Azure Machine Learning Project: Decision Tree Model

Project Description

In this project, I developed a machine learning model using Azure Machine Learning Studio (classic). The goal was to build a robust predictive model for classification tasks using a decision tree algorithm. The project involved several key steps, including data preprocessing, model training, and performance evaluation.

Key Features

• Data Preprocessing: Imported the dataset (`Restaurant data.csv`) and performed data cleaning to handle missing values. Selected relevant columns to optimize the model's performance.
• Model Building: Implemented a decision tree algorithm, specifically a Multiclass Decision Forest. Split the dataset into training and testing sets to ensure the model's validity.
• Model Training and Fine-tuning: Trained the decision tree model using the training dataset. Fine-tuned the model parameters to achieve optimal performance.
• Model Evaluation: Scored the trained model using the testing dataset. Evaluated the model's performance to ensure accuracy and reliability.

Technologies Used

• Azure Machine Learning Studio (classic)
• Decision Tree Algorithm (Multiclass Decision Forest)
• Data preprocessing and cleaning techniques

Outcome

The project successfully demonstrated the ability to build, fine-tune, and evaluate a decision tree model using Azure ML cloud services. The final model showed high accuracy and reliability in predicting the target outcomes.