#exercise KNN Algorithm using Python
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

df= pd.read_csv('o_m.csv')
df.head()

	weight	donut	result
0	20.409116	3	malnourished
1	22.551343	9	malnourished
2	63.816049	18	obese
3	57.820181	11	obese
4	49.767124	10	obese

df['result'].value_counts()

	count
result
obese	15
malnourished	8

dtype: int64

#exploratory analysis
sns.jointplot(data=df, x= 'donut', y='weight', kind= 'reg')
plt.show()

plt.figure(figsize=(5,4))
sns.scatterplot(data=df, x='donut', y='weight', hue='result',
                s=50, palette='hls')
plt.show()

#model building

train_x= df.iloc[:, 0:2].values
train_x
train_y= df.iloc[:, -1].values
train_y

array(['malnourished', 'malnourished', 'obese', 'obese', 'obese', 'obese',
       'obese', 'malnourished', 'malnourished', 'malnourished', 'obese',
       'obese', 'obese', 'obese', 'obese', 'obese', 'obese',
       'malnourished', 'malnourished', 'obese', 'obese', 'malnourished',
       'obese'], dtype=object)

#model
model= KNeighborsClassifier(n_neighbors=5)

#train the model
model.fit(train_x, train_y)

KNeighborsClassifier()

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#new samples
mickey1 = [[40, 20]]
mickey2= [[4,2]]

prediction1 = model.predict(mickey1)
prediction2= model.predict(mickey2)
print(prediction1, prediction2)

['obese'] ['malnourished']

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