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

Es un modulo estadístico que establece una relación entre dos variables: una independiente (predictora) y una dependiente (de respuesta). El modelo de regresión lineal para este taller (lm) donde el resultado de examen es la variable dependiente (lo que queremos predecir) y las horas de estudio es la variable independiente (lo que usamos para predecir)