This project uses a random forest classifier to recognize handwritten digits from the MNIST dataset. The program loads the dataset, visualizes some of the images, and trains a random forest classifier on a subset of the data. It then tests the classifier on a validation set to determine its accuracy in recognizing digits.

Preprocessing the Data

The program loads the digits dataset from the Scikit-learn library, which contains 1797 images of handwritten digits. Each image is a 8x8 matrix of pixel values. The program preprocesses the data by reshaping the images into a 1D array of length 64, so that it can be used as input for the Random Forest Classifier algorithm.

Training the Random Forest Classifier

The program uses the Random Forest Classifier algorithm to train the model to recognize the handwritten digits. The program creates a random sample of the data for training the model, and a validation set for evaluating the accuracy of the model. The Random Forest Classifier algorithm is trained on the sample data, and the accuracy of the model is evaluated on the validation data.

Predicting the Handwritten Digit Once the model is trained, the program can predict the handwritten digit in an input image. The program loads the input image and preprocesses it by reshaping it into a 1D array of length 64, so that it can be used as input for the trained model. The program then uses the trained model to predict the handwritten digit in the input image.