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Classifying Handwritten Digits with Machine Learning using MNIST Dataset

The goal of this data science project is to take an image of a handwritten single digit, and determine what that digit is.

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What will you learn

  • Caret
  • FNN
  • H20
  • Random Forrest
  • Logistic Regression
  • k-nearest techniques
  • deep learning
  • Convert pixel data into image

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.


  • Language used: Python

Project Description

Data scientists looking for their first machine learning or data science project begin by trying the handwritten digit recognition problem. The Digit Recognizer data science project makes use of the popular MNIST database of handwritten digits, taken from American Census Bureau employees. The dataset consists of already pre-processed and formatted 60,000 images of 28x28 pixel handwritten digits. With the use of image recognition techniques and a chosen machine learning algorithm, a program can be built to accurately read the handwritten digits with 95% accuracy. The accuracy rate can be higher based on the chosen machine learning algorithm,



Statistical Analyst SME

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