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Merge pull request #1022 from Shantnu-singh/Shantnu-singh-perceptron
Added Perceptron Network
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Here is a short README file for your Perceptron implementation: | ||
# Perceptron Implementation | ||
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This repository contains a simple implementation of the Perceptron algorithm for binary classification. The Perceptron is a type of linear classifier that updates its weights and bias based on the errors made on the training data. | ||
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## Requirements | ||
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- numpy | ||
- pandas | ||
- matplotlib | ||
- scikit-learn | ||
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You can install these dependencies using pip: | ||
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```bash | ||
pip install numpy pandas matplotlib scikit-learn | ||
``` | ||
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## Usage | ||
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The `perceptron` class is implemented with methods to fit the model to the training data and make predictions on new data. The dataset used in the example is generated using `make_blobs` from `scikit-learn`. | ||
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## Results | ||
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The accuracy of the model is printed after training and predicting on the test set. Additionally, a plot is generated to visualize the decision boundary learned by the perceptron. | ||
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## How to Use This Repository | ||
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- Clone this repository to your local machine. | ||
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```bash | ||
git clone https://github.com/Niketkumardheeryan/ML-CaPsule/Perceptron From Scratch | ||
``` | ||
- For Python implementations and visualizations: | ||
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1. Ensure you have Jupyter Notebook installed | ||
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```bash | ||
pip install jupyter | ||
``` | ||
2. Navigate to the project directory in your terminal. | ||
3. Open perceptron.ipynb. |