Predicting using a dataset

Without much talking, let's take a look at the following code:

import numpy as np
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier

knn = KNeighborsClassifier(n_neighbors=5)
data = pd.read_csv('dataset.csv')

x = np.array(data[['Time', 'Temp']])
y = np.array(data[['State']]).ravel()

knn.fit(x,y)

time = raw_input("Enter time")
temp = raw_input("Enter temp")

data =. []

data.append(float(time))
data.append(float(temp))

a = knn.predict([data])

print(a[0])}

So, let's see what we are doing here:

import numpy as np

We are importing numpy to our program; this helps us handle lists and matrices:

import pandas as pd

Here, we are importing a library named pandas; this helps us read files in comma-separated values or in other words, CSV files. We will be using CSV files to store our data and access it for learning process:

from sklearn.neighbors import KNeighborsClassifier

Here, we are importing KneighborsClassifier from the library sklearn. sklearn itself...