Recommendation System for Personalized Learning Platform: This system leverages a Random Forest Model to recommend suitable learning tracks based on user-entered skills.
Input: Users provide their skills via an interface (e.g., web form or terminal). These skills are processed into feature vectors for the model.
Model: A Random Forest Classifier is trained on a dataset containing skill sets (features) and corresponding learning tracks (labels). The ensemble learning approach ensures high accuracy and robustness in predictions.
Output: The system predicts the most suitable learning track for the user, offering a personalized learning path tailored to their skills.
Advantages: Handles diverse and complex relationships between skills and tracks. Offers scalable and efficient recommendations for large datasets. This system enhances user engagement by aligning their learning goals with relevant skill tracks using machine learning intelligence.
Programming Language: Python
Used Packages & libraries:
-> os: Used for file and directory operations, such as loading datasets stored locally or navigating directories.
-> pandas: Provides easy-to-use data structures like DataFrames for handling and manipulating tabular data. Example: Reading datasets, preprocessing, and organizing data for training and testing.
-> sklearn.model_selection.train_test_split: Splits the dataset into training and testing sets for model validation.
-> sklearn.feature_extraction.text.CountVectorizer: Converts text data (e.g., user-provided skills) into numerical feature vectors, suitable for machine learning models.
-> sklearn.ensemble.RandomForestClassifier: Implements the Random Forest algorithm, an ensemble method that combines multiple decision trees for robust classification.
-> sklearn.metrics.classification_report and accuracy_score: classification_report: Provides detailed performance metrics for the model, such as precision, recall, F1-score, and support. accuracy_score: Calculates the overall accuracy of the predictions.