Please consider the following:
1) Random forest algorithm can be used for both classifications and regression task.
2) It typically provides very high accuracy.
3) Random forest classifier will handle the missing values and maintain the accuracy of a large proportion of data.
4) If there are more trees, it usually won’t allow overfitting trees in the model.
5) It has the power to handle a large data set with high dimensionality
Ultimately, what algo you choose to work with is up to you. you definitely want the predictive capabilities of the algo to be pretty high (over 90%). Sometimes other algos beat the RF algo, but I have found that often the RF is quite good! Usually, I start with RF, and if I see decent performance, I am done. I believe, in around at least 80% of the time, I'm done. If you are not getting good results from the RF algo, test some others.
This gives a nice comparison of a few different algos.
import matplotlib.pyplot as plt
from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
# load dataset
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = pandas.read_csv(url, names=names)
array = dataframe.values
X = array[:,0:8]
Y = array[:,8]
# prepare configuration for cross validation test harness
seed = 7
# prepare models
models = 
# evaluate each model in turn
results = 
names = 
scoring = 'accuracy'
for name, model in models:
kfold = model_selection.KFold(n_splits=10, random_state=seed)
cv_results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)
msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
# boxplot algorithm comparison
fig = plt.figure()
ax = fig.add_subplot(111)
Reference: How To Compare Machine Learning Algorithms in Python with scikit-learn