samich246 / food-order-predictor

This repository contains my project "Food Order Predictor". The primary goal of this project is to automate the prediction of customer orders using machine learning techniques.

Food Order Predictor

Introduction

The primary objective of this project is to automate order prediction to enhance customer satisfaction and reduce the workload on staff. We aim to build a machine learning model to predict orders based on the provided dataset.

Exploratory Data Analysis (EDA)

• Conducted EDA to understand the distribution and biases in the data.
• Visualized feature distributions and interactions with the target variable.
• Provided insights into the data's patterns and correlations.

Implications

Ethical Implications

• Considered ethical implications related to data collection and usage, ensuring data respects privacy and obtains necessary consents.

Business Outcome Implications

• Assessed the potential business benefits of order prediction, such as reduced staff workload and improved customer experience.

Technical Implications

• Discussed the technical aspects of model integration, scalability, and data security.

Model Building

• Utilized scikit-learn to build machine learning models, including Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine models.
• Conducted hyperparameter tuning using GridSearchCV to optimize model performance.

Model Evaluation

• Evaluated model performance using various metrics, including accuracy, precision, recall, and F1-score.
• Provided classification reports for model assessment.

Considerations

Considerations for the deployment and maintenance of the solution, including model updates, data security, scalability, and model explainability.

View Full Report

For a more detailed analysis, view the full report in the Report.md file