2.Data Collection 
The data for this project was sourced from Kaggle and contains comprehensive records of COVID-
19 cases. The dataset includes various vital statistics such as the number of confirmed cases,
 recoveries, and deaths, along with associated geographical coordinates.
  
Output: 
  
  
 
3. Exploratory Data Analysis (EDA) 
EDA involves summarizing the main characteristics of the dataset, often with visual methods. It helps in understanding the data distribution and identifying patterns or anomalies. 
  
Output: 
  
4. Visualizations 
	1. 	Distribution of Session Duration	
			
  
  
 
 
 
2.	 Trend of Daily Active Users	 
  
  
 
 
 
3.Traffic Sources 
  
  
 
 
 
4.Device Type Distribution 
  
  
   
   
5.User Type Distribution 
 
  
6.Bounce Rate Distribution 
  
 
 
Chapter – 4 
Advantages and Applications 
Advantages of Web Analysis 
1.Improved User Experience 
Identifies areas for website improvement by analyzing user behavior. 
Helps understand navigation issues through metrics like bounce rate and session duration. 
 
2.Increased Conversion Rates 
Optimizes marketing campaigns by providing insights into user engagement. 
Allocates marketing budget effectively by identifying high-conversion traffic sources. 
 
3.Enhanced Content Strategy 
Guides content creation by analyzing most viewed and shared content. 
Determines engaging topics through metrics like page views and time on page. 
 
4.Targeted Marketing 
Enables personalized marketing by segmenting audience based on behavior and demographics. 
Tailors efforts to specific groups using user demographics and traffic sources. 
 
5.Informed Decision Making 
Supports strategic decisions with data-driven insights across departments. 
Adapts quickly to changing behaviors with real-time data. 
  
 
 
  
 
 
 
Applications of Web Analysis: 
1.E-commerce 
Tracks customer behavior, manages inventory, and optimizes product listings. 
Enhances shopping experience through user interaction analysis. 
 
2.Digital Marketing 
Measures campaign effectiveness and audience engagement. 
Improves SEO, social media, and PPC strategies. 
 
3.Content Management 
Gauges content performance and identifies popular topics. 
Optimizes content distribution channels. 
 
  
 
Chapter – 5 
Conclusions   
Web analysis is a powerful tool that offers significant benefits across various industries. By leveraging data-driven insights, businesses can enhance user experience, optimize conversion rates, and tailor their marketing strategies to better meet the needs of their audience. The ability to track and analyze user behavior allows for continuous improvement of website performance and content strategy, leading to more effective decision-making and higher returns on investment. Moreover, web analysis supports targeted marketing efforts, ensuring that resources are allocated efficiently to maximize impact. 
 
In summary, web analysis provides a comprehensive understanding of how users interact with websites, enabling businesses to make informed decisions that drive growth and improve customer satisfaction. The integration of web analysis into business strategies is no longer optional but essential for staying competitive in today's digital landscape. 
Future Scope 
The future of web analysis holds immense potential with the continuous advancements in technology and data science. Here are some areas where web analysis is expected to evolve: 
Artificial Intelligence and Machine Learning: AI and ML will play a crucial role in predictive analytics, helping businesses anticipate user behavior and trends more accurately. 
 
Real-time Analytics: The demand for real-time data will increase, allowing businesses to make immediate adjustments to their strategies based on live user interactions. 
 
Enhanced Personalization: Advanced analytics will enable deeper personalization, providing users with highly tailored experiences based on their behavior, preferences, and demographics. 
 
Integration with IoT: As the Internet of Things (IoT) continues to expand, web analysis will integrate with various connected devices, offering a more holistic view of user interactions across multiple platforms. 

in same structure and size generate for geospatial analysis in R language 
for this 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")
"