Trees.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
df = pd.read_csv('SurveyLargeDataSet.csv')
df.head()
df.info()
from sklearn.cross_validation import train_test_split
X = df.drop('q89',axis=1)
y = df['q89']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)

#Model generation with Tress
from sklearn.tree import DecisionTreeClassifier
dtree = DecisionTreeClassifier()
dtree.fit(X_train,y_train)
predictions = dtree.predict(X_test)
from sklearn.metrics import classification_report,confusion_matrix
print(confusion_matrix(y_test,predictions))
print('\n')
print(classification_report(y_test,predictions))

#Another Model generation that includes random forest (Is know for being more accurate)
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier(n_estimators=200)
rfc.fit(X_train,y_train)
rfc_pred = rfc.predict(X_test)
print(confusion_matrix(y_test,rfc_pred))
print('\n')
print(classification_report(y_test,rfc_pred))