This repository contains the code and documentation for the Handwritten Digit Prediction project.
The objective of this project was to build a machine learning model that accurately predicts handwritten digits (0-9) using the sci-kit-learn's built-in "digits" dataset. The dataset consists of images of handwritten digits, where each image is an 8x8 pixel matrix. The project involved data preprocessing, model training, evaluation, and prediction.
The dataset used for this project is the "digits" dataset from sci-kit-learn's built-in datasets. It consists of 1797 images of handwritten digits, each represented by an 8x8 pixel matrix. The target variable represents the actual digit value (0-9).
After running the notebook, you will see the accuracy of the trained model and performance metrics such as the confusion matrix and classification report. Additionally, you can use the trained model to make predictions for new handwritten digits.
The Handwritten Digit Prediction project showcases the power of classification models in identifying handwritten digits with high accuracy.