library(caret)
library(rpart)
library(rpart.plot)
library(RColorBrewer)
library(RGtk2)
library(rattle)
library(randomForest)
library(gbm)

Here are the datasets, loaded directly from web and then downloaded.

training.data<- 'pml-training.csv'
testing.data <- 'pml-testing.csv'
training.data<- read.csv('pml-training.csv')
testing.data <-read.csv('pml-testing.csv')

dim(training.data)

[1] 19622   160

dim(testing.data)

[1]  20 160

Data Cleansing

A. Removing Variables which are having nearly zero variance.

non_zero_var <- nearZeroVar(training.data)
training.df <- training.data[,-non_zero_var]
testing.df <- testing.data[,-non_zero_var]

dim(training.df)

[1] 19622   100

dim(testing.df)

[1]  20 100

B. Removing Variables which are having NA values. Our threshhold is 95%.

na_val_col <- sapply(training.df, function(x) mean(is.na(x))) > 0.95
training.df <- training.df[,na_val_col == FALSE]
testing.df <- testing.df[,na_val_col == FALSE]

dim(training.df)

[1] 19622 59

dim(testing.df)

[1] 20 59

C. Removing variables which are non-numeric and hence will not contribute into our model. The very first 7 variables are of that kind only. Hence those needs to be removed from the datasets.

training.df <- training.df[,8:59]
testing.df <- testing.df[,8:59]

dim(training.df)

[1] 19622    52

dim(testing.df)

[1] 20 52

colnames(training.df)

[1] "pitch_belt"           "yaw_belt"             "total_accel_belt"    
 [4] "gyros_belt_x"         "gyros_belt_y"         "gyros_belt_z"        
 [7] "accel_belt_x"         "accel_belt_y"         "accel_belt_z"        
[10] "magnet_belt_x"        "magnet_belt_y"        "magnet_belt_z"       
[13] "roll_arm"             "pitch_arm"            "yaw_arm"             
[16] "total_accel_arm"      "gyros_arm_x"          "gyros_arm_y"         
[19] "gyros_arm_z"          "accel_arm_x"          "accel_arm_y"         
[22] "accel_arm_z"          "magnet_arm_x"         "magnet_arm_y"        
[25] "magnet_arm_z"         "roll_dumbbell"        "pitch_dumbbell"      
[28] "yaw_dumbbell"         "total_accel_dumbbell" "gyros_dumbbell_x"    
[31] "gyros_dumbbell_y"     "gyros_dumbbell_z"     "accel_dumbbell_x"    
[34] "accel_dumbbell_y"     "accel_dumbbell_z"     "magnet_dumbbell_x"   
[37] "magnet_dumbbell_y"    "magnet_dumbbell_z"    "roll_forearm"        
[40] "pitch_forearm"        "yaw_forearm"          "total_accel_forearm" 
[43] "gyros_forearm_x"      "gyros_forearm_y"      "gyros_forearm_z"     
[46] "accel_forearm_x"      "accel_forearm_y"      "accel_forearm_z"     
[49] "magnet_forearm_x"     "magnet_forearm_y"     "magnet_forearm_z"    
[52] "classe"

colnames(testing.df)

[1] "pitch_belt"           "yaw_belt"             "total_accel_belt"    
 [4] "gyros_belt_x"         "gyros_belt_y"         "gyros_belt_z"        
 [7] "accel_belt_x"         "accel_belt_y"         "accel_belt_z"        
[10] "magnet_belt_x"        "magnet_belt_y"        "magnet_belt_z"       
[13] "roll_arm"             "pitch_arm"            "yaw_arm"             
[16] "total_accel_arm"      "gyros_arm_x"          "gyros_arm_y"         
[19] "gyros_arm_z"          "accel_arm_x"          "accel_arm_y"         
[22] "accel_arm_z"          "magnet_arm_x"         "magnet_arm_y"        
[25] "magnet_arm_z"         "roll_dumbbell"        "pitch_dumbbell"      
[28] "yaw_dumbbell"         "total_accel_dumbbell" "gyros_dumbbell_x"    
[31] "gyros_dumbbell_y"     "gyros_dumbbell_z"     "accel_dumbbell_x"    
[34] "accel_dumbbell_y"     "accel_dumbbell_z"     "magnet_dumbbell_x"   
[37] "magnet_dumbbell_y"    "magnet_dumbbell_z"    "roll_forearm"        
[40] "pitch_forearm"        "yaw_forearm"          "total_accel_forearm" 
[43] "gyros_forearm_x"      "gyros_forearm_y"      "gyros_forearm_z"     
[46] "accel_forearm_x"      "accel_forearm_y"      "accel_forearm_z"     
[49] "magnet_forearm_x"     "magnet_forearm_y"     "magnet_forearm_z"    
[52] "problem_id"

Partition the training data into a training set and a testing/validation set

inTrain <- createDataPartition(training.df$classe, p=0.6, list=FALSE)
training <- training.df[inTrain,]
testing <- training.df[-inTrain,]
dim(training)

[1] 11776    52

dim(testing)

[1] 7846   52

DT_modfit <- train(classe ~ ., data = training, method="rf")
DT_prediction <- predict(DT_modfit, testing)
confusionMatrix(DT_prediction,DT_prediction)

Confusion Matrix and Statistics

          Reference
Prediction    A    B    C    D    E
         A 4203    0    0    0    0
         B    0 1122    0    0    0
         C    0    0 1273    0    0
         D    0    0    0  824    0
         E    0    0    0    0  424

Overall Statistics
                                     
               Accuracy : 1          
                 95% CI : (0.9995, 1)
    No Information Rate : 0.5357     
    P-Value [Acc > NIR] : < 2.2e-16  
                                     
                  Kappa : 1          
                                     
 Mcnemar's Test P-Value : NA         

Statistics by Class:

                     Class: A Class: B Class: C Class: D Class: E
Sensitivity            1.0000    1.000   1.0000    1.000  1.00000
Specificity            1.0000    1.000   1.0000    1.000  1.00000
Pos Pred Value         1.0000    1.000   1.0000    1.000  1.00000
Neg Pred Value         1.0000    1.000   1.0000    1.000  1.00000
Prevalence             0.5357    0.143   0.1622    0.105  0.05404
Detection Rate         0.5357    0.143   0.1622    0.105  0.05404
Detection Prevalence   0.5357    0.143   0.1622    0.105  0.05404
Balanced Accuracy      1.0000    1.000   1.0000    1.000  1.00000

rpart.plot(DT_modfit$finalModel, roundint=FALSE)

Random Forest Model

RF_modfit <- train(classe ~ ., data = training, method = "rf", ntree = 100)

Prediction in terms of Random Forest Model

RF_prediction <- predict(RF_modfit, testing)
RF_pred_conf <- confusionMatrix(RF_prediction, testing$classe)

Confusion Matrix and Statistics
Confusion Matrix and Statistics

          Reference
 Prediction    A    B    C    D    E
          A 2230   14    0    0    0
          B    1 1495   10    0    0
          C    1    7 1353   17    1
          D    0    0    5 1268    5
          E    0    2    0    1 1436
 
 Overall Statistics
                                           
                Accuracy : 0.9918          
                  95% CI : (0.9896, 0.9937)
     No Information Rate : 0.2845          
     P-Value [Acc > NIR] : < 2.2e-16       
                                           
                  Kappa : 0.9897          
  Mcnemar's Test P-Value : NA              
 
 Statistics by Class:
 
                      Class: A Class: B Class: C Class: D Class: E
 Sensitivity            0.9991   0.9848   0.9890   0.9860   0.9958
 Specificity            0.9975   0.9983   0.9960   0.9985   0.9995
 Pos Pred Value         0.9938   0.9927   0.9811   0.9922   0.9979
 Neg Pred Value         0.9996   0.9964   0.9977   0.9973   0.9991
 Prevalence             0.2845   0.1935   0.1744   0.1639   0.1838
 Detection Rate         0.2842   0.1905   0.1724   0.1616   0.1830
 Detection Prevalence   0.2860   0.1919   0.1758   0.1629   0.1834
 Balanced Accuracy      0.9983   0.9916   0.9925   0.9922   0.9977

Gradient Boosting Model

 GBM_modfit <- train(classe ~ ., data = training, method = "gbm", verbose = FALSE)

GBM_prediction <- predict(GBM_modfit, testing)

GBM_pred_conf <- confusionMatrix(GBM_prediction, testing$classe)

Confusion Matrix and Statistics
           Reference
 Prediction    A    B    C    D    E
          A 2191   61    0    2    1
          B   26 1414   40    7   11
          C  8   36 1313   40   16
          D    6    1   12 1220   18
          E    1    6    3   17 1396
 
 Overall Statistics
                                           
                Accuracy : 0.9602          
                  95% CI : (0.9557, 0.9645)
     No Information Rate : 0.2845          
     P-Value [Acc > NIR] : < 2.2e-16       
                                           
                   Kappa : 0.9497          
  Mcnemar's Test P-Value : 4.337e-08       
 
 Statistics by Class:
 
                      Class: A Class: B Class: C Class: D Class: E
 Sensitivity            0.9816   0.9315   0.9598   0.9487   0.9681
 Specificity            0.9886   0.9867   0.9846   0.9944   0.9958
 Pos Pred Value         0.9716   0.9439   0.9292   0.9706   0.9810
 Neg Pred Value         0.9927   0.9836   0.9915   0.9900   0.9928
 Prevalence             0.2845   0.1935   0.1744   0.1639   0.1838
 Detection Rate         0.2793   0.1802   0.1673   0.1555   0.1779
 Detection Prevalence   0.2874   0.1909   0.1801   0.1602   0.1814
 Balanced Accuracy      0.9851   0.9591   0.9722   0.9715   0.9819

Now we need to see how each model has predicted the validation dataset across the classifications. We are not considering Decision Tree model as it didn’t reach the satisfactory prediction accuracy level. SO only Random Forest and Gradient Boosting methods are being compared.

RF_pred_conf$overall

 Accuracy       Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
0.9918430      0.9896806      0.9895954      0.9937126      0.2844762 
 AccuracyPValue  McnemarPValue 
      0.0000000            NaN

GBM_pred_conf$overall

Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
9.602345e-01   9.496836e-01   9.556727e-01   9.644503e-01   2.844762e-01 
 AccuracyPValue  McnemarPValue 
 0.000000e+00   4.336741e-08

Conclusion

After checking the Overall Statistics data, the Random Forest model has definitely more accuracy than GBM. Hence we will be selecting Random Forest model for final prediction from testing.df

Final Prediction- Applying selected model on the Test Data

Final_RF_prediction <- predict(RF_modfit, testing.df)

[1] B A B A A E D B A A B C B A E E A B B B
 Levels: A B C D E

practical machine learning

Ravi Bhushan Bhardwaj

May 27, 2020

Here are the datasets, loaded directly from web and then downloaded.

Data Cleansing

A. Removing Variables which are having nearly zero variance.

B. Removing Variables which are having NA values. Our threshhold is 95%.

C. Removing variables which are non-numeric and hence will not contribute into our model. The very first 7 variables are of that kind only. Hence those needs to be removed from the datasets.

Partition the training data into a training set and a testing/validation set

Random Forest Model

Prediction in terms of Random Forest Model

Gradient Boosting Model

Now we need to see how each model has predicted the validation dataset across the classifications. We are not considering Decision Tree model as it didn’t reach the satisfactory prediction accuracy level. SO only Random Forest and Gradient Boosting methods are being compared.

Conclusion

After checking the Overall Statistics data, the Random Forest model has definitely more accuracy than GBM. Hence we will be selecting Random Forest model for final prediction from testing.df

Final Prediction- Applying selected model on the Test Data

practical machine learning

Ravi Bhushan Bhardwaj

May 27, 2020

Here are the datasets, loaded directly from web and then downloaded.

Data Cleansing

A. Removing Variables which are having nearly zero variance.

B. Removing Variables which are having NA values. Our threshhold is 95%.

C. Removing variables which are non-numeric and hence will not contribute into our model. The very first 7 variables are of that kind only. Hence those needs to be removed from the datasets.

Partition the training data into a training set and a testing/validation set

Random Forest Model

Prediction in terms of Random Forest Model

Gradient Boosting Model

** Now we need to see how each model has predicted the validation dataset across the classifications. ** We are not considering Decision Tree model as it didn’t reach the satisfactory prediction accuracy level. SO only Random Forest and Gradient Boosting methods are being compared.

Conclusion

After checking the Overall Statistics data, the Random Forest model has definitely more accuracy than GBM. Hence we will be selecting Random Forest model for final prediction from testing.df

Final Prediction- Applying selected model on the Test Data

Now we need to see how each model has predicted the validation dataset across the classifications. We are not considering Decision Tree model as it didn’t reach the satisfactory prediction accuracy level. SO only Random Forest and Gradient Boosting methods are being compared.