Microsoft Malware Prediction

1. Problem Statement

As a skilled data scientist at Microsoft, you play a critical role in protecting millions of Windows users from the ever-evolving threat of malware. The malware landscape is constantly changing, with new viruses and attack vectors emerging every day. To stay ahead of these threats, Microsoft needs to leverage advanced machine learning techniques to identify and neutralize malware before it can cause harm. You are working in Microsoft's defense, and now you must prepare for the future of coding.

You are given a massive and complex dataset containing telemetry data from Windows machines worldwide. Your task is to build a robust and accurate classification model that can predict the likelihood of a machine being infected with malware based on its configuration, usage patterns, and other relevant factors. This model will be integrated into Microsoft's endpoint protection solution, Windows Defender, to proactively identify and block malware attacks, safeguarding the security and privacy of millions of users around the globe. With your skills, you will continue to protect their millions of users against any attacks from all corners.

Goal: The goal of this project is to build a model that can predict whether a Windows machine will be infected with malware.

2. Data Description

The dataset contains telemetry data and machine properties, used to predict the probability of a machine getting infected by malware.

Data Source: Kaggle Microsoft Malware Prediction

Download Data

3. Your Task

Your task is to build a classification model to predict malware infection.

  1. Data Exploration and Preprocessing:
    • Load the dataset using Pandas.
    • Explore the data to understand the distribution of features.
    • Handle missing values - be aware there are many!
    • Consider techniques for dimensionality reduction due to the high number of features (e.g., PCA, feature selection).
    • Convert categorical variables to numerical format using techniques like one-hot encoding.
  2. Feature Engineering (Crucial):
    • Create new features that may improve model performance - be creative!
  3. Model Building:
    • Split the data into training and validation sets. Given the size of the dataset, consider using techniques like stratified sampling.
    • Choose a suitable classification algorithm (e.g., Logistic Regression, Random Forest, Gradient Boosting). Consider using algorithms that are designed for large datasets.
    • Train the model on the training data.
  4. Model Evaluation:
    • Evaluate the model's performance on the validation data.
    • Use metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Pay careful attention to the evaluation metrics - consider the problem as potentially imbalanced.
  5. Prediction and Submission:
    • Make predictions on the test data.

Python Libraries: Pandas, NumPy, scikit-learn, Matplotlib/Seaborn, (potentially) XGBoost/LightGBM due to the size and complexity of the data.