Add an analysis script

code/classification_analysis.py

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+import argparse
+import pandas as pd
+import seaborn as sns
+from sklearn import model_selection
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.metrics import classification_report
+
+parser = argparse.ArgumentParser(description="Analyze iris data")
+parser.add_argument('data', help="Input data (CSV) to process")
+parser.add_argument('output_figure', help="Output figure path")
+parser.add_argument('output_report', help="Output report path")
+args = parser.parse_args()
+
+# prepare the data as a pandas dataframe
+df = pd.read_csv(args.data)
+attributes = ["sepal_length", "sepal_width", "petal_length","petal_width", "class"]
+df.columns = attributes
+
+# create a pairplot to plot pairwise relationships in the dataset
+plot = sns.pairplot(df, hue='class', palette='muted')
+plot.savefig(args.output_figure)
+
+# perform a K-nearest-neighbours classification with scikit-learn
+
+# Step 1: split data in test and training dataset (20:80)
+array = df.values
+X = array[:,0:4]
+Y = array[:,4]
+test_size = 0.20
+seed = 7
+X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y,
+                                                                    test_size=test_size,
+                                                                    random_state=seed)
+
+# Step 2: Fit the model and make predictions on the test dataset
+knn = KNeighborsClassifier()
+knn.fit(X_train, Y_train)
+predictions = knn.predict(X_test)
+
+# Step 3: Save the classification report
+report = classification_report(Y_test, predictions, output_dict=True)
+df_report = pd.DataFrame(report).transpose().to_csv(args.output_report)