Support Vector Machine-Machine learning

Model Evaluation and Validation

Project: Digits-Recongnition

Install

This project requires Python and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• scikit-learn
• You will also need to have software installed to run and execute a Jupyter Notebook.

Code

Template code is provided in the Digits Recongiation(2).ipynb notebook file. You will also be required to use the included Python file and dataset file to complete your work. While some code has already been implemented to get you started, you will need to implement additional functionality when requested to successfully complete the project. If you are interested in how the visualizations are created in the notebook, please feel free to explore this Python file.

Run

In a terminal or command window, navigate to the top-level project directory (that's there in the project) and run one of the following commands:

ipython notebook digits recongiation.ipynb

or

jupyter notebook digits recongiation.ipynb

or open with Juoyter Lab

jupyter lab

This will open the Jupyter Notebook software and project file in your browser.

Support Vector Machine(SVM)

A support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text.

Data:

The digits recognition takes the hand written data from given data set in the model the numbers are from 0-8.

Target Variable

1. target: numbers from 0 to 8.

Note:

1.Try over the numbers 0 to 8.

2.Accuracy can vary cause of the test and train data so nothing worried about it

3.Love to solve your issues