UNPLUG-THE-PLAYERS

This project is live at-------> https://unplug-the-players.herokuapp.com/

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Objective

The main objective of the project is to create a web application game which allows to explore our knowledge in football. It will be like quiz-based game where game player will be awarded different points on his correct answer at different stage. The pre-objective is to gather the complete data and pre-process the data on which our web app will run.

Methodology

• Data Collection.
• Data Preprocessing
• Developing Cosine Similarty function.
• Creating web pages.
• Deployment.

Technologies to be Used

Codebase

The entire code has been developed using Python programming language and is hosted on Heroku. The analysis and model is developed using ScikitLearn and scipy library. The website is developed using Flask.

How to run the project 🚀:

1. Open the Terminal.
2. Clone the repository by entering $ git clone https://github.com/shsarv/UNPLUG-THE-PLAYER.git .
3. Ensure that Python3 and pip are installed on the system.
4. change the diectory to repository name using $ cd [Repository name].
5. Create a virtualenv by executing the following command: virtualenv mygame.
6. Activate the mygame virtual environment by executing the follwing command: source env/bin/activate.
7. Enter the cloned repository directory and execute pip install -r requirements.txt.
8. Now, execute the following command: flask run and it will point to the localhost server with the port 5000.
9. Enter the IP Address: http://localhost:5000 on a web browser and use the application.

📂 Structure

The directory contains web sub directories and a sub directory for hosting model and other scripts:

1. app.py The file which contains all the main backend operations of the website and used to run the flask server locally.

2. Procfile for setting up heroku.

3. requirements.txt contains all the dependencies.

4. templates contains the html file.

5. static contains the css,javascript files and images.

6. notebook contains all the jupyter notebooks and model development.

7. Resources.zip contains all the report and other resources in form of compressed file.

Dependencies

The following dependencies can be found in requirements.txt:

1. scikit-learn
2. Flask
3. pandas
4. numpy
5. scikit-learn
6. gunicorn
7. scipy

System architecture: How does this work? 🔧

For better understanding, flow of the project is as follows:

Determine the data set Understanding
Load the data
Analyse the data
Data pre-processing
Define the function to Pairwise distances between observations in n-dimensional space.
selecting 6 similar elements from the data randomly.
choose 5 attribute of selected element as a hint for end user

if user unable to choose the right player, window gives alert.

The app.py compares the dataset every time it executes. 

License

MIT License

Contributors

Sarvesh Kumar Sharma 🖋 📖💻	Satyam Kumar jha 💻	Sachi Tripathi 💻	Jeevesh Gangwar 💻	Ashutosh Tripathi 💻