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Challenge

Project 2

VVinaysai Rangumudri2 May 2023

Machine Learning Fundamentals In Depth - Challenges Project 2

Implement Clustering and then predict the class of car from “Car dataset”.

In [55]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns 
from scipy.sparse import hstack
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn.cluster import KMeans, AgglomerativeClustering
from sklearn.manifold  import TSNE
from sklearn.metrics import mean_squared_error
​
In [56]:
 
df = pd.read_csv(r'/content/clean_auto.csv')
print(df)
     symboling  normalized-losses         make aspiration num-of-doors  \
0            3                122  alfa-romero        std          two   
1            3                122  alfa-romero        std          two   
2            1                122  alfa-romero        std          two   
3            2                164         audi        std         four   
4            2                164         audi        std         four   
..         ...                ...          ...        ...          ...   
196         -1                 95        volvo        std         four   
197         -1                 95        volvo      turbo         four   
198         -1                 95        volvo        std         four   
199         -1                 95        volvo      turbo         four   
200         -1                 95        volvo      turbo         four   

      body-style drive-wheels engine-location  wheel-base    length  ...  \
0    convertible          rwd           front        88.6  0.811148  ...   
1    convertible          rwd           front        88.6  0.811148  ...   
2      hatchback          rwd           front        94.5  0.822681  ...   
3          sedan          fwd           front        99.8  0.848630  ...   
4          sedan          4wd           front        99.4  0.848630  ...   
..           ...          ...             ...         ...       ...  ...   
196        sedan          rwd           front       109.1  0.907256  ...   
197        sedan          rwd           front       109.1  0.907256  ...   
198        sedan          rwd           front       109.1  0.907256  ...   
199        sedan          rwd           front       109.1  0.907256  ...   
200        sedan          rwd           front       109.1  0.907256  ...   

     compression-ratio  horsepower  peak-rpm city-mpg highway-mpg    price  \
0                  9.0       111.0    5000.0       21          27  13495.0   
1                  9.0       111.0    5000.0       21          27  16500.0   
2                  9.0       154.0    5000.0       19          26  16500.0   
3                 10.0       102.0    5500.0       24          30  13950.0   
4                  8.0       115.0    5500.0       18          22  17450.0   
..                 ...         ...       ...      ...         ...      ...   
196                9.5       114.0    5400.0       23          28  16845.0   
197                8.7       160.0    5300.0       19          25  19045.0   
198                8.8       134.0    5500.0       18          23  21485.0   
199               23.0       106.0    4800.0       26          27  22470.0   
200                9.5       114.0    5400.0       19          25  22625.0   

    city-L/100km  horsepower-binned  diesel  gas  
0      11.190476             Medium       0    1  
1      11.190476             Medium       0    1  
2      12.368421             Medium       0    1  
3       9.791667             Medium       0    1  
4      13.055556             Medium       0    1  
..           ...                ...     ...  ...  
196    10.217391             Medium       0    1  
197    12.368421               High       0    1  
198    13.055556             Medium       0    1  
199     9.038462             Medium       1    0  
200    12.368421             Medium       0    1  

[201 rows x 29 columns]
In [57]:
df.columns
Out[57]:
Index(['symboling', 'normalized-losses', 'make', 'aspiration', 'num-of-doors',
       'body-style', 'drive-wheels', 'engine-location', 'wheel-base', 'length',
       'width', 'height', 'curb-weight', 'engine-type', 'num-of-cylinders',
       'engine-size', 'fuel-system', 'bore', 'stroke', 'compression-ratio',
       'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg', 'price',
       'city-L/100km', 'horsepower-binned', 'diesel', 'gas'],
      dtype='object')
In [58]:
df.info()
​
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 201 entries, 0 to 200
Data columns (total 29 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   symboling          201 non-null    int64  
 1   normalized-losses  201 non-null    int64  
 2   make               201 non-null    object 
 3   aspiration         201 non-null    object 
 4   num-of-doors       201 non-null    object 
 5   body-style         201 non-null    object 
 6   drive-wheels       201 non-null    object 
 7   engine-location    201 non-null    object 
 8   wheel-base         201 non-null    float64
 9   length             201 non-null    float64
 10  width              201 non-null    float64
 11  height             201 non-null    float64
 12  curb-weight        201 non-null    int64  
 13  engine-type        201 non-null    object 
 14  num-of-cylinders   201 non-null    object 
 15  engine-size        201 non-null    int64  
 16  fuel-system        201 non-null    object 
 17  bore               201 non-null    float64
 18  stroke             197 non-null    float64
 19  compression-ratio  201 non-null    float64
 20  horsepower         201 non-null    float64
 21  peak-rpm           201 non-null    float64
 22  city-mpg           201 non-null    int64  
 23  highway-mpg        201 non-null    int64  
 24  price              201 non-null    float64
 25  city-L/100km       201 non-null    float64
 26  horsepower-binned  200 non-null    object 
 27  diesel             201 non-null    int64  
 28  gas                201 non-null    int64  
dtypes: float64(11), int64(8), object(10)
memory usage: 45.7+ KB
In [59]:
 
df.describe()
Out[59]:
symboling normalized-losses wheel-base length width height curb-weight engine-size bore stroke compression-ratio horsepower peak-rpm city-mpg highway-mpg price city-L/100km diesel gas
count 201.000000 201.00000 201.000000 201.000000 201.000000 201.000000 201.000000 201.000000 201.000000 197.000000 201.000000 201.000000 201.000000 201.000000 201.000000 201.000000 201.000000 201.000000 201.000000
mean 0.840796 122.00000 98.797015 0.837102 0.915126 53.766667 2555.666667 126.875622 3.330692 3.256904 10.164279 103.405534 5117.665368 25.179104 30.686567 13207.129353 9.944145 0.099502 0.900498
std 1.254802 31.99625 6.066366 0.059213 0.029187 2.447822 517.296727 41.546834 0.268072 0.319256 4.004965 37.365700 478.113805 6.423220 6.815150 7947.066342 2.534599 0.300083 0.300083
min -2.000000 65.00000 86.600000 0.678039 0.837500 47.800000 1488.000000 61.000000 2.540000 2.070000 7.000000 48.000000 4150.000000 13.000000 16.000000 5118.000000 4.795918 0.000000 0.000000
25% 0.000000 101.00000 94.500000 0.801538 0.890278 52.000000 2169.000000 98.000000 3.150000 3.110000 8.600000 70.000000 4800.000000 19.000000 25.000000 7775.000000 7.833333 0.000000 1.000000
50% 1.000000 122.00000 97.000000 0.832292 0.909722 54.100000 2414.000000 120.000000 3.310000 3.290000 9.000000 95.000000 5125.369458 24.000000 30.000000 10295.000000 9.791667 0.000000 1.000000
75% 2.000000 137.00000 102.400000 0.881788 0.925000 55.500000 2926.000000 141.000000 3.580000 3.410000 9.400000 116.000000 5500.000000 30.000000 34.000000 16500.000000 12.368421 0.000000 1.000000
max 3.000000 256.00000 120.900000 1.000000 1.000000 59.800000 4066.000000 326.000000 3.940000 4.170000 23.000000 262.000000 6600.000000 49.000000 54.000000 45400.000000 18.076923 1.000000 1.000000
In [60]:
 
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 201 entries, 0 to 200
Data columns (total 29 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   symboling          201 non-null    int64  
 1   normalized-losses  201 non-null    int64  
 2   make               201 non-null    object 
 3   aspiration         201 non-null    object 
 4   num-of-doors       201 non-null    object 
 5   body-style         201 non-null    object 
 6   drive-wheels       201 non-null    object 
 7   engine-location    201 non-null    object 
 8   wheel-base         201 non-null    float64
 9   length             201 non-null    float64
 10  width              201 non-null    float64
 11  height             201 non-null    float64
 12  curb-weight        201 non-null    int64  
 13  engine-type        201 non-null    object 
 14  num-of-cylinders   201 non-null    object 
 15  engine-size        201 non-null    int64  
 16  fuel-system        201 non-null    object 
 17  bore               201 non-null    float64
 18  stroke             197 non-null    float64
 19  compression-ratio  201 non-null    float64
 20  horsepower         201 non-null    float64
 21  peak-rpm           201 non-null    float64
 22  city-mpg           201 non-null    int64  
 23  highway-mpg        201 non-null    int64  
 24  price              201 non-null    float64
 25  city-L/100km       201 non-null    float64
 26  horsepower-binned  200 non-null    object 
 27  diesel             201 non-null    int64  
 28  gas                201 non-null    int64  
dtypes: float64(11), int64(8), object(10)
memory usage: 45.7+ KB
In [61]:
 
#df = df.drop(columns = df[['Unnamed: 0']])
df
Out[61]:
symboling normalized-losses make aspiration num-of-doors body-style drive-wheels engine-location wheel-base length ... compression-ratio horsepower peak-rpm city-mpg highway-mpg price city-L/100km horsepower-binned diesel gas
0 3 122 alfa-romero std two convertible rwd front 88.6 0.811148 ... 9.0 111.0 5000.0 21 27 13495.0 11.190476 Medium 0 1
1 3 122 alfa-romero std two convertible rwd front 88.6 0.811148 ... 9.0 111.0 5000.0 21 27 16500.0 11.190476 Medium 0 1
2 1 122 alfa-romero std two hatchback rwd front 94.5 0.822681 ... 9.0 154.0 5000.0 19 26 16500.0 12.368421 Medium 0 1
3 2 164 audi std four sedan fwd front 99.8 0.848630 ... 10.0 102.0 5500.0 24 30 13950.0 9.791667 Medium 0 1
4 2 164 audi std four sedan 4wd front 99.4 0.848630 ... 8.0 115.0 5500.0 18 22 17450.0 13.055556 Medium 0 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
196 -1 95 volvo std four sedan rwd front 109.1 0.907256 ... 9.5 114.0 5400.0 23 28 16845.0 10.217391 Medium 0 1
197 -1 95 volvo turbo four sedan rwd front 109.1 0.907256 ... 8.7 160.0 5300.0 19 25 19045.0 12.368421 High 0 1
198 -1 95 volvo std four sedan rwd front 109.1 0.907256 ... 8.8 134.0 5500.0 18 23 21485.0 13.055556 Medium 0 1
199 -1 95 volvo turbo four sedan rwd front 109.1 0.907256 ... 23.0 106.0 4800.0 26 27 22470.0 9.038462 Medium 1 0
200 -1 95 volvo turbo four sedan rwd front 109.1 0.907256 ... 9.5 114.0 5400.0 19 25 22625.0 12.368421 Medium 0 1

201 rows × 29 columns

In [62]:
 
plt.subplots(figsize = ([15,15]))
sns.heatmap(df.corr(),cmap = 'YlGnBu',annot = True)
Out[62]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fa85b668d50>
In [63]:
 
new_df = df[['make','city-mpg','price']]
new_df
Out[63]:
make city-mpg price
0 alfa-romero 21 13495.0
1 alfa-romero 21 16500.0
2 alfa-romero 19 16500.0
3 audi 24 13950.0
4 audi 18 17450.0
... ... ... ...
196 volvo 23 16845.0
197 volvo 19 19045.0
198 volvo 18 21485.0
199 volvo 26 22470.0
200 volvo 19 22625.0

201 rows × 3 columns

In [64]:
 
X = new_df.iloc[:,[1,2]]
X
Out[64]:
city-mpg price
0 21 13495.0
1 21 16500.0
2 19 16500.0
3 24 13950.0
4 18 17450.0
... ... ...
196 23 16845.0
197 19 19045.0
198 18 21485.0
199 26 22470.0
200 19 22625.0

201 rows × 2 columns

In [65]:
 
Y = new_df.iloc[:,[0]]
Y
Out[65]:
make
0 alfa-romero
1 alfa-romero
2 alfa-romero
3 audi
4 audi
... ...
196 volvo
197 volvo
198 volvo
199 volvo
200 volvo

201 rows × 1 columns

In [66]:
 
plt.figure(figsize = (15,12))
plt.scatter(X['city-mpg'],Y['make'])
plt.xlabel('City MPG')
plt.ylabel('Make')
Out[66]:
Text(0, 0.5, 'Make')
In [67]:
 
plt.figure(figsize = (15,12))
plt.scatter(X['price'],Y['make'])
plt.xlabel('Price of the Vehicles')
plt.ylabel('Make')
Out[67]:
Text(0, 0.5, 'Make')
In [68]:
 
scaler = StandardScaler()
scaler.fit(X)
scaled_X = pd.DataFrame(scaler.transform(X),columns = X.columns)
scaled_X
Out[68]:
city-mpg price
0 -0.652249 0.036314
1 -0.652249 0.415385
2 -0.964397 0.415385
3 -0.184027 0.093711
4 -1.120471 0.535224
... ... ...
196 -0.340101 0.458906
197 -0.964397 0.736429
198 -1.120471 1.044227
199 0.128120 1.168481
200 -0.964397 1.188034

201 rows × 2 columns

In [69]:
 
k_rng = range(1,10)
sse1 = []
for k1 in k_rng:
    km1 = KMeans(n_clusters = k1)
    km1.fit(scaled_X[['city-mpg']])
    sse1.append(km1.inertia_)
    
plt.xlabel('K_Values')
plt.ylabel('City Mileage')
plt.plot(k_rng,sse1,marker = 'o')
Out[69]:
[<matplotlib.lines.Line2D at 0x7fa85b0ca3d0>]
In [70]:
 
km1 = KMeans(n_clusters = 3)
km1.fit(scaled_X[['city-mpg']])
y_predict = km1.predict(scaled_X[['city-mpg']])
MC_df = pd.concat([scaled_X['city-mpg'],Y['make']],axis = 1)
MC_df['Cluster(M vs C)'] = y_predict
MC_df
Out[70]:
city-mpg make Cluster(M vs C)
0 -0.652249 alfa-romero 1
1 -0.652249 alfa-romero 1
2 -0.964397 alfa-romero 1
3 -0.184027 audi 2
4 -1.120471 audi 1
... ... ... ...
196 -0.340101 volvo 2
197 -0.964397 volvo 1
198 -1.120471 volvo 1
199 0.128120 volvo 2
200 -0.964397 volvo 1

201 rows × 3 columns

In [71]:
 
MC_df1 = MC_df[MC_df['Cluster(M vs C)'] == 0]
MC_df2 = MC_df[MC_df['Cluster(M vs C)'] == 1]
MC_df3 = MC_df[MC_df['Cluster(M vs C)'] == 2]
plt.subplots(figsize = ([15,10]))
plt.scatter(MC_df1['city-mpg'],MC_df1['make'],color = 'red')
plt.scatter(MC_df2['city-mpg'],MC_df2['make'],color = 'green')
plt.scatter(MC_df3['city-mpg'],MC_df3['make'],color = 'blue')
plt.title('KMeans Clustering of Vehicle City Mileage')
plt.xlabel('City Mileage')
plt.ylabel('Make')
Out[71]:
Text(0, 0.5, 'Make')
In [72]:
 
km2 = KMeans(n_clusters = 3)
km2.fit(scaled_X[['price']])
y_predict = km1.predict(scaled_X[['price']])
MP_df = pd.concat([scaled_X['price'],Y['make']],axis = 1)
MP_df['Cluster(M vs P)'] = y_predict
MP_df
/usr/local/lib/python3.7/dist-packages/sklearn/base.py:493: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.
Feature names unseen at fit time:
- price
Feature names seen at fit time, yet now missing:
- city-mpg

  warnings.warn(message, FutureWarning)
Out[72]:
price make Cluster(M vs P)
0 0.036314 alfa-romero 2
1 0.415385 alfa-romero 2
2 0.415385 alfa-romero 2
3 0.093711 audi 2
4 0.535224 audi 2
... ... ... ...
196 0.458906 volvo 2
197 0.736429 volvo 0
198 1.044227 volvo 0
199 1.168481 volvo 0
200 1.188034 volvo 0

201 rows × 3 columns

In [73]:
 
MP_df1 = MP_df[MP_df['Cluster(M vs P)'] == 0]
MP_df2 = MP_df[MP_df['Cluster(M vs P)'] == 1]
MP_df3 = MP_df[MP_df['Cluster(M vs P)'] == 2]
plt.subplots(figsize = ([15,10]))
plt.scatter(MP_df1['price'],MP_df1['make'],color = 'red')
plt.scatter(MP_df2['price'],MP_df2['make'],color = 'green')
plt.scatter(MP_df3['price'],MP_df3['make'],color = 'blue')
plt.title('KMeans Clustering of Price')
plt.xlabel('Price')
plt.ylabel('Make')
Out[73]:
Text(0, 0.5, 'Make')
In [74]:
 
plt.figure(figsize = (15,15))
import scipy.cluster.hierarchy as sch
dendrograms = sch.dendrogram(sch.linkage(scaled_X[['city-mpg']],method = 'ward'))
plt.title('Dendrogram for City Mileage')
plt.xlabel('City Mileage')
plt.ylabel('Euclidean Distance')
Out[74]:
Text(0, 0.5, 'Euclidean Distance')
In [75]:
 
plt.figure(figsize = (15,15))
dendrograms = sch.dendrogram(sch.linkage(scaled_X[['price']],method = 'ward'))
plt.title('Dendrogram for Car Price')
plt.xlabel('Car Price')
plt.ylabel('Euclidean Distance')
Out[75]:
Text(0, 0.5, 'Euclidean Distance')
In [76]:
 
hc = AgglomerativeClustering(n_clusters = 3,affinity = 'euclidean',linkage = 'ward')
hc_predict_mpg = hc.fit_predict(scaled_X[['city-mpg']])
mpg = pd.concat([scaled_X['city-mpg'],Y['make']],axis = 1)
mpg['Clusters'] = hc_predict_mpg
mpg
Out[76]:
city-mpg make Clusters
0 -0.652249 alfa-romero 2
1 -0.652249 alfa-romero 2
2 -0.964397 alfa-romero 2
3 -0.184027 audi 1
4 -1.120471 audi 2
... ... ... ...
196 -0.340101 volvo 1
197 -0.964397 volvo 2
198 -1.120471 volvo 2
199 0.128120 volvo 1
200 -0.964397 volvo 2

201 rows × 3 columns

In [77]:
 
hc = AgglomerativeClustering(n_clusters = 3,affinity = 'euclidean',linkage = 'ward')
hc_predict_price = hc.fit_predict(scaled_X[['price']])
price = pd.concat([scaled_X['price'],Y['make']],axis = 1)
price['Clusters'] = hc_predict_price
price
Out[77]:
price make Clusters
0 0.036314 alfa-romero 0
1 0.415385 alfa-romero 1
2 0.415385 alfa-romero 1
3 0.093711 audi 0
4 0.535224 audi 1
... ... ... ...
196 0.458906 volvo 1
197 0.736429 volvo 1
198 1.044227 volvo 1
199 1.168481 volvo 1
200 1.188034 volvo 1

201 rows × 3 columns

In [78]:
 
mpg1 = mpg[mpg['Clusters'] == 0]
mpg2 = mpg[mpg['Clusters'] == 1]
mpg3 = mpg[mpg['Clusters'] == 2]
plt.subplots(figsize = ([15,10]))
plt.scatter(mpg1['city-mpg'],mpg1['make'],color = 'red')
plt.scatter(mpg2['city-mpg'],mpg2['make'],color = 'green')
plt.scatter(mpg3['city-mpg'],mpg3['make'],color = 'blue')
plt.title('Hierarchical clustering of City Mileage')
plt.xlabel('City Mileage')
plt.ylabel('Make')
Out[78]:
Text(0, 0.5, 'Make')
In [79]:
 
price1 = price[price['Clusters'] == 0]
price2 = price[price['Clusters'] == 1]
price3 = price[price['Clusters'] == 2]
plt.subplots(figsize = ([15,10]))
plt.scatter(price1['price'],price1['make'],color = 'red')
plt.scatter(price2['price'],price2['make'],color = 'green')
plt.scatter(price3['price'],price3['make'],color = 'blue')
plt.title('Hierarchical clustering of Vehicle Price')
plt.xlabel('Price')
plt.ylabel('Make')
Out[79]:
Text(0, 0.5, 'Make')

END OF THE SCRIPTS

conclusion

The code provided performs clustering on a dataset of car makes, prices, and city mileage using KMeans clustering.

First, the code reads in a CSV file with the dataset and selects the "make", "price", and "city-mpg" columns, assigning them to the variables Y, X, and scaled_X, respectively.

Next, the code creates two scatter plots of the data, one with city-mpg on the x-axis and make on the y-axis, and one with price on the x-axis and make on the y-axis.

The code then uses the StandardScaler function to standardize the X data, and creates a range of K values to test for the number of clusters. For each K value, the code calculates the sum of squared errors (SSE) and plots the SSE values against the K values to determine the optimal K value for clustering the data. In this case, the code selects K=3.

The code then performs KMeans clustering on the city-mpg data using K=3, and creates a new DataFrame (MC_df) that includes the standardized city-mpg data, the original car make data, and the cluster assignment for each row. The code then creates three separate DataFrames for each of the three clusters, and creates a scatter plot of the city-mpg data with each cluster assigned a different color.

The code then repeats the same process for price data, but the function warns that some feature names were not seen at fit time, and a future warning is given. The code then creates a new DataFrame (MP_df) that includes the standardized price data, the original car make data, and the cluster assignment for each row. The code then creates three separate DataFrames for each of the three clusters, but there is no scatter plot or graph of the data.

These plots are showing the results of hierarchical clustering of cars based on two different attributes: city mileage and price. The data has been divided into three clusters, represented by different colors (red, green, and blue). The y-axis shows the car make, while the x-axis shows either city mileage or price.

The first plot shows that there is some clustering of cars based on city mileage, with certain makes of cars having higher or lower city mileage. The second plot shows that there is clustering of cars based on price, with some makes of cars being more expensive than others.

Overall, these plots are useful for visualizing the results of hierarchical clustering and can help to identify patterns in the data. However, it is important to keep in mind that these plots are only showing two attributes of the data and there may be other factors that are important in determining how cars are clustered.

Final conclusion

Based on the analysis performed, it can be concluded that hierarchical clustering can be a useful tool for segmenting vehicles based on their price and city mileage. The analysis revealed three clusters for each variable, indicating that there are distinct groups of vehicles that differ in terms of these two attributes.

In terms of city mileage, the first cluster consisted of vehicles with the highest mileage, followed by a cluster with moderate mileage, and a final cluster with the lowest mileage. On the other hand, for vehicle price, the first cluster included the most expensive vehicles, followed by a cluster with moderately-priced vehicles, and a final cluster with the least expensive vehicles.

The scatter plots showed the distribution of makes within each cluster, providing additional insight into the characteristics of each group. Overall, this analysis can help automakers and dealerships better understand the market and tailor their offerings to different customer segments.

​
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