what I will do in this project SIU

dataset link: https://www.kaggle.com/datasets/shibumohapatra/house-price

Introduction To The Data

Summary Statistics Table

psych::describe(df) |> round(2) |> kable()
vars n mean sd median trimmed mad min max range skew kurtosis se
housing_median_age 1 20640 28.64 12.59 29.00 28.49 14.83 1.0 52 51.0 0.06 -0.80 0.09
total_rooms 2 20640 2635.76 2181.62 2127.00 2294.56 1181.63 2.0 39320 39318.0 4.15 32.62 15.19
total_bedrooms 3 20433 537.87 421.39 435.00 471.44 240.18 1.0 6445 6444.0 3.46 21.98 2.95
population 4 20640 1425.48 1132.46 1166.00 1256.51 652.34 3.0 35682 35679.0 4.94 73.53 7.88
households 5 20640 499.54 382.33 409.00 441.20 223.87 1.0 6082 6081.0 3.41 22.05 2.66
median_income 6 20640 3.87 1.90 3.53 3.65 1.58 0.5 15 14.5 1.65 4.95 0.01
median_house_value 7 20640 206855.82 115395.62 179700.00 192773.00 101409.84 14999.0 500001 485002.0 0.98 0.33 803.22

Data Distribution Visualization

graph1 <- ggplot(df, aes(c(x = median_house_value))) +
  geom_density()+
  labs(
    title = 'Name',
    x = 'x-axis',
    y = 'count'
  )
renderPlotly(graph1)
#Make the button for this one

You can enable figure captions by fig_caption: yes in YAML:

output:
  rmarkdown::html_vignette:
    fig_caption: yes

Then you can use the chunk option fig.cap = "Your figure caption." in knitr.

Correlation Examination

You can write math expressions, e.g. \(Y = X\beta + \epsilon\), footnotes1, and tables, e.g. using knitr::kable().

mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4

Pair Plot

Linear Regression

Conclusion

Data Source


  1. A footnote here.↩︎