https://github.com/derrickbaruga7/pytorch-machine-learning

This project demonstrates a PyTorch-based machine learning model for predicting sales from historical purchase data. It includes:
https://github.com/derrickbaruga7/pytorch-machine-learning

data-science machine-learning marketing predictive-modeling pytorch

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This project demonstrates a PyTorch-based machine learning model for predicting sales from historical purchase data. It includes:

• Host: GitHub
• URL: https://github.com/derrickbaruga7/pytorch-machine-learning
• Owner: derrickbaruga7
• Created: 2024-07-24T18:57:28.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-07-28T22:33:46.000Z (6 months ago)
• Last Synced: 2024-07-28T23:27:33.781Z (6 months ago)
• Topics: data-science, machine-learning, marketing, predictive-modeling, pytorch
• Language: Jupyter Notebook
• Homepage:
• Size: 197 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Pytorch-Machine-Learning

This PyTorch project is designed to predict sales based on historical purchase data. It encompasses data cleaning, feature engineering, and model training using a neural network. The neural network employs mean squared error (MSE) as the loss function and leverages mini-batch processing through DataLoader. The final MSE on the test set is approximately 0.1400. Complete code and instructions are available for replication and further experimentation.

### PyTorch Purchase Prediction Model

### Overview

This GitHub project demonstrates a PyTorch-based machine learning model for predicting sales from historical purchase data. It includes:

### 1. Data Preparation:

- Importing necessary libraries such as PyTorch for deep learning, Scikit-learn for data preprocessing, and Matplotlib for data visualization.

- Loading and inspecting the dataset (Sales Data.csv), which contains detailed purchase records.

- Performing data cleaning and feature engineering, including converting categorical variables to dummy variables and standardizing features.

- Splitting the dataset into training and testing sets.

### 2. Model Architecture:

- Building a neural network with three fully connected layers and ReLU activations using PyTorch.

- Training the model using mini-batch processing with DataLoader and optimizing with Adam.

### 3. Training and Evaluation:

- Training the model for 100 epochs while monitoring the mean squared error loss.

- Evaluating the model's performance on the test set, reporting the mean squared error.

### 5. Code and Instructions:

- The repository includes all necessary code and instructions to reproduce and extend the sales prediction model.