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import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from pandas.tseries.holiday import USFederalHolidayCalendar as calendar
def define_target(df):
"""
Creates the target variable 'IsViolent' based on crime category.
"""
violent_categories = [
'ASSAULT', 'ROBBERY', 'SEX OFFENSES FORCIBLE', 'KIDNAPPING', 'HOMICIDE', 'ARSON'
]
df['IsViolent'] = df['Category'].apply(lambda x: 1 if x in violent_categories else 0)
return df
def extract_temporal_features(df):
"""
Extracts temporal features from the 'Dates' column.
"""
df['Hour'] = df['Dates'].dt.hour
df['Day'] = df['Dates'].dt.day
df['Month'] = df['Dates'].dt.month
df['Year'] = df['Dates'].dt.year
df['DayOfWeek'] = df['Dates'].dt.dayofweek # 0=Monday, 6=Sunday
df['IsWeekend'] = df['DayOfWeek'].apply(lambda x: 1 if x >= 5 else 0)
# Holidays
cal = calendar()
holidays = cal.holidays(start=df['Dates'].min(), end=df['Dates'].max())
df['IsHoliday'] = df['Dates'].dt.date.astype('datetime64[ns]').isin(holidays).astype(int)
return df
def get_season(month):
if month in [12, 1, 2]:
return 'Winter'
elif month in [3, 4, 5]:
return 'Spring'
elif month in [6, 7, 8]:
return 'Summer'
else:
return 'Fall'
def extract_contextual_features(df):
"""
Extracts contextual features like Season.
"""
df['Season'] = df['Month'].apply(get_season)
return df
def extract_location_features(df, n_clusters=10, kmeans_model=None):
"""
Extracts location features including K-Means clusters for high-crime zones.
"""
if kmeans_model is None:
# Fit mode
kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
df['LocationCluster'] = kmeans.fit_predict(df[['X', 'Y']])
return df, kmeans
else:
# Predict mode
df['LocationCluster'] = kmeans_model.predict(df[['X', 'Y']])
return df, kmeans_model
def preprocess_pipeline(df, is_train=True, kmeans_model=None):
"""
Runs the full preprocessing pipeline.
"""
df = extract_temporal_features(df)
df = extract_contextual_features(df)
# Location features (Clustering)
df, kmeans_model = extract_location_features(df, kmeans_model=kmeans_model)
if is_train:
df = define_target(df)
return df, kmeans_model
if __name__ == "__main__":
# Test
pass
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