Abstract   
This project will analyze the web traffics to know users habits, improve the website performance and help to come up strategic decisions. In this digital age, web analytics is a must-read for businesses to understand their online façade and boost their digital tactics. This project is based on the data of Google Analytics. We perform an end-to-end analysis for a website using this data. 
The steps involved in the project are: Data collection, Iris data cleaning and preprocessing part. Exploratory data analysis (EDA) on iris speciesand visualization of EDA results In this phase, we gather the appropriate web traffic data from Google Analytics. before data analysis, the collected data needs to be refined in order to have proper accuracy and consistency. This stage is called removed from previous slide Data2Variables will use EDA techniques to reveal hidden insights and correlations that 
DatasetDieseShowsyclerview can’t directly determine 
To conclude this Web Analysis project enables to learn various important aspects of what makes website function as well it also demonstrates the continuous monitoring and analysis is essential in remaining competitive online platforms. This project’s findings and methodologies have laid down the groundwork for further research and progress in the area of web analytics. 

use this structure to make a abstract of my code
# Load required libraries
library(dplyr)
library(lubridate)
library(tidyr)
library(ggplot2)
library(sf)
library(leaflet)
library(htmlwidgets)
library(prophet)
library(RColorBrewer)
library(rworldmap)

# Assume that ‘CV_LatLon_21Jan_12Mar’ is the pre-loaded dataframe containing the COVID-19 data

# Clean data
data <- CV_LatLon_21Jan_12Mar %>% 
  mutate(
    state = replace_na(state, "Unknown"),
    country = replace_na(country, "Unknown"),  # Added country column handling
    date = as.Date(date, format = "%m/%d/%y"),  # Specify the date format
    lat = as.numeric(lat),
    lon = as.numeric(lon)
  ) %>% 
  filter(
    lat >= -90 & lat <= 90 & 
    lon >= -180 & lon <= 180 & 
    !(confirmed == 0 & recovered == 0 & death == 0)
  )

# Aggregate data by country
agg_data <- data %>%
  group_by(country) %>%
  summarise(
    total_confirmed = sum(confirmed),
    total_recovered = sum(recovered),
    total_deaths = sum(death)
  ) %>%
  ungroup()

# Visualizations
# Top countries by confirmed cases
top_countries_confirmed <- agg_data %>% 
  arrange(desc(total_confirmed)) %>% 
  head(10)
ggplot(top_countries_confirmed, aes(x=reorder(country, total_confirmed), y=total_confirmed)) +
  geom_bar(stat="identity", fill="steelblue") +
  coord_flip() +
  labs(title="Top 10 Countries by Confirmed Cases", x="Country", y="Total Confirmed Cases")

# Top countries by deaths
top_countries_deaths <- agg_data %>% 
  arrange(desc(total_deaths)) %>% 
  head(10)
ggplot(top_countries_deaths, aes(x=reorder(country, total_deaths), y=total_deaths)) +
  geom_bar(stat="identity", fill="red") +
  coord_flip() +
  labs(title="Top 10 Countries by Deaths", x="Country", y="Total Deaths")

# Top countries by recovered cases
top_countries_recovered <- agg_data %>% 
  arrange(desc(total_recovered)) %>% 
  head(10)
ggplot(top_countries_recovered, aes(x=reorder(country, total_recovered), y=total_recovered)) +
  geom_bar(stat="identity", fill="green") +
  coord_flip() +
  labs(title="Top 10 Countries by Recovered Cases", x="Country", y="Total Recovered Cases")

# Geospatial Analysis Using Leaflet
# Convert to spatial object
data_sf <- st_as_sf(data, coords = c("lon", "lat"), crs = 4326)

# Interactive Map of Confirmed Cases
m_confirmed <- leaflet(data_sf) %>%
  addTiles() %>%
  addCircleMarkers(
    radius = ~log1p(confirmed + 1),
    color = "blue",
    popup = ~paste0("<strong>Country: </strong>", country, "<br>",
                    "<strong>Confirmed Cases: </strong>", confirmed, "<br>",
                    "<strong>Recovered: </strong>", recovered, "<br>",
                    "<strong>Deaths: </strong>", death)
  ) %>%
  addLegend("bottomright", 
            colors = c("blue"),
            labels = c("Confirmed Cases"),
            opacity = 0.5,
            title = "COVID-19 Cases")
saveWidget(m_confirmed, "Confirmed_Cases_Map.html")

# Time Series Forecasting with Prophet
# Aggregate data by date
time_series_data <- data %>%
  group_by(date) %>%
  summarise(total_confirmed = sum(confirmed)) %>%
  ungroup()

# Handle NA values
time_series_data <- na.omit(time_series_data)

# First ensure your data is correctly structured
prophet_data <- time_series_data %>%
  rename(ds = date, y = total_confirmed)

# Fit the model with error handling
model <- tryCatch({
  prophet(prophet_data)  # Fit the prophet model
}, error = function(e) { 
  print("Error fitting model: ")
  print(e)
  return(NULL)  # Return NULL if an error occurs
})
# Disabling yearly seasonality. Run prophet with yearly.seasonality=TRUE to override this.
# Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.
# n.changepoints greater than number of observations. Using 23

# Proceed only if model fitting was successful
if (!is.null(model) && inherits(model, "prophet")) {
  # Create future dataframe
  future <- make_future_dataframe(model, periods = 30)
  future <- future %>%
    mutate(ds = as.Date(ds, format = "%Y-%m-%d"))
  
  # Predict and visualize
  forecast <- predict(model, future)
  plot(model, forecast)
  prophet_plot_components(model, forecast)
} else {
  print("Model fitting failed. Future dataframe and forecast were not created.")
}

# Create future dataframe
future <- make_future_dataframe(model, periods = 30)
future <- future %>%
  mutate(ds = as.Date(ds, format = "%Y-%m-%d")) # Ensure dates are in Date format
forecast <- predict(model, future)

# Visualize the forecast
if (exists("model") && !inherits(model, "error")) {
  plot(model, forecast)
  prophet_plot_components(model, forecast)
}

# Summary Statistics
summary_stats <- agg_data %>%
  summarise(
    total_confirmed = sum(total_confirmed),
    total_recovered = sum(total_recovered),
    total_deaths = sum(total_deaths),
    mean_confirmed = mean(total_confirmed),
    mean_recovered = mean(total_recovered),
    mean_deaths = mean(total_deaths)
  )
print(summary_stats)

# Heatmap
ggplot(data, aes(x = lon, y = lat)) +
  geom_hex(bins = 30) +
  scale_fill_gradient(low = "blue", high = "red") +
  labs(title="Heatmap of COVID-19 Cases",
       x="Longitude", y="Latitude")

# World Map Visualization Using Country
# Correct aggregation for country-level data
agg_data_country <- data %>%
  group_by(country) %>%
  summarise(
    total_confirmed = sum(confirmed),
    total_recovered = sum(recovered),
    total_deaths = sum(death)
  ) %>%
  ungroup()

# Join aggregated data with world map data using the country column
map_data <- joinCountryData2Map(agg_data_country, joinCode = "NAME", nameJoinColumn = "country")

# Plot the world map
par(mar = c(0, 0, 2, 0))
mapCountryData(map_data, nameColumnToPlot = "total_confirmed", 
               mapTitle = "Global Distribution of COVID-19 Confirmed Cases", 
               colourPalette = "heat")
btw the data is covid19 cases data from kaggle its geospatial analysis project