#!/usr/bin/env python
# coding: utf-8

# # Welcome to Visual Python DEMO binder 🎉

# Get started using Visual Python by clicking the 🟧 **orange button** 🟧 on the right side!

# ### Visual Python can "Import" libraries easily,

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# Visual Python: Data Analysis > Import
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')
import seaborn as sns


# ### Load sample data with several clicks, (using "File" app)

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# Visual Python: Data Analysis > File
data = pd.read_csv('https://raw.githubusercontent.com/visualpython/visualpython/main/visualpython/data/sample_csv/iris.csv')
data


# ### "Subset" the dataframe with mutiple conditions,

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# Visual Python: Data Analysis > Subset
data.loc[(data['variety'] == 'Setosa')&(data['sepal.length'] < 5), ['variety','sepal.length','petal.length']]


# ### Draw a Plot with preview, (using "Seaborn" app)

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# Visual Python: Visualization > Seaborn
sns.lineplot(data=data)
plt.show()


# ### Split DataSet,

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# Visual Python: Machine Learning > Data Split
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(data[['sepal.length', 'sepal.width', 'petal.length', 'petal.width']], data['variety'])


# ### Fit and Predict using listed ML algorithms

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# Visual Python: Machine Learning > Classifier
from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier()


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# Visual Python: Machine Learning > Fit/Predict
model.fit(X_train, y_train)


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# Visual Python: Machine Learning > Fit/Predict
pred = model.predict(X_test)
pred


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# Visual Python: Machine Learning > Evaluation
from sklearn import metrics


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# Visual Python: Machine Learning > Evaluation
from sklearn import metrics


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# Visual Python: Machine Learning > Evaluation
# Confusion Matrix
pd.crosstab(y_test, pred, margins=True)


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# Visual Python: Machine Learning > Evaluation
# Classification report
print(metrics.classification_report(y_test, pred))


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