Handwritten Digit Recognition using KNN

Description

This project is a data analysis task that uses the K-Nearest Neighbors (KNN) algorithm to classify handwritten digits. The model is trained on a data.csv file, which contains pixel data of handwritten digit images. The trained model is then used to analyze a test.csv file, and the results, including the confusion matrix and accuracy, are displayed.

Installation

To run this project, the following libraries need to be installed:

• Python 3.12
• NumPy
• Pandas
• scikit-learn
• Matplotlib

Install these dependencies using pip:

pip install numpy pandas scikit-learn matplotlib

Usage

1. Clone the repository or download the notebook to your local machine.

2. Prepare the data:

   • Ensure you have data.csv and test.csv files in the same directory as the notebook. These files should contain the pixel data for training and testing respectively.

3. Run the notebook:

   • Open the notebook in Jupyter Notebook or Jupyter Lab.
   • Execute the cells sequentially to train the KNN model and analyze the test data.

Data

• data.csv: This file contains the training data with pixel values for the handwritten digit images.
• test.csv: This file contains the test data which will be used for evaluating the KNN model.

Results

The notebook will output the following results:

• A confuion matrix displaying the performance of the model.
• The accuracy score of the model.
• Other relevant metrics and visualizations.

Author

This project was created by Vanshaj Raghuvanshi.

Built With

• jupyter-notebook