Source code for: https://kasperfred.com/posts/creating-your-first-machine-learning-classification-model-in-sklearn/

classifier.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

plt.style.use('ggplot') # make plots look better


#### import the data ####
df = pd.read_csv("iris.csv")

print (df.head())
print (df.describe())


#### Manual Feature selection ####
sns.FacetGrid(df, 
	hue="Species").map(plt.scatter, "PetalLengthCm", "PetalWidthCm").add_legend()

plt.show() # expect to see three defined clusters
# it's seen that the petal features seem to describe the species the best


#### prepare data for sklearn ####
# drop irreleveant coloums
df_feature_selected = df.drop(['SepalLengthCm', 'SepalWidthCm', "Id", "Species"], axis=1)

# create and encode labels
labels = np.asarray(df.Species)

from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(labels)

labels = le.transform(labels) 

# create features using DictVectorizer, and pandas's to_dict method
df_features = df_feature_selected.to_dict( orient = 'records' )

from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer()
features = vec.fit_transform(df_features).toarray()


##### split up in test and training data ####
from sklearn.model_selection import train_test_split

features_train, features_test, labels_train, labels_test = train_test_split(
	features, labels, 
	test_size=0.33, random_state=42)


#### Fit to random forests ####

# Random Forests Classifier
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(
	min_samples_split=4,
	criterion="entropy"
	)

# Support Vector Classifier
"""
from sklearn.svm import SVC
clf = SVC()
"""

clf.fit(features_train, labels_train)

# find the accuracy of the model
acc_test = clf.score(features_test, labels_test)
acc_train = clf.score(features_train, labels_train)
print ("Train Accuracy:", acc_train)
print ("Test Accuracy:", acc_test)


# compute predictions on test features
pred = clf.predict(features_test)

# predict our new unique iris flower
flower = [5.2,0.9]
class_code = clf.predict(flower) # [1]

decoded_class = le.inverse_transform(class_code)
print (decoded_class) # ['Iris-versicolor']


#### Figure out what kind of mistakes it makes ####
from sklearn.metrics import recall_score, precision_score

precision = precision_score(labels_test, pred, average="weighted")
recall = recall_score(labels_test, pred, average="weighted")

print ("Precision:", precision)
print ("Recall:", recall)