Unsupervised Learning - Kmeans Week 11 Challenge
Question 1: How does similarity is calculated if data is categorical in nature?
Answer: The Similarity Measure is a way of measuring how data samples are related or close to each other.
There are Several ways to find the similarity in the categorical in nature. They are mentioned below as,
1. Euclidean.
2. Manhattan.
3.Chebychev.
4. Minkowski.
5. Cosine.
6. Pearson.
7. Mahalanobis.
8. SED.
9. Jaccard.
10. Levenshtein.
11. Sorenson-Dice.
12. Jensen-Shannon.
13. Canberra.
14. Hamming.
15. Spearman.
16.Chi-Square.(X^2=(Observed frequency-expected frequency)^2/Expected frequency)
so by any of the above method we will find the similarity for the categorical data.
Question 2: Implement K-means for “Car dataset” and come up with the business insights?
Answer:
from os import execl
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
df=pd.read_csv("auto-mpg_kaggle.csv")
print(df)
print(df.describe())
def minmax(col):
for i in range(0,len(col)):
new_value=(float(col[i])-float(min(col)))/(float(max(col))-float(min(col)))
ex.append(new_value)
ex=[]
minmax(df.cylinders)
df["cylinders_minmax"]=ex
ex=[]
minmax(df.displacement)
df["displacement_minmax"]=ex
ex=[]
minmax(df.horsepower)
df["horsepower_minmax"]=ex
ex=[]
minmax(df.weight)
df["weight_minmax"]=ex
ex=[]
minmax(df.acceleration)
df["acceleration_minmax"]=ex
ex=[]
minmax(df.mpg)
df["mpg_minmax"]=ex
print(df)
km = KMeans(n_clusters=2)
print(km)
Y_new=km.fit_predict(df[["cylinders_minmax","displacement_minmax","horsepower_minmax","weight_minmax","acceleration_minmax","mpg_minmax",]])
print(Y_new)
df["cluster_minmax"]=Y_new
print(df)
df0=df[df.cluster_minmax==0]
df1=df[df.cluster_minmax==1]
plt.scatter(df0.acceleration_minmax,df0["mpg_minmax"],color="red")
plt.scatter(df1.acceleration_minmax,df1["mpg_minmax"],color="blue")
print(km.cluster_centers_)
print(km.algorithm)
plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color="purple",marker="*")
plt.legend()
plt.show()
k_range=range(1,10)
sse=[]
for k in k_range:
km=KMeans(n_clusters=k)
km.fit(df[["cylinders_minmax","displacement_minmax","horsepower_minmax","weight_minmax","acceleration_minmax","mpg_minmax"]])
sse.append(km.inertia_)
print(sse)
plt.plot(k_range,sse)
plt.show()
Result:
mpg cylinders displacement horsepower weight acceleration \
0 18.0 8 307.0 130 3504 12.0
1 15.0 8 350.0 165 3693 11.5
2 18.0 8 318.0 150 3436 11.0
3 16.0 8 304.0 150 3433 12.0
4 17.0 8 302.0 140 3449 10.5
.. ... ... ... ... ... ...
393 27.0 4 140.0 86 2790 15.6
394 44.0 4 97.0 52 2130 24.6
395 32.0 4 135.0 84 2295 11.6
396 28.0 4 120.0 79 2625 18.6
397 31.0 4 119.0 82 2720 19.4
model year origin car name
0 70 1 chevrolet chevelle malibu
1 70 1 buick skylark 320
2 70 1 plymouth satellite
3 70 1 amc rebel sst
4 70 1 ford torino
.. ... ... ...
393 82 1 ford mustang gl
394 82 2 vw pickup
395 82 1 dodge rampage
396 82 1 ford ranger
397 82 1 chevy s-10
[398 rows x 9 columns]
mpg cylinders displacement horsepower weight \
count 398.000000 398.000000 398.000000 398.000000 398.000000
mean 23.514573 5.454774 193.425879 104.185930 2970.424623
std 7.815984 1.701004 104.269838 38.292729 846.841774
min 9.000000 3.000000 68.000000 46.000000 1613.000000
25% 17.500000 4.000000 104.250000 75.000000 2223.750000
50% 23.000000 4.000000 148.500000 93.500000 2803.500000
75% 29.000000 8.000000 262.000000 125.000000 3608.000000
max 46.600000 8.000000 455.000000 230.000000 5140.000000
acceleration model year origin
count 398.000000 398.000000 398.000000
mean 15.568090 76.010050 1.572864
std 2.757689 3.697627 0.802055
min 8.000000 70.000000 1.000000
25% 13.825000 73.000000 1.000000
50% 15.500000 76.000000 1.000000
75% 17.175000 79.000000 2.000000
max 24.800000 82.000000 3.000000
No handles with labels found to put in legend.
mpg cylinders displacement horsepower weight acceleration \
0 18.0 8 307.0 130 3504 12.0
1 15.0 8 350.0 165 3693 11.5
2 18.0 8 318.0 150 3436 11.0
3 16.0 8 304.0 150 3433 12.0
4 17.0 8 302.0 140 3449 10.5
.. ... ... ... ... ... ...
393 27.0 4 140.0 86 2790 15.6
394 44.0 4 97.0 52 2130 24.6
395 32.0 4 135.0 84 2295 11.6
396 28.0 4 120.0 79 2625 18.6
397 31.0 4 119.0 82 2720 19.4
model year origin car name cylinders_minmax \
0 70 1 chevrolet chevelle malibu 1.0
1 70 1 buick skylark 320 1.0
2 70 1 plymouth satellite 1.0
3 70 1 amc rebel sst 1.0
4 70 1 ford torino 1.0
.. ... ... ... ...
393 82 1 ford mustang gl 0.2
394 82 2 vw pickup 0.2
395 82 1 dodge rampage 0.2
396 82 1 ford ranger 0.2
397 82 1 chevy s-10 0.2
displacement_minmax horsepower_minmax weight_minmax \
0 0.617571 0.456522 0.536150
1 0.728682 0.646739 0.589736
2 0.645995 0.565217 0.516870
3 0.609819 0.565217 0.516019
4 0.604651 0.510870 0.520556
.. ... ... ...
393 0.186047 0.217391 0.333711
394 0.074935 0.032609 0.146583
395 0.173127 0.206522 0.193365
396 0.134367 0.179348 0.286929
397 0.131783 0.195652 0.313864
acceleration_minmax mpg_minmax
0 0.238095 0.239362
1 0.208333 0.159574
2 0.178571 0.239362
3 0.238095 0.186170
4 0.148810 0.212766
.. ... ...
393 0.452381 0.478723
394 0.988095 0.930851
395 0.214286 0.611702
396 0.630952 0.505319
397 0.678571 0.585106
[398 rows x 15 columns]
KMeans(n_clusters=2)
[1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1
1 1 1 1 1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 1 1
1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0 0
0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0
0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 1 0 0 1 1 1 1 1 0 0 0 0 0 1
1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 1 1 1
0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0
0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0]
mpg cylinders displacement horsepower weight acceleration \
0 18.0 8 307.0 130 3504 12.0
1 15.0 8 350.0 165 3693 11.5
2 18.0 8 318.0 150 3436 11.0
3 16.0 8 304.0 150 3433 12.0
4 17.0 8 302.0 140 3449 10.5
.. ... ... ... ... ... ...
393 27.0 4 140.0 86 2790 15.6
394 44.0 4 97.0 52 2130 24.6
395 32.0 4 135.0 84 2295 11.6
396 28.0 4 120.0 79 2625 18.6
397 31.0 4 119.0 82 2720 19.4
model year origin car name cylinders_minmax \
0 70 1 chevrolet chevelle malibu 1.0
1 70 1 buick skylark 320 1.0
2 70 1 plymouth satellite 1.0
3 70 1 amc rebel sst 1.0
4 70 1 ford torino 1.0
.. ... ... ... ...
393 82 1 ford mustang gl 0.2
394 82 2 vw pickup 0.2
395 82 1 dodge rampage 0.2
396 82 1 ford ranger 0.2
397 82 1 chevy s-10 0.2
displacement_minmax horsepower_minmax weight_minmax \
0 0.617571 0.456522 0.536150
1 0.728682 0.646739 0.589736
2 0.645995 0.565217 0.516870
3 0.609819 0.565217 0.516019
4 0.604651 0.510870 0.520556
.. ... ... ...
393 0.186047 0.217391 0.333711
394 0.074935 0.032609 0.146583
395 0.173127 0.206522 0.193365
396 0.134367 0.179348 0.286929
397 0.131783 0.195652 0.313864
acceleration_minmax mpg_minmax cluster_minmax
0 0.238095 0.239362 1
1 0.208333 0.159574 1
2 0.178571 0.239362 1
3 0.238095 0.186170 1
4 0.148810 0.212766 1
.. ... ... ...
393 0.452381 0.478723 0
394 0.988095 0.930851 0
395 0.214286 0.611702 0
396 0.630952 0.505319 0
397 0.678571 0.585106 0
[398 rows x 16 columns]
[[0.2460251 0.13048014 0.19142259 0.21970739 0.50206714 0.51599083]
[0.8591195 0.61513334 0.50382827 0.63312488 0.372941 0.19066974]]
auto
[142.69975011779007, 47.04873541164898, 25.14455169702185, 20.31536442327276, 17.400459716185562, 15.600400938593374, 14.290559840638814, 13.106351473577453, 12.281248425880117]
