# Load the data
laptop_data <- read.csv("/Users/revathiyajjavarapu/Documents/statistics(1)/laptop_prices.csv")
head(laptop_data)
##   Company     Product  TypeName Inches Ram         OS Weight Price_euros
## 1   Apple MacBook Pro Ultrabook   13.3   8      macOS   1.37     1339.69
## 2   Apple Macbook Air Ultrabook   13.3   8      macOS   1.34      898.94
## 3      HP      250 G6  Notebook   15.6   8      No OS   1.86      575.00
## 4   Apple MacBook Pro Ultrabook   15.4  16      macOS   1.83     2537.45
## 5   Apple MacBook Pro Ultrabook   13.3   8      macOS   1.37     1803.60
## 6    Acer    Aspire 3  Notebook   15.6   4 Windows 10   2.10      400.00
##     Screen ScreenW ScreenH Touchscreen IPSpanel RetinaDisplay CPU_company
## 1 Standard    2560    1600          No      Yes           Yes       Intel
## 2 Standard    1440     900          No       No            No       Intel
## 3  Full HD    1920    1080          No       No            No       Intel
## 4 Standard    2880    1800          No      Yes           Yes       Intel
## 5 Standard    2560    1600          No      Yes           Yes       Intel
## 6 Standard    1366     768          No       No            No         AMD
##   CPU_freq      CPU_model PrimaryStorage SecondaryStorage PrimaryStorageType
## 1      2.3        Core i5            128                0                SSD
## 2      1.8        Core i5            128                0      Flash Storage
## 3      2.5  Core i5 7200U            256                0                SSD
## 4      2.7        Core i7            512                0                SSD
## 5      3.1        Core i5            256                0                SSD
## 6      3.0 A9-Series 9420            500                0                HDD
##   SecondaryStorageType GPU_company              GPU_model
## 1                   No       Intel Iris Plus Graphics 640
## 2                   No       Intel       HD Graphics 6000
## 3                   No       Intel        HD Graphics 620
## 4                   No         AMD         Radeon Pro 455
## 5                   No       Intel Iris Plus Graphics 650
## 6                   No         AMD              Radeon R5
dim(laptop_data)
## [1] 1275   23
# Summarize numeric column 'Price_euros'

price_summary <- summary(laptop_data$Price_euros)
#(laptop_data$Price_euros) provides summary of numeric column like min, max, mean, median
price_quantiles <- quantile(laptop_data$Price_euros)
#(laptop_data$Price_euros) gives quantiles such as 25%, 50%, 100%

# Summarize categorical column 'Company'
company_summary <- table(laptop_data$Company)
#(laptop_data$Company) provides count of each unique value in company column

# Print results
print("Summary of Price_euros:")
## [1] "Summary of Price_euros:"
print(price_summary)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     174     609     989    1135    1496    6099
print("Quantiles of Price_euros:")
## [1] "Quantiles of Price_euros:"
print(price_quantiles)
##     0%    25%    50%    75%   100% 
##  174.0  609.0  989.0 1496.5 6099.0
print("Summary of Company counts:")
## [1] "Summary of Company counts:"
print(company_summary)
## 
##      Acer     Apple      Asus     Chuwi      Dell   Fujitsu    Google        HP 
##       101        21       152         3       291         3         3       268 
##    Huawei    Lenovo        LG  Mediacom Microsoft       MSI     Razer   Samsung 
##         2       289         3         7         6        54         7         9 
##   Toshiba      Vero    Xiaomi 
##        48         4         4

How does the screen size is affecting the prices?

#Columns - Inches, Price_euros #goal- Determine how inches is affecting the price of laptop as high inches gets high price

How does CPU performance impact laptop prices?

#Columns- CPU_freq, CPU_model, Price_euros #goal- Explore whether higher-end CPUs drive the cost of laptops

Is there relation between screen size,laptop weight and laptop price?

Columns - inches, price_euros, Weight

#Goal - determine how different form factors affect laptop prices

# Aggregating the data: Group by 'Company' and calculate the mean of 'Price_euros'
company_price_mean <- aggregate(Price_euros ~ Company, data = laptop_data, FUN = mean)
#(Price_euros ~ Company, data = laptop_data, FUN = mean) -> It means group by Company column and apply mean function to price_euros

# Print the result
print(company_price_mean)
##      Company Price_euros
## 1       Acer    633.4645
## 2      Apple   1564.1986
## 3       Asus   1123.8297
## 4      Chuwi    314.2967
## 5       Dell   1199.2251
## 6    Fujitsu    729.0000
## 7     Google   1677.6667
## 8         HP   1080.3147
## 9     Huawei   1424.0000
## 10    Lenovo   1093.8622
## 11        LG   2099.0000
## 12  Mediacom    295.0000
## 13 Microsoft   1612.3083
## 14       MSI   1728.9081
## 15     Razer   3346.1429
## 16   Samsung   1413.4444
## 17   Toshiba   1267.8125
## 18      Vero    217.4250
## 19    Xiaomi   1133.4625
# Load required libraries
library(ggplot2)


# 1. Distribution of Laptop Prices with Company categories
ggplot(laptop_data, aes(x = Price_euros, fill = Company)) +
  geom_histogram(binwidth = 200, alpha = 0.7) +
  labs(title = "Distribution of Laptop Prices by Company", x = "Price (Euros)", y = "Count") +
  theme_minimal()

#This plot uses Histogram to show Distribution of Laptop Prices
#fill = Company adds colour channel to visualize how prices are distributed among the brands

# 2. Scatter plot of Screen Size vs. Price with OS categories
ggplot(laptop_data, aes(x = Inches, y = Price_euros, color = OS)) +
  geom_point(alpha = 0.6) +
  labs(title = "Screen Size vs. Price by Operating System", x = "Screen Size (Inches)", y = "Price (Euros)") +
  theme_minimal()

# This plot shows the relationship between size and prices
# color = OS adds color channel to show how operating system category affects this relationship