https://github.com/derrickbaruga7/r-linear-model

This R project focuses on predicting housing prices based on various features such as building area, number of rooms, and more. The dataset used is cleaned and analyzed to build a predictive model using linear regression.
https://github.com/derrickbaruga7/r-linear-model

data-science linear-modeling r visualization

Last synced: about 2 months ago
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This R project focuses on predicting housing prices based on various features such as building area, number of rooms, and more. The dataset used is cleaned and analyzed to build a predictive model using linear regression.

• Host: GitHub
• URL: https://github.com/derrickbaruga7/r-linear-model
• Owner: derrickbaruga7
• Created: 2024-07-26T14:23:10.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-07-26T14:29:47.000Z (6 months ago)
• Last Synced: 2024-07-26T16:07:52.246Z (6 months ago)
• Topics: data-science, linear-modeling, r, visualization
• Language: R
• Homepage:
• Size: 3.91 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# R-Linear-Model

This R project focuses on predicting housing prices based on various features such as building area, number of rooms, and more. The dataset used is cleaned and analyzed to build a predictive model using linear regression.

Requirements

R version 4.3.1 or later

R packages:

tidyverse 2.0.0

tidymodels 1.1.1

Installation

Install the required R packages using the following commands:

install.packages("tidyverse")

install.packages("tidymodels")

Usage

1. Load the necessary libraries:

library(tidyverse)

library(tidymodels)

2. Clean the data by removing rows with missing values:

housing_data_clean <- drop_na(housing_data)

3. Examine the correlation between numeric features:

cor(housing_data_clean[, sapply(housing_data_clean, is.numeric)])

4. Create scatter plots for initial visualization:

ggplot(housing_data_clean, aes(x = BuildingArea, y = Price)) +

  geom_point() +

  labs(title = "Price vs Building Area")

5. Split the data into training and testing sets:

set.seed(123)

data_split <- initial_split(housing_data_clean, prop = 0.75)

train_data <- training(data_split)

test_data <- testing(data_split)

6. Identify and remove outliers based on calculated fences:

calculate_fences <- function(data, column) {

  Q1 <- quantile(data[[column]], 0.25)

  Q3 <- quantile(data[[column]], 0.75)

  IQR <- Q3 - Q1

  list(lower = Q1 - 1.5 * IQR, upper = Q3 + 1.5 * IQR)

}

price_fences <- calculate_fences(train_data, "Price")

building_area_fences <- calculate_fences(train_data, "BuildingArea")

train_data <- train_data %>%

  filter(Price > price_fences$lower, Price < price_fences$upper) %>%

  filter(BuildingArea > building_area_fences$lower, BuildingArea < building_area_fences$upper)

7. Build the linear regression model:

model <- lm(Price ~ BuildingArea, data = train_data)

8. Predict and visualize the results:

test_data$Predicted_Price <- predict(model, test_data)

ggplot(test_data, aes(x = BuildingArea, y = Price)) +

  geom_point(aes(color = "Actual")) +

  geom_point(aes(y = Predicted_Price, color = "Predicted")) +

  labs(title = "Actual vs Predicted Prices")

  

License

This project is licensed under the terms of the R Foundation for Statistical Computing.