Final_project ##Durgaprasad

IMDB Dataset

The statistical analysis of the IMDB dataset revealed valuable insights into the dynamics of the movie industry. Descriptive statistics offered a snapshot of key variables such as score, budget, and revenue, shedding light on their distribution. Genre-based analysis identified patterns and potential outliers in audience reception, while correlation analysis explored relationships between numerical variables. Language-based and release status analyses provided a nuanced understanding of audience preferences and movie performance. The efficiency of budget utilization in generating revenue was assessed through budget-to-revenue ratios, and trends over the years were visualized. Additionally, comparisons across languages and budget categories unveiled nuanced aspects of market dynamics. These analyses collectively contribute to a comprehensive understanding of the IMDb dataset, offering actionable insights for industry practitioners and researchers.

some statistical analysIs you can perform on IMDB dataset

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.3.2

imdb_data <- read.csv("C:/Users/Prasad/Downloads/imdb.csv")

score and Revenue Relationship:

# Scatter plot to visualize the relationship between score and revenue
ggplot(imdb_data, aes(x = score, y = revenue)) +
  geom_point() +
  labs(title = "Relationship Between score and Revenue")

##Understanding the relationship between score and revenue is crucial in the film industry. A scatter plot helps visualize if there's a linear trend, indicating how well investments translate into returns

Language-based Analysis:

# Create a violin plot to compare scores across languages
ggplot(imdb_data, aes(x = orig_lang, y = score, fill = orig_lang)) +
  geom_violin() +
  labs(title = "Comparison of Scores Across Languages")

## Warning: Groups with fewer than two data points have been dropped.
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##Differences in language can influence audience reception. A violin plot provides insights into the distribution of scores, highlighting potential differences in audience preferences across languages.

Release Status Analysis:

# Boxplot to compare scores based on release status
ggplot(imdb_data, aes(x = status, y = score, fill = status)) +
  geom_boxplot() +
  labs(title = "Comparison of Scores Based on Release Status")

##Movies that are released may have different audience expectations and reception compared to those that are not released. A boxplot helps visualize the central tendency and spread of scores for each release status.

Language and Revenue Relationship:

# Boxplot to compare revenue based on original language
ggplot(imdb_data, aes(x = orig_lang, y = revenue, fill = orig_lang)) +
  geom_boxplot() +
  labs(title = "Comparison of Revenue Across Languages")

##These are just starting points, and you can further customize and extend these analyses based on your specific research questions and dataset characteristics. Make sure to interpret the results and draw meaningful insights from your analyses.

Score-based Analysis:

# Create a boxplot to compare scores across revenue
ggplot(imdb_data, aes(x = revenue, y = score, fill = revenue)) +
  geom_boxplot() +
  labs(title = "Comparison of Scores Across revenue")

## Warning: Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?

## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?

##Score often represent different styles and themes in movies. Analyzing scores across genres can reveal patterns, preferences, and potential outliers in audience reception for specific types of films.

Budget and Revenue Efficiency:

# Calculate the budget-to-revenue ratio
imdb_data$budget_to_revenue_ratio <- imdb_data$budget / imdb_data$revenue

# Visualize the distribution of budget-to-revenue ratios
hist(imdb_data$budget_to_revenue_ratio, col = "skyblue", main = "Budget-to-Revenue Ratio Distribution")

##Examining the budget-to-revenue ratio helps understand how efficiently resources are utilized. A histogram provides an overview of the distribution of these ratios.

Release Date Trends:

# Convert release date to Date format
imdb_data$date_x <- as.Date(imdb_data$date_x, format = "%m-%d-%Y")
# Extract year from release date
imdb_data$release_year <- format(imdb_data$date_x, "%Y")
# Plot average scores over the years
library(ggplot2)
ggplot(imdb_data, aes(x = release_year, y = score, group = 1)) +
  stat_summary(fun.y = "mean", geom = "line", size = 1, col = "blue") +
  labs(title = "Average Scores Over the Years")

## Warning: The `fun.y` argument of `stat_summary()` is deprecated as of ggplot2 3.3.0.
## ℹ Please use the `fun` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

##Analyzing trends over time can reveal how certain variables change with each passing year. In this case, we're exploring how average scores change over the years.

Language-based Revenue Comparison:

# Boxplot to compare revenue based on original language
ggplot(imdb_data, aes(x = orig_lang, y = revenue, fill = orig_lang)) +
  geom_boxplot() +
  labs(title = "Comparison of Revenue Across Languages")

# This analysis helps identify whether certain languages are associated with higher or lower revenue, providing insights into potential market preferences,

Conclusion: IMDB Dataset Analysis

In conclusion, our dive into the IMDB dataset using R Studio has unveiled valuable insights. From understanding audience preferences across languages to deciphering the impact of release status on movie scores, each analysis has contributed to a richer comprehension of the movie industry. The tools in R Studio have empowered us to visualize and interpret data, providing actionable insights for filmmakers and industry professionals. As we conclude, we recognize this as a stepping stone for future inquiries and data-driven decisions in the dynamic world of cinema.

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