https://github.com/rafath0ssain/predihome

Data analysis using economic factors affecting living conditions across Canadian provinces.
https://github.com/rafath0ssain/predihome

data-analysis data-visualization dplyr ggplot2 graph kaggle linear-regression prediction-model r shiny tidyr

Last synced: about 2 months ago
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Data analysis using economic factors affecting living conditions across Canadian provinces.

• Host: GitHub
• URL: https://github.com/rafath0ssain/predihome
• Owner: RafatH0ssain
• Created: 2025-01-03T18:44:47.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-05T21:26:39.000Z (over 1 year ago)
• Last Synced: 2025-04-07T06:33:08.613Z (about 1 year ago)
• Topics: data-analysis, data-visualization, dplyr, ggplot2, graph, kaggle, linear-regression, prediction-model, r, shiny, tidyr
• Language: R
• Homepage:
• Size: 1.38 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# PrediHome: Housing Price Index and Employment Rate Analytics

**PrediHome** is an interactive web app built using **R** and **Shiny**, designed to help users make informed decisions about the best province to live in Canada. The app provides detailed analytics on **housing price data (HPI)**, **unemployment rate**, and **employment rate** data for the years **1986-2035**. Users can input a specific year and receive insights and predictions about the HPI and employment metrics across different provinces.

## Objective

The primary goal of **PrediHome** is to help users evaluate potential provinces to live in based on key economic and housing indicators. Users can:

- Explore **historical data (1986-2022)** and **predicted data (2023-2035)**.

- View the province with the **lowest Housing Price Index (HPI)** and the province with the **lowest unemployment rate**.

- **Visualize** HPI and employment statistics for different provinces, assisting in comparing various factors that impact the cost of living.

## Features

1. **User Input**:

   - Users select a year between **1986** and **2035** to analyze housing prices and employment statistics.

   

2. **Data Analysis**:

   - The app identifies the **lowest HPI** province and **lowest unemployment rate** province using both historical data and predictive models (for the years 2023-2035).

   

3. **Visualizations**:

   - **Bar Chart**: Displays the **HPI** values for each province in the selected year.

   - **Bar Chart**: Shows the **unemployment rate** for each province in the selected year.

4. **Regression Model Predictions (for years 2023-2035)**:

   - Uses linear regression models to predict future **HPI** and **unemployment rates** based on historical data.

   

5. **Identifies the best province to live in**:

- Calculates a composite score for each province, where a higher score indicates a better province to live in. This is done based on the following metrics:

  - Housing Price Index (HPI)

  - Unemployment Rate

  - Employment Rate

## Datasets

The analysis is based on the following datasets from Kaggle:

1. [**Canada housing price data by regions 1981-2022**](https://www.kaggle.com/datasets/anki112279/canada-housing-price-data-by-regions-19812022): Contains data on the housing price index for each province in Canada between 1981 and 2022.

2. [**Unemployment in Canada, by Province (1976-Present)**](https://www.kaggle.com/datasets/pienik/unemployment-in-canada-by-province-1976-present): Includes employment and unemployment rates for each province between 1976 and 2022.

## Steps Involved

### 1. **Data Preprocessing**:

   - Cleaned and prepared the datasets by removing unnecessary columns and aggregating data at the **province** level for comparison.

### 2. **Data Analysis**:

   - **Historical Data (1986-2022)**: Analyzed trends in **HPI** and **unemployment rates** across provinces.

   - **Prediction for Future Years (2023-2035)**: Applied regression models to predict future values of **HPI** and **unemployment rates**.

### 3. **Data Visualization**:

   The following visualizations help users understand and compare housing and employment metrics across provinces:

#### 1. **Bar Chart**: Housing Price Index (HPI) by Province

   - A bar chart showing **HPI** values for each province in the selected year.

   - Helps users visually compare housing prices and identify affordable or expensive provinces for that year.

   - The bars have **continuous color coding** that reflects the HPI values.

   ![Bar Chart: Housing Price Index](plot-examples/lowest_hpi_2027.png)

#### 2. **Bar Chart**: Unemployment Rates by Province

   - A bar chart displaying the **unemployment rate** for each province in the selected year.

   - Helps users analyze the economic climate in different provinces and make informed decisions about employment opportunities.

   - The bars have **continuous color coding** that reflects the unemployment rates.

   ![Bar Chart: Unemployment Rates](plot-examples/lowest_unemployment_rate_2027.png)

## User Interface

**PrediHome** features a modern, easy-to-navigate user interface that includes:

- **Numeric Input**: Users can select a year between **1986** and **2035**.

- **Submit Button**: After selecting the year, users can click the submit button to receive:

  - The overall **best province** to live in.

  - The province with the **lowest HPI**.

  - The province with the **lowest unemployment rate**.

  - **Visualizations**: A bar chart showing the HPI values of all provinces and one showing unemployment rates for all provinces.

  ![User Interface](webpage.jpeg)

## Tech stack

- **R**: For data cleaning, manipulation, and analysis.

- **Shiny**: For building the interactive web application.

- **ggplot2**: For creating visualizations like bar charts.

- **dplyr**: For data manipulation and aggregation.

- **tidyr**: For tidying and reshaping the data.

- **lm()**: For building regression models to predict future data.

## How to Run the App in RStudio

1. Install the necessary R packages:

   ```r

   install.packages(c("shiny", "ggplot2", "dplyr", "tidyr"))

2. Clone or download the repository.

3. Open the R script and run the app.

4. Enter a year (1986-2035) to view the analysis and predictions.

- **Note**: If you see an error that says "*cannot open file '': No such file or directory*", run `setwd("")` in your terminal, replacing  with the path to the directory containing the files you cloned.

## Authors

1. - Name: Nafisah Nubah

   - Email: nafisahnubah@gmail.com

   

2. - Name: Muhammad Rafat Hossain

   - Email: rafat.click.hossain@gmail.com