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Global Population Analysis API

Project Overview

This project analyzes the population data of over 160 countries, providing insights into both urban and rural populations. It uses Python for the data analysis and Flask to create a RESTful API that allows users to easily access the population data. Additionally, a Linear Regression model is implemented to predict future population trends based on user input for specific years. This can help understand future rural and urban population distributions across the world.

Features

  1. Population Data Access:

    • Access data for over 160 countries, including the urban and rural population, total population, and respective percentages for different years.
    • Get data sorted by year, country, and urban/rural population percentage.

  2. Country-Specific Data:

    • Retrieve the population details for any given country and year.

  3. Population Prediction:

    • Use linear regression to predict the increase in urban population for any given country in future years.

  4. Flask API:

    • Easy access to the dataset and predictions through a well-defined API structure.

Technologies Used

  • Python: For data analysis and machine learning.
  • Flask: For creating the API.
  • Pandas: For handling and manipulating data.
  • Scikit-learn: To perform linear regression and make predictions.
  • CSV Data: Urban and rural population data loaded from CSV files.

API Endpoints

1. Get Population Data

Endpoint: /data/
Method: GET
Description: Retrieves the entire dataset, including rural and urban population data for all available countries.

2. Get Country Names

Endpoint: /countryNames/
Method: GET
Description: Returns a list of all distinct country names available in the dataset.

3. Get Urban Population Data

Endpoint: /UrbanData/
Method: GET
Description: Fetches urban population data for all countries.

4. Get Rural Population Data

Endpoint: /ruralData/
Method: GET
Description: Fetches rural population data for all countries.

5. Get Development Ratio Data

Endpoint: /developmentRatio/
Method: GET
Description: Returns data on the development ratio of urban versus rural areas.

6. Urban Population Data By Year

Endpoint: /urbanisedDataByYear/<int:year>/
Method: GET
Description: Provides the urban population data for a specified year, sorted in decreasing order by the urban population percentage.

7. Rural Population Data By Year

Endpoint: /ruralDataByYear/<int:year>/
Method: GET
Description: Provides the rural population data for a specified year, sorted in decreasing order by rural population percentage.

8. Country Data By Date

Endpoint: /dataByDate/<string:country>/<int:year1>/
Method: GET
Description: Returns detailed population data for a specific country and year.

9. Population Prediction

Endpoint: /predict/<string:country>
Method: GET
Description: Predicts future urban population trends for a specific country using linear regression. Returns predicted values for future years along with the model's accuracy score.

How to Use

1. Clone the repository

git clone https://github.com/yourusername/population-analysis-api.git
cd population-analysis-api

2. Install required dependencies

pip install -r requirements.txt

3. Run the Flask Application

python app.py

4. Access the API

Once the application is running, you can access the API at http://localhost:5000/.

Example API Request and Response

Request: Urban Population Data By Year

GET /urbanisedDataByYear/2020/

Response:

{
  "sorted_urban_dataset": [
    {
      "Country1": {
        "Urban_population": 5000000,
        "Code": "C1",
        "Year": 2020,
        "Rural_population": 2000000,
        "Total_Population": 7000000,
        "Urban_Population_percentage": 71.43
      }
    },
    ...
  ],
  "status": 200
}

Future Enhancements

  • Add more prediction models for rural population analysis.
  • Create visualization tools to better represent the trends and patterns in the population data.
  • Extend the API to allow user-defined queries for more customized results.

License

This project is licensed under the MIT License.