From 83b32cbea7a73a235c27d1bf5df592386c733951 Mon Sep 17 00:00:00 2001
From: Agata <agakul1@st.amu.edu.pl>
Date: Sun, 10 Apr 2022 10:57:44 +0200
Subject: [PATCH] Convert preparation.ipynb file to python script

---
 preparation.py | 104 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 104 insertions(+)
 create mode 100644 preparation.py

diff --git a/preparation.py b/preparation.py
new file mode 100644
index 0000000..66c0236
--- /dev/null
+++ b/preparation.py
@@ -0,0 +1,104 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[ ]:
+
+
+# get_ipython().system('kaggle datasets download -d tejashvi14/travel-insurance-prediction-data')
+
+
+# In[ ]:
+
+
+get_ipython().system('unzip -o travel-insurance-prediction-data.zip')
+
+
+# In[5]:
+
+
+import pandas as pd
+travel_insurance=pd.read_csv('TravelInsurancePrediction.csv', index_col=0)
+travel_insurance
+
+
+# In[ ]:
+
+
+# usunięcie wierszy zawierających braki
+travel_insurance.dropna(axis='index', how='any')
+
+
+# In[6]:
+
+
+# normalizacja danych
+for column in travel_insurance.columns:
+    if travel_insurance[column].dtype == 'object':
+        travel_insurance[column] = travel_insurance[column].str.lower()
+
+travel_insurance
+
+
+# In[8]:
+
+
+# podział na podzbiory train/dev/test
+import sklearn
+from sklearn.model_selection import train_test_split
+travel_insurance_train, travel_insurance_rest = sklearn.model_selection.train_test_split(travel_insurance, test_size=0.4, random_state=1)
+travel_insurance_test, travel_insurance_dev = sklearn.model_selection.train_test_split(travel_insurance_rest, test_size=0.5, random_state=1)
+
+
+# In[27]:
+
+
+travel_insurance.describe(include='all')
+
+
+# In[23]:
+
+
+# zwracanie informacji o danym zbiorze 
+
+import seaborn as sns
+
+def printInformation(data):
+    print(f'Size (rows): {len(data)}\n')
+    mean_value = data.mean()
+    min_value = data.min(numeric_only=True)
+    max_value = data.max(numeric_only=True)
+    std_value = data.std()
+    median_value = data.median()
+    print(f'(mean)\n{mean_value}', f'(min)\n{min_value}', f'(max)\n{max_value}', f'(std)\n{std_value}', f'(median)\n{median_value}', sep="\n\n")
+    sns.pairplot(data=data, hue="TravelInsurance")
+
+
+# In[24]:
+
+
+printInformation(travel_insurance)
+
+
+# In[11]:
+
+
+printInformation(travel_insurance_train)
+
+
+# In[12]:
+
+
+printInformation(travel_insurance_test)
+
+
+# In[13]:
+
+
+printInformation(travel_insurance_dev)
+
+
+# In[ ]:
+
+
+
+