The provided dataset contains daily observations from numerous weather stations in Australia. (Daily observations are available from the Australian Government's Bureau of Meteorology.) The variables included here are:

Date the date of the observation

Location the common name of the location of the weather station

MinTemp the minimum temperature in degrees Celsius

MaxTemp the maximum temperature in degrees Celsius

Rainfall the amount of rainfall recorded for the day in mm

Evaporation the Class A pan evaporation (mm) in the 24 hours prior to 9AM

Sunshine the number of hours of bright sunshine in the day

WindGustDir the direction of the strongest wind gust in the 24 hours prior to midnight

WindGustSpeed the speed (km/h) of the strongest wind gust in the 24 hours prior to midnight

WindDir9am direction of the wind at 9AM

WindDir3pm direction of the wind at 3PM

WindSpeed9am wind speed (km/h) averaged over the 10 minutes prior to 9AM

WindSpeed3pm wind speed (km/h) averaged over the 10 minutes prior to 3PM

Humidity9am humidity (%) at 9AM

Humidity3pm humidity (%) at 3PM

Pressure9am atmospheric pressure (hPa) reduced to mean sea level at 9AM

Pressure3pm atmospheric pressure (hPa) reduced to mean sea level at 3PM

Cloud9am fraction of sky obscured by clouds at 9AM (This is measured in oktas, which are a unit of eighths. It records how many eighths of the sky are obscured by clouds. 0 indicates a complete clear sky, while 8 indicates a completely overcast sky.)

Cloud3pm fraction of sky obscured by clouds at 3PM (oktas)

Temp9am temperature (degrees C) at 9AM

Temp3pm temperature (degrees C) at 3PM

RainToday 1 if precipitation in the 24 hours prior to 9AM exceeds 1mm, 0 otherwise

RainTomorrow Did it rain the next day? (1 if yes, 0 otherwise)

Within RStudio(Show Code)

1. Suppose the ultimate goal of a project is to use this dataset to predict whether it will rain the next day (RainTomorrow). Apply the EDA techniques youve learned to explore this dataset.

2. An overview of the dataset with a brief description of each variable

3. Summary statistics table for all numeric variables and a summary of what you learn from these summary statistics to include:

• How much (if any) data is missing from which variables
• Which variables (if any) are highly skewed
• Which variables (if any) contain outliers
• Which variables (if any) appear to have erroneous data