The above shows the output after applying a linear regression model into this dataset : https://drive.google.com/file/d/1XqsEnwsLQ7QN1Q4aD30DIJePmiljr1tL/view?usp=share_link

The dataset: https://drive.google.com/file/d/1XqsEnwsLQ7QN1Q4aD30DIJePmiljr1tL/view?usp=share_link

Retrieved from: https://data.world/data-society/air-traffic-passenger-data

Then, i used the following coding by adding log to the variable Passenger Count then i get the output as the above picture. But then, it seems like the model is not a good fit to the dataset. The model summary shows that the coefficient of determination (R-squared) is 0.006565, which is a measure of how well the model fits the data. This value indicates that the model is not a good fit for the data. Is the model doesn't fit well with the data? What can be done to fix this problem? Is there a need of changing to other type of machine learning model? Please investigate more on the model and the dataset! And the variables that are needed to be considered.

Please fix this problem and i use the following coding so please fix this code using R programming!

# Load data data

# Inspect data str(data) head(data)

# Check for missing values sum(is.na(data))

# Print the summary of the imported data summary(data)

# Visualizing the data library(ggplot2) ggplot(data, aes(x = Month, y = Passenger.Count, color = Year)) + geom_line() + ggtitle("Air Traffic Passenger Data")

# Create log transformation of 'Passengers' column data$log_Passenger.Count

# Splitting the data into training and test sets install.packages(c("caTools")) library(caTools) set.seed(123) #use 80% of dataset as training set and 20% as test set sample

# Make sure 'Month' variable has the same levels in both train_data and test_data training_data$Month

# Fit linear regression model using transformed data model2

#Checking the residuals residuals

# Compare model performance using test data prediction1 Ca 11 : 1m (formu 1a= log_Passenger. Count Month + Year, data = training_data) Residual standard error: 1.613 on 12010 degrees of freedom Mu7tiple R-squared: 0.006565, Adjusted R-squared: 0.005572 F-statistic: 6.614 on 12 and 12010 DF, p-value: 6.03 e-12