import pandas as pd
import numpy as np
import openml
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')

url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00502/online_retail_II.xlsx'
df = pd.read_excel(url, sheet_name='Year 2010-2011')
df['InvoiceDate'] = pd.to_datetime(df['InvoiceDate'])
print(df.shape)
print(df.head(3))

customer_df = df.groupby('Customer ID').agg({
    'Invoice': 'nunique',
    'Quantity': 'sum',
    'Price': 'sum'
}).reset_index().rename(columns={'Invoice':'NumPurchases','Quantity':'TotalQty','Price':'TotalSpent'})
print(customer_df.head(3))

print('Missing values per column:')
print(customer_df.isnull().sum())
customer_df = customer_df.dropna()
print('Customer sample after dropping missing:')
print(customer_df.head(3))

scaler = StandardScaler()
features = ['NumPurchases', 'TotalQty', 'TotalSpent']
X_scaled = scaler.fit_transform(customer_df[features])
print('Means after scaling:', X_scaled.mean(axis=0))

inertia = []
K_range = range(1, 10)
for k in K_range:
    km = KMeans(n_clusters=k, random_state=42)
    km.fit(X_scaled)
    inertia.append(km.inertia_)
plt.figure(figsize=(7, 4))
plt.plot(K_range, inertia, marker='o')
plt.xlabel('Number of clusters (k)')
plt.ylabel('Inertia')
plt.title('Elbow Method for Optimal k')
plt.show()

best_k = 3
kmeans = KMeans(n_clusters=best_k, random_state=42)
clusters = kmeans.fit_predict(X_scaled)
customer_df['Cluster'] = clusters
print(customer_df['Cluster'].value_counts())

sns.pairplot(customer_df, hue='Cluster', vars=features, palette='Set1')
plt.suptitle('Customer Clusters by Behavior', y=1.02)
plt.show()

summary = customer_df.groupby('Cluster')[features].mean()
print('Average behavior per cluster:')
print(summary)
summary.plot(kind='bar', figsize=(8,5))
plt.title('Average Customer Metrics per Segment')
plt.ylabel('Standardized Value')
plt.xlabel('Cluster Label')
plt.legend(loc='best')
plt.show()

dataset = openml.datasets.get_dataset(42178)
df_survey, _, _, _ = dataset.get_data(dataset_format='dataframe')
print(df_survey.shape)
print(df_survey.head(3))

cat_columns = ['gender', 'Partner', 'Dependents', 'InternetService', 'Contract', 'PaymentMethod']
survey_enc = df_survey.copy()
for col in cat_columns:
    survey_enc[col] = LabelEncoder().fit_transform(survey_enc[col].astype(str))
print(survey_enc[cat_columns].head(3))

survey_features = ['gender', 'SeniorCitizen', 'Partner', 'tenure', 'MonthlyCharges']
survey_scaled = StandardScaler().fit_transform(survey_enc[survey_features])
survey_kmeans = KMeans(n_clusters=3, random_state=42)
survey_clusters = survey_kmeans.fit_predict(survey_scaled)
df_survey['Segment'] = survey_clusters
print(df_survey.groupby('Segment').size())

# Customer NPS Clustering Example
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
nps_df = pd.DataFrame({
    'CustomerID': range(1, 501),
    'Age': np.random.randint(18, 70, 500),
    'Region': np.random.choice(['North', 'South', 'East', 'West'], 500),
    'NPS_Score': np.random.randint(0, 11, 500)
})
X_nps = nps_df[['Age', 'NPS_Score']]
scaler = StandardScaler()
X_nps_scaled = scaler.fit_transform(X_nps)
kmeans_nps = KMeans(n_clusters=4, random_state=42)
nps_clusters = kmeans_nps.fit_predict(X_nps_scaled)
nps_df['Cluster'] = nps_clusters
cluster_summary = nps_df.groupby('Cluster')[['NPS_Score', 'Age']].mean()
print(cluster_summary)
plt.figure(figsize=(8,5))
plt.scatter(nps_df['Age'], nps_df['NPS_Score'], c=nps_df['Cluster'], cmap='viridis', alpha=0.6)
plt.title('Customer Segments Based on Age and NPS Score')
plt.xlabel('Age')
plt.ylabel('NPS Score')
plt.colorbar(label='Cluster')
plt.show()

missing_before = df_survey.isnull().sum().sum()
df_survey = df_survey.fillna(method='ffill').fillna(method='bfill')
missing_after = df_survey.isnull().sum().sum()
print('Missing before:', missing_before)
print('Missing after:', missing_after)

try:
    # Intentionally grouping without resetting index
    err_df = df.groupby('Country').agg({'Quantity':'sum'})
    print('Grouped:', err_df.head(2))
except Exception as e:
    print('Grouping error:', e)

nps_values = np.random.randint(0, 11, 20)
labels = ['Detractor' if s < 7 else 'Promoter' if s > 8 else 'Passive' for s in nps_values]
print('NPS values:', nps_values)
print('Labeled interpretation:', labels)

filtered = customer_df[customer_df['TotalSpent'] > 0]
top_segment = filtered.groupby('Cluster')['TotalSpent'].mean().idxmax()
print(f'The most valuable segment is Cluster {top_segment}:')
segment_members = filtered[filtered['Cluster'] == top_segment]
print('Sample high-value customers:')
print(segment_members[['Customer ID','TotalSpent']].head())

report = 'Customer Segmentation using K-Means\n\n'
report += f'Total customers: {len(filtered)}\n'
for cid, rec in segment_members.head(3).iterrows():
    report += f'CustomerID: {rec[0]}, TotalSpent: {rec[4]:.2f}\n'
with open('cluster_report.txt', 'w') as f:
    f.write(report)
print('Report saved.')