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microsoft_malware_prediction_kaggle_2nd

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This repository contains the winning solution (2nd place) of the Macrosoft Maleware Prediction Challenge on Kaggle.

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# Microsoft Maleware Prediction on Kaggle

This repository contains the winning solution (2nd place) of the Macrosoft Maleware Prediction Challenge on Kaggle. For details on our approach, see the [overview of our soultion](https://www.kaggle.com/c/microsoft-malware-prediction/discussion/84065).

Link to the competition homepage: https://www.kaggle.com/c/microsoft-malware-prediction

## Team:
+ Stephan Michaels
+ Florian Imorde

## Competition Description:
The competition "Microsoft Maleware Prediction" was based on the questions whether or not a computer is infected by maleware. Based on different properties and features povided by Microsofts Windwos Defender, an algorithm had to be created, which predicts the probability of such an infection.

## Soution Description:

For solving the maleware prediction problem, two models were trained:
* Model 1 (later called M1): The data set was cleaned and string values encoded. Afterwards a LightGBM was trained. 
* Model 2 (later called M2): The preprocessed data from model 1 was extended with new features. Next, important features were selected and a LightGBM was trained.

Finally an average of the predictions of both models was calculated.

## Archive Contents
* code
	- 1_Data_Cleaning_train_set.ipynb 
	*Cleaning the train set.*
	- 2_Data_Cleaning_test_set.ipynb 
	*Cleaning the test set.* 
	- 3_Data_Encoding_M1.ipynb 
	*Encode the train - and test data for model 1.* 
	- 4_Submission_M1.ipynb 
	*Building model 1.* 
	- 5_Feature_Engineering_M2.ipynb 
	*Creating new features for model 2.* 
	- 6_Data_Encoding_M2.ipynb 
	*Encode the train - and test data for model 2.* 
	- 7_Submission_M2.ipynb 
	*Building model 2.* 
	- 8_Submission_Solution.ipynb 
	*Building the final solution by averaging the solutions from model 1 and model 2.* 
	- Optional_Feature_Selection_M2.ipynb 
	*Selection the most importend features for building up model 2.* 
	- Optional_Submission_Simple_Model 
	*Building a simplified model.*
* data
	- Data_Description.xlsx 
	*Feature informations: Relevant for the future, type, description.*
	- encoding_dictionary.p 
	*Dictionary, which contains the encoder for relabeling values of features.*
* feature_importance
	- Featureimportance_M1.csv 
	*List of all features used in model 1 with corresponding importance.*
	- Featureimportance_Feature_Selection_M2.csv 
	*List of all features in model 2 after Feature Engineering with corresponding importance.*
	- Featureimportance_M2.csv 
	*List of all features used in model 2 with corresponding importance.*
	- FeatureImportance_Simple.csv 
	*List of all features used in the simplified model.*
* models
	- Placeholder 
	*Models are saved here.*
* submissions
	- Placeholder 
	*Final submissions are stored here.*

## Data
The original train- and testdata can be downloaded form the competition homepage.

Link to the data:https://www.kaggle.com/c/microsoft-malware-prediction/data

The datasets have to be stored in the data folder.

## Hardware:
Notebook with:
* Intel(R) Core(TM) i7-8850H
* 16GB RAM

## Software
* Windows 10 Pro, 64 Bit (Version: 1809)
* Anaconda 1.9.6
* Python 3.7.1
* Jupyter Notebook 5.7.4

## Libraries
The following libraries are required:
* numpy (Version 1.15.4)
* pandas (Version 0.23.4)
* dask (Version 1.0.0)
* scikit-learn (Version 0.20.1 )
* tqdm (Version 4.28.1)
* lightgbm (Version 2.2.1)
* pickle (Version 4.0)

## Licence
Out code is submitted under [MIT license](https://opensource.org/licenses/MIT).

microsoft_malware_prediction_kaggle_2nd

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