This document summarizes a project to classify handwritten digits from the MNIST dataset using a decision tree strategy. It discusses using decision trees to determine informative features and construct a classifier from 60,000 training examples and 10,000 test examples. The implementation loads data, trains on 21,000 items, tests on the remaining items, and displays predictions with 28x28 pixel images. Accuracy is improved further with a nearest neighbor final test, achieving 99.6% classification rate. Screenshots of the running code are provided in an attached folder.

1. MNIST Handwritten Digit
Classification Challenge
(ECKOVATION MACHINE LEARNING)
PROJECT REPORT

2. WHAT IS MNIST ?
 The MNIST database of handwritten digits, available from this page, has a training set
of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set
available from NIST. The digits have been size-normalized and centered in a fixed-size
image.It is a good database for people who want to try learning techniques and
pattern recognition methods on real-world data while spending minimal efforts on
preprocessing and formatting.
 The original black and white (bilevel) images from NIST were size normalized to fit in a
20x20 pixel box while preserving their aspect ratio. The resulting images contain grey
levels as a result of the anti-aliasing technique used by the normalization algorithm.
the images were centered in a 28x28 image by computing the center of mass of the
pixels, and translating the image so as to position this point at the center of the 28x28
field.

3. STRATEGY USED AND WHY ?! :
 DECISION TREE : It is impossible to calculate all of the features in an image. We
therefore use decision trees to simultaneously determine a small collection of
informative features and construct a classifier. By only considering a small random
sample of queries at each mode we are able to generate multiple, randomized
trees that determine a more varied and informative collection of features than is
possible with a single tree. The trees, which provide posterior estimates of the
class probabilities, are aggregated to produce a stable and robust classifier.^ We
analyze the performance of this method and propose several means of
augmenting its performance.

4. HOW IT IS IMPLEMENTED?
 1. Necesssary libraries are imported –numpy,scikitlearn,matplotlib,pandas
 2. Csv file is loaded and converted into a matrix form
 3. Training is done for 21000 items out of 45000 items
 4. Testing is done for (45000-21000) items
 5. Data will be fit according to 28*28 matrix for the image production
 6. Image will be shown on a white background in a black font
 7. Prediction will be done by giving an input and getting an image as a output
 8. Accuracy will be taken out on the screen

5. HOW THE ACCURACY CAN BE IMPROVED:
 Most notably, we introduce a nearest neighbor final test that reduces the already
low error rate an additional 20-30%. Testing was done on a subset of a National
Institute of Standards and Technology database, and we report a classification
rate of 99.6%, comparable to the top results reported elsewhere

6. SCREENSHOTS AND GITHUB LINK:
 SCREENSHOTS OF THE RUNNING CODE AND THE RUNNING APPLICATION IS
ATTACHED IN THE “HANDWRITNG RECOGNITION CHALLENGE” FOLDER ITSELF!