Rainfall_prediction_model/PreprocessingandDataTransformation.R at main · TrevorA17/Rainfall_prediction_model · GitHub

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#134780 4.2 C
# Load dataset
WeatherData <- read.csv("data/Weather.csv", colClasses = c(
  MinTemp = "numeric",
  MaxTemp = "numeric",
  Rainfall = "numeric",
  Evaporation = "numeric",
  Sunshine = "numeric",
  WindGustDir = "factor",
  WindGustSpeed = "numeric",
  WindDir9am = "factor",
  WindDir3pm = "factor",
  WindSpeed9am = "numeric",
  WindSpeed3pm = "numeric",
  Humidity9am = "numeric",
  Humidity3pm = "numeric",
  Pressure9am = "numeric",
  Pressure3pm = "numeric",
  Cloud9am = "numeric",
  Cloud3pm = "numeric",
  Temp9am = "numeric",
  Temp3pm = "numeric",
  RainToday = "factor",
  RISK_MM = "numeric",
  RainTomorrow = "factor"
))

# Define levels for categorical columns
wind_dir_levels <- c("N", "NE", "E", "SE", "S", "SW", "W", "NW")
rain_levels <- c("No", "Yes")

# Update factor columns with levels
WeatherData$WindGustDir <- factor(WeatherData$WindGustDir, levels = wind_dir_levels)
WeatherData$WindDir9am <- factor(WeatherData$WindDir9am, levels = wind_dir_levels)
WeatherData$WindDir3pm <- factor(WeatherData$WindDir3pm, levels = wind_dir_levels)
WeatherData$RainToday <- factor(WeatherData$RainToday, levels = rain_levels)
WeatherData$RainTomorrow <- factor(WeatherData$RainTomorrow, levels = rain_levels)

View(WeatherData)

# Check for missing values in the entire dataset
missing_values <- any(is.na(WeatherData))

# Display the result
if (missing_values) {
  cat("There are missing values in the dataset.\n")
} else {
  cat("There are no missing values in the dataset.\n")
}

#install mice package for multiple imputation
install.packages("mice")
library(mice)

# Impute missing values in numeric columns using mean imputation
numeric_cols <- sapply(WeatherData, is.numeric)
imputed_data_numeric <- complete(mice(WeatherData[, numeric_cols]))

# Impute missing values in categorical columns using mode imputation
categorical_cols <- sapply(WeatherData, is.factor)
imputed_data_categorical <- complete(mice(WeatherData[, categorical_cols]))

# Combine the imputed numeric and categorical datasets
imputed_data <- cbind(imputed_data_numeric, imputed_data_categorical)

# Check if there are still missing values in the imputed dataset
missing_values_after_imputation <- any(is.na(imputed_data))

# Display the result
if (missing_values_after_imputation) {
  cat("There are still missing values after imputation.\n")
} else {
  cat("All missing values have been successfully imputed.\n")
}

#replace WeatheData dataset with imputed dataset
# Weather <- imputed_data

# Standardize the Numeric variables that could be used for prediction
numeric_vars <- c("MaxTemp", "MinTemp", "Rainfall", "Sunshine")

# Standardize the numeric variables
Weather_standardized <- WeatherData
Weather_standardized[numeric_vars] <- scale(WeatherData[numeric_vars])
# Assuming your dataset is already loaded and named "WeatherData"
# Assuming you want to standardize "MaxTemp," "MinTemp," "Rainfall," and "Sunshine"

# Numeric variables to be standardized
numeric_vars <- c("MaxTemp", "MinTemp", "Rainfall", "Sunshine")

# Standardize the numeric variables
Weather_standardized <- WeatherData
Weather_standardized[numeric_vars] <- scale(WeatherData[numeric_vars])

# Load required packages
library(ggplot2)

# Function to create histograms for original MaxTemp and standardized MaxTemp variables
create_histograms <- function(original_data, standardized_data, var) {
  original_plot <- ggplot(original_data, aes(x = !!sym(var))) +
    geom_histogram(binwidth = 1, fill = "blue", color = "black", alpha = 0.7) +
    labs(title = paste("Histogram of Original", var), x = var, y = "Frequency")

  standardized_plot <- ggplot(standardized_data, aes(x = !!sym(var))) +
    geom_histogram(binwidth = 0.2, fill = "orange", color = "black", alpha = 0.7) +
    labs(title = paste("Histogram of Standardized", var), x = var, y = "Frequency")

  return(list(original_plot = original_plot, standardized_plot = standardized_plot))
}

# Create histograms for original and standardized variables
histograms <- create_histograms(WeatherData, Weather_standardized, numeric_vars[1])

# Display the histograms separately
original_plot <- histograms$original_plot
standardized_plot <- histograms$standardized_plot

print(original_plot)
print(standardized_plot)