import pandas as pd
from sklearn.preprocessing import StandardScaler # for scaling
from sklearn.cluster import KMeans # for clustering

# load the dataset
segment_df = pd.read_csv('travelTide_customer_segmented_dataset-kmeans.csv')
segment_df

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	user_id	total_hotel_spent	checked_bags	no_cancelled_trip	discounted_trip	trip_with_return_flight
0	534594	28114.0	0.0	2	0.0	1.0
1	596863	27489.0	4.0	3	0.0	1.0
2	507805	27241.0	6.0	5	1.0	5.0
3	539710	22619.0	1.0	4	1.0	3.0
4	669329	22356.0	0.0	2	0.0	1.0
...	...	...	...	...	...	...
5993	544926	-349.0	0.0	3	2.0	3.0
5994	577534	-398.0	1.0	1	1.0	1.0
5995	596320	-477.0	2.0	2	1.0	1.0
5996	539868	-562.0	1.0	3	1.0	2.0
5997	515835	-575.0	1.0	3	0.0	3.0

5998 rows × 6 columns

# sort the dataset according to user_id
segment_df = segment_df.sort_values('user_id', ascending = True)

segment_df

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	user_id	total_hotel_spent	checked_bags	no_cancelled_trip	discounted_trip	trip_with_return_flight
863	23557	3802.0	0.0	2	1.0	0.0
5178	94883	130.0	1.0	2	0.0	2.0
1856	101486	2199.0	0.0	2	0.0	1.0
1634	101961	2429.0	2.0	5	1.0	5.0
117	106907	8514.0	10.0	1	0.0	1.0
...	...	...	...	...	...	...
5151	792549	144.0	2.0	4	0.0	1.0
2984	796032	1261.0	5.0	3	1.0	2.0
3704	801660	872.0	1.0	3	1.0	3.0
3742	811077	852.0	0.0	1	0.0	1.0
5965	844489	0.0	0.0	0	0.0	0.0

5998 rows × 6 columns

# Select relevant metrics for clustering
metrics = ['total_hotel_spent', 'checked_bags', 'no_cancelled_trip', 'discounted_trip', 'trip_with_return_flight']
X = segment_df[metrics]

X

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	total_hotel_spent	checked_bags	no_cancelled_trip	discounted_trip	trip_with_return_flight
863	3802.0	0.0	2	1.0	0.0
5178	130.0	1.0	2	0.0	2.0
1856	2199.0	0.0	2	0.0	1.0
1634	2429.0	2.0	5	1.0	5.0
117	8514.0	10.0	1	0.0	1.0
...	...	...	...	...	...
5151	144.0	2.0	4	0.0	1.0
2984	1261.0	5.0	3	1.0	2.0
3704	872.0	1.0	3	1.0	3.0
3742	852.0	0.0	1	0.0	1.0
5965	0.0	0.0	0	0.0	0.0

5998 rows × 5 columns

# Standardize the data
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

X_scaled

array([[ 0.79305145, -0.97483371, -0.44893115,  0.36429641, -1.34872558],
       [-0.77913349, -0.28377443, -0.44893115, -0.87229095,  0.07626227],
       [ 0.10671908, -0.97483371, -0.44893115, -0.87229095, -0.63623165],
       ...,
       [-0.46144252, -0.28377443,  0.20846364,  0.36429641,  0.78875619],
       [-0.47000562, -0.97483371, -1.10632594, -0.87229095, -0.63623165],
       [-0.83479363, -0.97483371, -1.76372072, -0.87229095, -1.34872558]])

# Apply K-means clustering
num_clusters = 5  # Number of clusters
kmeans = KMeans(n_clusters=num_clusters, random_state=42)
segment_df['cluster'] = kmeans.fit_predict(X_scaled)

C:\Python311\Lib\site-packages\sklearn\cluster\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(

# Assign perks based on cluster
def assign_perk(cluster_id):
    if cluster_id == 0:
        return 'Free Hotel Meal'
    elif cluster_id == 1:
        return 'Free Checked Bag'
    elif cluster_id == 2:
        return 'No Cancellation Fees'
    elif cluster_id == 3:
        return 'Exclusive discounts'
    elif cluster_id == 4:
        return '1 night free with flight'

segment_df['perk'] = segment_df['cluster'].apply(assign_perk)

# see the assigned cluster
segment_df[['user_id', 'cluster', 'perk']]

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	user_id	cluster	perk
863	23557	3	Exclusive discounts
5178	94883	1	Free Checked Bag
1856	101486	3	Exclusive discounts
1634	101961	0	Free Hotel Meal
117	106907	2	No Cancellation Fees
...	...	...	...
5151	792549	1	Free Checked Bag
2984	796032	0	Free Hotel Meal
3704	801660	1	Free Checked Bag
3742	811077	3	Exclusive discounts
5965	844489	3	Exclusive discounts

5998 rows × 3 columns

segment_df

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	user_id	total_hotel_spent	checked_bags	no_cancelled_trip	discounted_trip	trip_with_return_flight	cluster	perk
863	23557	3802.0	0.0	2	1.0	0.0	3	Exclusive discounts
5178	94883	130.0	1.0	2	0.0	2.0	1	Free Checked Bag
1856	101486	2199.0	0.0	2	0.0	1.0	3	Exclusive discounts
1634	101961	2429.0	2.0	5	1.0	5.0	0	Free Hotel Meal
117	106907	8514.0	10.0	1	0.0	1.0	2	No Cancellation Fees
...	...	...	...	...	...	...	...	...
5151	792549	144.0	2.0	4	0.0	1.0	1	Free Checked Bag
2984	796032	1261.0	5.0	3	1.0	2.0	0	Free Hotel Meal
3704	801660	872.0	1.0	3	1.0	3.0	1	Free Checked Bag
3742	811077	852.0	0.0	1	0.0	1.0	3	Exclusive discounts
5965	844489	0.0	0.0	0	0.0	0.0	3	Exclusive discounts

5998 rows × 8 columns

# save the segmented dataset
segment_df[['user_id', 'perk']].to_csv('travelTide_customer_segmented_dataset-kmeans-applied.csv', index=False)

import matplotlib.pyplot as plt # for plotting

# Group by 'perk' column and count rows in each group
grouped = segment_df.groupby('perk').size().reset_index(name='Count')

# Create a bar plot
plt.bar(grouped['perk'], grouped['Count'])
plt.xlabel('Perks')
plt.ylabel('Total users')
plt.title('User vs Perk (K-Means)')
plt.xticks(rotation=20)

# Add value labels on top of bars
for i, value in enumerate(grouped['Count']):
    plt.text(i, value + 0.1, str(value), ha='center', va='bottom', fontsize=10)

    
plt.show()

grouped

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	perk	Count
0	1 night free with flight	743
1	Exclusive discounts	1864
2	Free Checked Bag	2074
3	Free Hotel Meal	1007
4	No Cancellation Fees	310