset.
• test.csv: Test dataset.
• gender_submission.csv: Sample submission file for test dataset.

Results 📈

• The accuracy of Naive Bayes classifier: 63.6%
• The accuracy of KNN classifier: 65.6%

Contributing 🤝

Contributions are welcome! If you find any issues or have suggestions for improvements, feel free to open an issue or create a pull request.

License 📝

This project is licensed under the MIT License. See the LICENSE file for details.