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https://github.com/xgagandeep/naive-bayes-from-scratch
This project implements the Naive Bayes classification algorithm from scratch.
https://github.com/xgagandeep/naive-bayes-from-scratch
Last synced: 6 days ago
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This project implements the Naive Bayes classification algorithm from scratch.
- Host: GitHub
- URL: https://github.com/xgagandeep/naive-bayes-from-scratch
- Owner: xgagandeep
- Created: 2024-09-11T18:40:14.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-09-11T18:44:15.000Z (2 months ago)
- Last Synced: 2024-09-12T04:59:24.308Z (2 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 4.88 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Naive Bayes from Scratch
**Repository:** [xgagandeep/Naive-Bayes-from-Scratch](https://github.com/xgagandeep/Naive-Bayes-from-Scratch)
**Date:** 2020
**Language:** Python
**Libraries:** NumPy, pandas, scikit-learn
## Description
This project implements the Naive Bayes classification algorithm from scratch. The notebook demonstrates how to preprocess data, split it into training and testing sets, and build a Naive Bayes classifier to make predictions and evaluate its performance.
## Features
- **Data Preprocessing:** Reads and encodes categorical data using `LabelEncoder`.
- **Model Training:** Implements a Naive Bayes classifier with methods to calculate prior probabilities, conditional probabilities, and predictions.
- **Model Evaluation:** Evaluates the model's accuracy on a test dataset.
## Files
- **`Naive bayes from scratch.ipynb`:** Jupyter Notebook containing the implementation of the Naive Bayes classifier.
## Installation
To run this project, you need Python and the required libraries installed. Follow these steps:
1. **Clone the repository:**
```bash
git clone https://github.com/xgagandeep/Naive-Bayes-from-Scratch.git
```
2. **Navigate to the project directory:**
```bash
cd Naive-Bayes-from-Scratch
```
3. **Install the required libraries:**
```bash
pip install numpy pandas scikit-learn
```
4. **Download the dataset:**
This project uses the Mushroom Classification dataset. Ensure you have the dataset (`mushrooms.csv`) available in the directory specified in the notebook or update the path accordingly.
5. **Run the Jupyter Notebook:**
```bash
jupyter notebook Naive\ bayes\ from\ scratch.ipynb
```
## Usage
1. **Data Preprocessing:**
- Load the dataset and encode categorical features using `LabelEncoder`.
2. **Model Training:**
- Implement the Naive Bayes classifier functions:
- `prior_prob(x_train, y_train, label)`: Computes the prior probability of a class label.
- `cond_prob(x_train, y_train, feature_col, feature_value, label)`: Computes the conditional probability of a feature given a class label.
- `predict(x_train, y_train, xtest)`: Predicts the class label for a given test sample.
3. **Model Evaluation:**
- Use the `score(x_train, y_train, x_test, y_test)` function to evaluate the classifier's accuracy on the test dataset.
4. **Testing:**
- Test the classifier with sample data and print the predicted and actual labels.
## Example
The notebook demonstrates the full workflow of training and evaluating a Naive Bayes classifier. It loads a dataset of mushrooms, preprocesses the data, trains the classifier, and evaluates its performance by predicting on test samples.
## Contribution
Feel free to contribute to this project by submitting issues or pull requests. For any questions or feedback, please open an issue on the GitHub repository.