# Load the data

data $=$ load$\_$data$()$

# Perform EDA

perform$\_$eda$($data$)$

# Split the data into training, validation, and test sets

train$\_$data, validation$\_$data, test$\_$data $=$ split$\_$data$($data$,$ ratios$=[0\mathrm{.}6,0\mathrm{.}1,0\mathrm{.}3])$

# Initialize the results

results $=[]$

# Repeat the experiment $5$ times

for i in range$(5)$:

# Train the KNN model on the training data

model $=$ train$\_$knn$($train$\_$data, k$)$

# Validate the model on the validation data and tune $\text{'}$k$\text{'}$

k $=$ tune$\_$knn$($model$,$ validation$\_$data$)$

# Test the model on the test data

result $=$ test$\_$knn$($model$,$ test$\_$data$)$

# Append the result to the results

results.append$($result$)$

# Compute the average results and standard deviations

average$\_$results, standard$\_$deviations $=$ compute$\_$statistics$($results$)$

# Plot the results

plot$\_$results$($average$\_$results, standard$\_$deviations$)$