R-programming-Assesment.

Bike Rental Prediction Model

Introduction

This project focuses on predicting bike rentals using a random forest algorithm in R. The dataset contains information about daily bike rentals, including various attributes such as weather conditions, temperature, and the total number of bikes rented.

Tasks

Task 1: Exploratory Data Analysis

• Load the dataset using the readxl package.
• Perform data type conversion for attributes, specifically converting the "dteday" attribute to a Date type.
• Conduct missing value analysis; no missing values were found in the dataset.

Task 2: Attributes Distribution and Trends

• Visualize the monthly and yearly distribution of bikes rented using ggplot2.
• Create boxplots for outliers' analysis.

Task 3: Data Splitting

• Split the dataset into training and testing sets using the caret package.

Task 4: Random Forest Model Creation

• Create a random forest model using the randomForest package.
• Print the model summary.

Task 5: Model Evaluation

• Make predictions on the test set and calculate Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared for model evaluation.
• Test whether the model is overfitting by comparing performance on the training and test sets.

Task 6: Model with Regularization

• Train the model with regularization to improve generalization.
• Evaluate the performance of the regularized model.

Task 7: Hyperparameter Tuning

• Combine features and target into a data frame for hyperparameter tuning.
• Set up cross-validation and tune hyperparameters using random search.
• Assess the performance of the tuned model on the test set.

Files

• your_script.R: R script containing the code for data analysis and model building.
• your_dataset.xlsx: Dataset used for analysis.

Usage

1. Install the required packages by running the package installation commands.
2. Load the dataset using file.choose() and follow the provided tasks in the script.