Create Task Scheduling & Time Management.py

Task Scheduling & Time Management.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+
+# Load the dataset (assumed to be a CSV file)
+# Example dataset can be downloaded from Kaggle, for example, "Task Completion Prediction"
+# Replace the path with your local dataset file
+df = pd.read_csv('tasks_dataset.csv')
+
+# Assume the dataset has columns: urgency, importance, deadline_hours_left, task_length_hours, priority
+# Preview the dataset to understand its structure
+print(df.head())
+
+# Features and labels (adjust based on actual column names from the dataset)
+X = df[['urgency', 'importance', 'deadline_hours_left', 'task_length_hours']]  # Features
+y = df['priority']  # Priority labels (1 for high priority, 0 for low)
+
+# Split the dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Initialize the Random Forest Classifier
+clf = RandomForestClassifier()
+
+# Train the model
+clf.fit(X_train, y_train)
+
+# Predict priorities for the test set
+y_pred = clf.predict(X_test)
+
+# Calculate and print the accuracy of the model
+accuracy = accuracy_score(y_test, y_pred)
+print(f'Accuracy: {accuracy * 100:.2f}%')
+
+# Test the model with a new task (provide actual values for urgency, importance, etc.)
+new_task = [[4, 5, 8, 2]]  # Example: [urgency, importance, deadline_hours_left, task_length_hours]
+predicted_priority = clf.predict(new_task)
+print(f'Predicted priority for the new task: {"High" if predicted_priority[0] == 1 else "Low"}')
+
+# Feature importance to understand how the model prioritizes tasks
+feature_importances = clf.feature_importances_
+for feature, importance in zip(X.columns, feature_importances):
+    print(f'Feature: {feature}, Importance: {importance}')