https://github.com/mariamagro/supervisedtools_statisticallearning

In this project a dataset had to be analysed using Supervised Learning and the tools provided by R libraries. Therefore, classification and advanced regression play a main role in this project with the creation of different models such as Benchmark, QDA, Lasso or kNN.
https://github.com/mariamagro/supervisedtools_statisticallearning

Last synced: about 1 month ago
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In this project a dataset had to be analysed using Supervised Learning and the tools provided by R libraries. Therefore, classification and advanced regression play a main role in this project with the creation of different models such as Benchmark, QDA, Lasso or kNN.

• Host: GitHub
• URL: https://github.com/mariamagro/supervisedtools_statisticallearning
• Owner: mariamagro
• Created: 2023-11-07T19:21:30.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-08-18T16:48:15.000Z (5 months ago)
• Last Synced: 2024-08-18T18:09:08.018Z (5 months ago)
• Language: RMarkdown
• Homepage:
• Size: 1.04 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Supervised tools: House Rent Prediction

**Author:** María Ángeles Magro Garrote  

**Year:** 2022

## Overview

This project applies supervised learning techniques to predict house rents using a dataset of house rental listings. The project involves both classification and regression approaches to predict the target variable: the rent of houses. The dataset includes various predictors which are explored to understand their relationship with the target variable.

### Dataset Description

The dataset used in this project was created by Sourav Banerjee. You can access it [here](https://www.kaggle.com/datasets/iamsouravbanerjee/house-rent-prediction-dataset).

## R libraries installation

To run the analysis, ensure you have the following R libraries installed:

```r

c("VIM", "tidyverse", "MASS", "caret", "e1071", "GGally", "glmnet", "pROC", "randomForest", "rpart", "rpart.plot", "rattle", "naivebayes", "leaflet")

```

## The analysis

### Data preprocessing

- Cleaning

- Feature engineering

  

### Visualations

### Classification

- LDA

- QDA

- Benchmark 

- ROC Curve

- Decision tree

- Random forest

- Multinomial Naive-Bayes classification

- Tuning

### Advanced regression

- Benchmark

- caret linear model

- linear regression

- Lasso

- kNN

## Final Insights

The conclusions of this analysis can be seen in the notebook (Rmd) or HTML.