pml-training.csv

[Alifianajwa]

pml-training.csv

1. Preprocessing Data
   • Missing Values:
   na_percentage <- sapply(data, function(x) mean(is.na(x)))
   data_clean <- data[, na_percentage < 0.9] # Hapus kolom dengan >90% NA

• Delete Non-Predictive Coloum:
data_clean <- data_clean %>%
select(-c(X, user_name, raw_timestamp_part_1, raw_timestamp_part_2,
cvtd_timestamp, new_window, num_window))

• Conversion Variabel:
data_clean$classe <- as.factor(data_clean$classe)

2. Feature Selection and Data Division
   • Data Partition (70% training, 30% testing):
   set.seed(123)
   trainIndex <- createDataPartition(data_clean$classe, p = 0.7, list = FALSE)
   training <- data_clean[trainIndex, ]
   testing <- data_clean[-trainIndex, ]

3. Model Building with Random Forest
   • Model Training with 5-fold Cross Validation:
   trControl <- trainControl(method = "cv", number = 5)
   model_rf <- train(classe ~ .,
   data = training,
   method = "rf",
   trControl = trControl,
   verbose = FALSE)

• Model results:
Accuracy: 0.9923
Out-of-Sample Error Estimate: 0.77%

4. Model Validation and Evaluation
   • Prediction on Testing Set:
   predictions <- predict(model_rf, newdata = testing)
   confusionMatrix(predictions, testing$classe)

• Evaluation Results:
Accuracy : 0.993
95% CI : (0.991, 0.994)

5. Out-of-Sample Error Estimation
   • Estimated using cross-validation during model training:
   model_rf$results$Accuracy # Menunjukkan akurasi validasi silang

•Out-of-Sample Error = 1 - Accuracy = 1 - 0.9923 = 0.0077

•Conclusion:
Model Built: Random Forest with 99.23% accuracy on training data and 99.3% on testing set.
Error Estimation: Out-of-sample error estimated at 0.77% using cross validation.
Prediction Quality: The model was highly accurate in classifying the 5 activity types (A-E).

[hjwp]

looks like this ended up in the wrong place.