Git commits for the collaborative group project

Group_Default-R/Default-R.Rproj

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+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX

Group_Default-R/main.R

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+# Default-R project by 965
+# Dependencies
+install.packages(c("caTools", "glm2", "caret", "randomForest", "pROC", "corrplot", "MASS", "car", "carData", "farver"))
+
+library(caTools)
+library(glm2)
+library(caret)
+library(randomForest)
+library(pROC)
+library(corrplot)
+library(MASS)
+library(car)
+library(farver)
+
+# Preprocessing data
+weather_data <- read.csv("data.csv")
+weather_data$rain_numeric <- ifelse(weather_data$Rain == "rain", 1, 0)
+# str(weather_data)
+
+# Split data sheet
+set.seed(841524)
+split <- sample.split(weather_data$rain_numeric, SplitRatio = 0.7)
+train_data <- subset(weather_data, split == TRUE)
+test_data <- subset(weather_data, split == FALSE)
+
+# Constructing Model & Predict & Evaluate
+'model <- glm(train_data$rain_numeric ~ train_data$Temperature + 
+               train_data$Pressure + train_data$Cloud_Cover + 
+               train_data$Wind_Speed + train_data$Humidity, 
+               data = train_data, family = binomial)'
+
+model <- randomForest(train_data$rain_numeric ~ ., data = train_data, class.weight = c(0, 2))
+
+predictions <- predict(model, newdata = test_data, type = "response")
+
+confusionMatrix(as.factor(ifelse(predictions > 0.5, 1, 0)), 
+                as.factor(test_data$rain_numeric))
+
+plot(model)
+
+roc_obj <- roc(test_data$rain, predictions)
+plot(roc_obj)
+
+# Step Model & Extract Main factor
+full_model <- lm(weather_data$rain_numeric ~ weather_data$Temperature + 
+                 weather_data$Pressure + weather_data$Cloud_Cover + 
+                 weather_data$Wind_Speed + weather_data$Humidity, 
+                 data = weather_data)
+
+step_model <- step(full_model, direction = "both")
+
+summary(step_model)
+
+influencePlot(step_model)