Create app.py

app.py

@@ -0,0 +1,41 @@
+from datasets import load_dataset
+import pandas as pd
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import classification_report, accuracy_score
+from category_encoders import OneHotEncoder
+
+dataset = load_dataset("ombhojane/ckv3")
+df = pd.DataFrame(dataset['train'])
+
+# Preprocessing
+# One-hot encoding for categorical features
+encoder = OneHotEncoder(cols=['Biodiversity', 'Existing Infrastructure'], use_cat_names=True)
+df_encoded = encoder.fit_transform(df)
+
+scaler = StandardScaler()
+df_encoded[['Land Size (hectares)', 'Budget (INR)']] = scaler.fit_transform(df_encoded[['Land Size (hectares)', 'Budget (INR)']])
+
+# Splitting features and target
+X = df_encoded.drop('Service', axis=1)
+y = df_encoded['Service']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+model = RandomForestClassifier()
+param_grid = {
+    'n_estimators': [100, 200, 300],
+    'max_depth': [None, 10, 20, 30],
+    'min_samples_split': [2, 5, 10]
+}
+
+grid_search = GridSearchCV(model, param_grid, cv=5, scoring='accuracy')
+grid_search.fit(X_train, y_train)
+
+best_model = grid_search.best_estimator_
+
+# Model Evaluation
+predictions = best_model.predict(X_test)
+print(classification_report(y_test, predictions))
+print("Accuracy:", accuracy_score(y_test, predictions))