How to wisely choose machine algorithm for the given data set?
I used a simple Neural Network with 3 Inputs - 1 hidden layer with 8 neurons - 3 Outputs (Labels) with k-fold crossvalidation in keras for your data set.

import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils import np_utils
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder, scale
from sklearn.pipeline import Pipeline

path = r"User\train.csv"
data = pd.read_csv(path)

data.location.value_counts()
dataset = data.values

x = dataset[:,0:3]
x_scaled = scale(x)
y = dataset[:,3]


encoder = LabelEncoder()
encoder.fit(y)
encoded_Y = encoder.transform(y)

dummy_y = np_utils.to_categorical(encoded_Y)

def model():
    model = Sequential()
    model.add(Dense(8, input_dim=3, activation='relu'))
    model.add(Dense(3, activation='softmax'))

    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

    return model



estimator = KerasClassifier(build_fn=model, epochs=200, batch_size=5, verbose=0)
kfold = KFold(n_splits=10, shuffle=True)

results = cross_val_score(estimator, x_scaled, dummy_y, cv=kfold)
print("Model Prediction: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))


The used Model can categorize around 94% of the data correctly. Maybe this helps for the start.

Out:
Prediction: 93.95% (3.13%)


In data science and statistics in general you have to try and train several models to see which suits your dataset best (model benchmarking). In the end it is often a trade-off between computational costs and accuracy.