NOTE

Original files for the project exceed GitHub's maximum file size limit. Please contact me at asaficontact@gmail.com to access complete code and data files for the project.

Initial Setup

#Do all necessary Pacakge Imports
from imports import *

#Create a dataHandler class for our dataframe 
data = dh()

#Get Data for crisis in asia and southern asia
southern_asia_df = data.filter_data(region=['Southern Asia', 'South-Eastern Asia'])
asia_df = data.filter_data(region=['Middle East', 'Southern Asia', 'South-Eastern Asia'])

#Get Data for crisis in asia and southern asia caused by rebel groups
asia_rebel_groups_df = data.filter_data(region=['Middle East', 'Southern Asia', 'South-Eastern Asia'], rebel_groups=True)
southern_asia_rebel_groups_df = data.filter_data(region=['Southern Asia'], rebel_groups=True)

Predictors of Interest

1: Fatalities

2: Event Type

3: Sub Event Type

4: Associate Actor 1

5: Actor 2

6: Inter 2

7: Interaction

8: Region

9: Admin 1

10: Admin 2

11: Admin 3

12: Location

13: Associate Actor 2

14: Country

NOTE: To better understand the predictors look at documentation in Understanding_data folder.

Types of Actors involved in Crisis Dataset

1: State Forces

2: Rebel Groups

3: Political Militias

4: Identity Militias

5: Rioters

6: Protestors

7: Civilian

8: External/Other Forces

Crisis Data Initial Exploration for Asia

Crisis to Fatalities per Country caused by All Actors[2017-2019]

data.crisis_to_fatalities_graph(asia_df, 'country_paired')

Number of Crisis caused by all Actors per Country[2017-2019]

data.crisis_to_fatalities_graph(asia_df, type='crisis map')

Crisis to Fatalities per Country caused by Rebel Groups[2017-2019]

data.crisis_to_fatalities_graph(asia_rebel_groups_df, 'country_paired')

Number of Crisis caused by Rebel Groups per Country[2017-2019]

data.crisis_to_fatalities_graph(asia_rebel_groups_df, type='crisis map')

Crisis to Fatalities per Event Type caused by All Actors[2017-2019]

data.crisis_to_fatalities_graph(asia_df, type='event_paired')

Crisis to Fatalities per Country caused by Rebel Groups[2017-2019]

data.crisis_to_fatalities_graph(asia_rebel_groups_df, type='event_paired')

Random Forest Model for Classifying Rebel Groups in Afghanistan

Creating best random forest for Afghanistan by Grid Search

#Get Data for crisis in Afghanistan caused by rebel groups
afg_rebel_groups_df = data.filter_data(country=['Afghanistan'], rebel_groups=True)
#Get Random Forest [the best parameters have been found using grid search]
afg_rebel_groups_rf = data.random_forest(afg_rebel_groups_df, max_depth= 30, min_samples_leaf=1, min_samples_split = 10, n_estimators=500, model_name = 'afghanistan')

Load best Random Forest model based of grid search for Afghanistan

asia_rebel_groups_rf = data.load_model('Afghanistan')

Confusion Matrix for Training Data [Normalized]

data.plot_confusion_matrix(afg_rebel_groups_df, afg_rebel_groups_rf, dataset_type = 'train', normalize = True)

<matplotlib.axes._subplots.AxesSubplot at 0x7fab923ebe48>

Confusion Matrix for Testing Data [Normalized]

data.plot_confusion_matrix(afg_rebel_groups_df, afg_rebel_groups_rf, dataset_type = 'test', normalize = True)

<matplotlib.axes._subplots.AxesSubplot at 0x7fab444f72b0>

Tests to check accuracy of the model

data.classifier_accuracy(afg_rebel_groups_df, afg_rebel_groups_rf)

{'training_accuracy': 0.8340914013428633,
 'testing_accuracy': 0.8232618583495777,
 'training_F1_weighted': 0.7805860368800424,
 'testing_F1_weighted': 0.7674833570687929,
 'training_precision_weighted': 0.8515754941098571,
 'testing_precision_weighted': 0.8386940263095153,
 'training_recall_weighted': 0.8340914013428633,
 'testing_recall_weighted': 0.8232618583495777}

Random Forest Model for Classifying Rebel Groups in Southern Asia

Load best Random Forest model based of grid search for Southern Asia

southern_asia_rebel_groups_rf = data.load_model('southern_asia')

Confusion Matrix for Training Data [Normalized]

data.plot_confusion_matrix(southern_asia_rebel_groups_df, southern_asia_rebel_groups_rf, dataset_type = 'train', normalize = True)

<matplotlib.axes._subplots.AxesSubplot at 0x7fc528549f28>

Confusion Matrix for Testing Data [Normalized]

data.plot_confusion_matrix(southern_asia_rebel_groups_df, southern_asia_rebel_groups_rf, dataset_type = 'test', normalize = True)

<matplotlib.axes._subplots.AxesSubplot at 0x7fc528549ac8>

Tests to check accuracy of the model

data.classifier_accuracy(southern_asia_rebel_groups_df, southern_asia_rebel_groups_rf)

{'training_accuracy': 0.8423524022218405,
 'testing_accuracy': 0.8268338773406632,
 'training_F1_weighted': 0.7898185645638188,
 'testing_F1_weighted': 0.7707830293459663,
 'training_precision_weighted': 0.8712670574510711,
 'testing_precision_weighted': 0.8306234964296919,
 'training_recall_weighted': 0.8423524022218405,
 'testing_recall_weighted': 0.8268338773406632}

Random Forest Model for Classifying Rebel Groups in Asia

Load best Random Forest model based of grid search for Asia

asia_rebel_groups_rf = data.load_model('asia')

Confusion Matrix for Training Data [Normalized]

data.plot_confusion_matrix(asia_rebel_groups_df, asia_rebel_groups_rf, dataset_type = 'train', normalize = True)

<matplotlib.axes._subplots.AxesSubplot at 0x7fc539b6a5c0>

Confusion Matrix for Testing Data [Normalized]

data.plot_confusion_matrix(asia_rebel_groups_df, asia_rebel_groups_rf, dataset_type = 'test', normalize = True)

<matplotlib.axes._subplots.AxesSubplot at 0x7fc549f98b00>

Tests to check accuracy of the model

data.classifier_accuracy(asia_rebel_groups_df, asia_rebel_groups_rf)

{'training_accuracy': 0.853942423468457,
 'testing_accuracy': 0.84090411558669,
 'training_F1_weighted': 0.8372359852046187,
 'testing_F1_weighted': 0.8226387702490333,
 'training_precision_weighted': 0.8441646669875207,
 'testing_precision_weighted': 0.8279729962978163,
 'training_recall_weighted': 0.853942423468457,
 'testing_recall_weighted': 0.84090411558669}

Random Forest Model for Classifying Islamic State (Type of Rebel Group) in Asia

Create Dataset for Islamic State classification

isis_df = data.filter_data(region = ['Southern Asia', 'Middle East', 'South-Eastern Asia'], rebel_groups= True, actor_name= 'islamic state')

Load best Random Forest model based of grid search for ISIS activity in Asia

isis_rf = data.load_model('isis')

Confusion Matrix for Training Data [Normalized]

data.plot_confusion_matrix(isis_df, isis_rf, dataset_type = 'train', normalize = True)

<matplotlib.axes._subplots.AxesSubplot at 0x7fe8183ffe48>

Confusion Matrix for Testing Data [Normalized]

data.plot_confusion_matrix(isis_df, isis_rf, dataset_type = 'test', normalize = True)

<matplotlib.axes._subplots.AxesSubplot at 0x7fe85d373588>

Tests to check accuracy of the model

data.classifier_accuracy(isis_df, isis_rf)

{'training_accuracy': 0.925958500276882,
 'testing_accuracy': 0.9200664484291787,
 'training_F1_weighted': 0.9238511827444541,
 'testing_F1_weighted': 0.9177113406155695,
 'training_precision_weighted': 0.9330340541392627,
 'testing_precision_weighted': 0.9277676133005858,
 'training_recall_weighted': 0.925958500276882,
 'testing_recall_weighted': 0.9200664484291787}