Data-Science-using-Azure-GD-Goenka

Day1

Explore Automated Machine Learning in Azure Machine Learning

Classification Model using Azure Machine Learning Designer

Day2

Python for beginners

Data Exploration and Analysis with Python -numpy, pandas and matplotlib

Importing Data to Azure

Day3

Classification Algorithms

Evaluation Metrics

Run a training script as a command job in Azure Machine Learning

Track model training with MLflow in jobs

Perform hyperparameter tuning with Azure Machine Learning

Day4

Assessment

Packet Flood Detection in Switching Network using Azure Automated Machine Learning and AzureML Designer Problem Statement Packet flood detection in switching networks can be accomplished using machine learning algorithms. The idea is to use machine learning to identify abnormal network behavior, such as a high volume of incoming packets from a single source. The machine learning algorithm can then determine if this behavior is indicative of a network attack, such as a packet flood. This can be achieved through training the algorithm on a dataset of normal network traffic, and then using the learned patterns to detect anomalies in real-time.

Expected Solution/Approach: 1.Data Collection and Dataset Preparation: The dataset may be downloaded from here

Data description is available here

Data Preparation: Perform the necessary data cleaning(if required)

Feature Selection: Select the required columns after doing an analysis.

Data Preprocessing: Perform any encoding of the variables to integers(if required) after analyzing the dataset

Split the Data: Split the data into a training set and a validation set.

Train the model: Select suitable ML algorithms and train the model

Evaluation Measures: Measures such as accuracy, mean recall score, and mean precision should be computed to evaluate the classifier's performance.

Deploy the model: Deploy the model for performing real-time inferencing.

Upload the solution here