Create Random_Forest.py

Random_Forest.py

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+import pandas as pd
+import numpy as np
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+
+data = pd.read_csv("train") #train is location of training data
+data = data.dropna(axis='columns', thresh = int(0.5 * len(data)))
+data.fillna(data.mean())
+col = data.columns
+X = data[col[1:]]
+y = data['LABEL']
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
+
+# Create the model with 100 trees
+model = model = RandomForestClassifier(n_estimators=100, 
+                               bootstrap = True,
+                               max_features = 'sqrt')
+# Fit on training data
+model.fit(X_train, y_train)
+print(f'Model Accuracy: {model.score(X_test, y_test)}')