tarea de botanica

The goal of this project is to develop a machine learning model to classify news articles into categories such as Bias or Conspiracy based on linguistic features, using Text Mining and Machine Learning techniques in R. We will follow a structured pipeline: 1. Data Exploration (EDA) 2. Text Preprocessing 3. TF-IDF Feature Engineering 4. Word Cloud Analysis 5. Sentiment Analysis) 6. Train-Validation-Test Split 7. Model Building(Random Forest and SVM) 8. Model Evaluation 9. Final Model Comparison and Conclusion 10.Future Directions

The goal of this project is to develop a machine learning model to classify news articles into categories such as Bias or Conspiracy based on linguistic features, using Text Mining and Machine Learning techniques in R. We will follow a structured pipeline: 1. Data Exploration (EDA) 2. Text Preprocessing 3. TF-IDF Feature Engineering 4. Word Cloud Analysis 5. Sentiment Analysis) 6. Train-Validation-Test Split 7. Model Building(Random Forest and SVM) 8. Model Evaluation 9. Final Model Comparison and Conclusion 10.Future Directions

Exercise8

R coffee project

Compare GNI per capita vs. GINI index of resource-dependent countries in 2018