Sklearn Logistic Regression for Digit Recognition This project utilizes Scikit-learn's logistic regression algorithm to recognize digits from the Scikit-learn digits dataset.
The goal of this project is to build a machine-learning model using logistic regression to accurately classify handwritten digits. The Scikit-learn library provides a built-in dataset called "digits" that consists of 8x8 images of digits ranging from 0 to 9. Each image is represented as a 64-dimensional feature vector, where each feature corresponds to a pixel intensity value.
The logistic regression algorithm is a popular and widely used classification algorithm. It works by fitting a logistic function to the training data and then using that function to predict the probability of an instance belonging to a particular class. In this case, the logistic regression model will learn to predict the digit represented by an input image.
The project involves the following steps:
- Loading the Scikit-learn digits dataset.
- Preprocessing the data, which may include scaling, normalization, or feature extraction.
- Splitting the dataset into training and testing sets.
- Training the logistic regression model using the training data.
- Evaluating the model's performance on the testing data.
- Making predictions on new, unseen images of digits.
The accuracy of the model will be assessed based on its ability to correctly classify digits from the testing set. The project will provide insights into the effectiveness of logistic regression for digit recognition and serve as a foundation for further exploration of image classification tasks.
To run this project, make sure you have the following dependencies installed:
- Python (version 3.6 or above)
- Scikit-learn
You can install Scikit-learn using pip:
pip install scikit-learn
Once you have the dependencies installed, you can proceed with running the code and following the instructions in the Usage section.
To use this project, follow the steps below:
-
Install the required dependencies as mentioned in the Installation section.
-
Import the necessary modules in your Python script:
from sklearn.datasets import load_digits
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split- Load the digits dataset using
load_digits()function:
digits = load_digits()-
Preprocess the data if needed, such as scaling, normalization, or feature extraction.
-
Split the dataset into training and testing sets using
train_test_split():
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2, random_state=42)- Create an instance of the logistic regression model:
model = LogisticRegression()- Train the model using the training data:
model.fit(X_train, y_train)- Evaluate the model's accuracy on the testing data:
accuracy = model.score(X_test, y_test)
print("Accuracy:", accuracy)- Make predictions on new, unseen images using the trained model.
You can modify and expand upon these instructions based on the specific requirements and functionalities of your project. Include any additional code snippets or explanations to guide users on how to utilize your project effectively.
Feel free to customize and add more details to the code snippets and instructions based on the specific functionalities of your project.
This project is currently not licensed, and all rights are reserved.