This repository contains a Python implementation of a Multinomial Logistic Regression model, which is used for classification tasks. The model is built and trained using the TensorFlow library on the MNIST dataset, a popular dataset for digit classification.

1. Installation
2. Usage
3. Dataset
4. Model
5. Results
6. License

To run this project, you will need to have Python installed along with the following libraries:

• pandas
• tensorflow
• numpy
• matplotlib

You can install the necessary dependencies using the following command:

pip install pandas tensorflow numpy matplotlib

1. Clone the repository:

   git clone https://github.com/yourusername/MultinomialRegressionModelforClassification.git

2. Navigate to the project directory:

cd MultinomialRegressionModelforClassification

3. Open and run the Jupyter Notebook:

jupyter notebook MultinomialRegressionModelforClassification.ipynb

The notebook walks through the entire process of building, training, and evaluating a Multinomial Regression model for digit classification using the MNIST dataset.

The dataset used in this project is the MNIST dataset, which is loaded directly from the TensorFlow/Keras library. The dataset contains 70,000 grayscale images of handwritten digits (0-9), split into 60,000 training images and 10,000 test images.

MNIST = tf.keras.datasets.mnist

The model implemented is a Multinomial Logistic Regression model, which is trained on the MNIST dataset to predict the digit represented in an image. The main steps include:

Loading and preparing the dataset Defining the model using TensorFlow Training the model using cross-entropy loss Evaluating the model's accuracy on test data

After training the model, its performance is evaluated based on accuracy metrics, and the results are visualized using confusion matrices and plots of model performance.

This repository is public.