Punisher & Programmer
Punisher Punisher
Programmer Programmer
Punisher Punisher
I’ve got a stack of crime data on a serial offender. Want to help me build a predictive model that spots his next move before he does?
Programmer Programmer
Sure, let’s start by loading the dataset and inspecting the columns. Then we can pick a few features like time of day, location, and victim profile. Once we have the data cleaned, I’ll set up a supervised model—maybe a random forest or gradient boosting—to predict the next crime location and time. After that we’ll evaluate it with cross‑validation and fine‑tune the hyperparameters. Sound good?
Punisher Punisher
Sounds solid. Let’s pull the data and get those columns in order. I'll handle the cleaning and feature prep, you set up the model, and we run the cross‑validation to lock in the best parameters. Once we have a reliable prediction, we can pinpoint the next move. Let's move.
Programmer Programmer
Okay, load the CSV into a DataFrame, inspect the headers, then split into X and y. I’ll set up a Pipeline with a RandomForestClassifier, use GridSearchCV over n_estimators, max_depth, and min_samples_split. We’ll run 5‑fold CV, capture the best score, then output the feature importance. Once you finish cleaning, just drop the placeholder columns and hand me the cleaned X and y. Then I’ll run the grid search. Let’s keep the code concise and focused.
Punisher Punisher
import pandas as pd from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.pipeline import Pipeline from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score # Load data df = pd.read_csv('crime_data.csv') # Inspect columns print(df.columns) # Separate features and target # Assume target column is 'next_crime' (binary) y = df['next_crime'] X = df.drop(columns=['next_crime', 'placeholder1', 'placeholder2']) # drop placeholders # Basic cleaning: drop rows with missing values X = X.dropna() y = y.loc[X.index] # Train/test split (not strictly needed for GridSearch but handy) X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y) # Pipeline pipe = Pipeline([('clf', RandomForestClassifier(random_state=42))]) # Parameter grid param_grid = { 'clf__n_estimators': [100, 200, 300], 'clf__max_depth': [None, 10, 20, 30], 'clf__min_samples_split': [2, 5, 10] } # GridSearchCV grid = GridSearchCV(pipe, param_grid, cv=5, scoring='accuracy', n_jobs=-1) grid.fit(X_train, y_train) print('Best CV accuracy:', grid.best_score_) print('Best params:', grid.best_params_) # Feature importance best_model = grid.best_estimator_.named_steps['clf'] importances = best_model.feature_importances_ feat_importance = pd.Series(importances, index=X.columns).sort_values(ascending=False) print('Feature importances:\n', feat_importance) # Validate on holdout y_pred = grid.predict(X_val) print('Holdout accuracy:', accuracy_score(y_val, y_pred))