Recognizing Handwritten Digits with Python
Build a Logistic Regression Model with SKLearn, load and analyze 70,000 digits from the MNIST dataset, and accurately recognize samples!
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TECHNOLOGIES
WATCH TIME
32 minutes
LEVEL
Overview
This project is a beginner-friendly Python and Machine Learning application focused on building a logistic regression model to analyze and recognize handwritten digits from the MNIST dataset. We’ll read over 70,000 input samples, visualize and analyze each digit, create our testing and training datasets, fit the logistic regression model and visualize the accuracy of our predictions with a confusion matrix!
Project Tasks
Welcome to the project!
2 min
Processing and Visualizing our Digits
Process and visualize your handwritten digits in a Jupyter lab notebook.
7 min
Getting our MNIST Digits Dataset
Use the MNIST dataset to access thousands of handwritten digits to analyze.
8 min
Visualizing our Predictions and Confusion Matrix
Based on your numerical algorithm, predict the value of your visualized digit and assess the accuracy using a Confusion Matrix.
10 min
Applying Logistic Regression
Apply a logistic regression and a numerical algorithm on the visualized digits.
8 min