vincent9514/aws-webapp-soccer-player-prediction

⚽️Full Stack AWS Web App Development

MSiA423-Vincent-Developer

Developer: Vincent, Product Owner: Rush, QA: Lauren

What is Soccer Player Prediction?

Predict soccer player’s value by using predictive models based on the features including base stats, skills, preferred position, and club, etc to assist club manager to gain negotiation power in the transfer market, offer wise transfer fee and wage, and ultimately benefit the club from the deal.

Project Charter:

• Vision: Assist club manager to gain negotiation power in the transfer market, offer wise transfer fee and wage, and ultimately benefit the club from the deal.
• Mission: Predict player’s value, wage, and overall rating by using predictive models based on the features including base stats, skills, preferred position, and club, etc.
• Success criteria: A R^2 of greater than 80% to measure the performance of the transfer market value prediction model and demonstrate the effectiveness of the web application.

If you want to know more, this is a list of selected starting points:

• FIFA18 Complete Dataset on Kaggle
• FIFA Offical Website

Pivotal Tracker Project URL:

https://www.pivotaltracker.com/n/projects/2143921

Getting Started:

1. Clone repository

2. Create virtual environment for new app

virtualenv -p python3 fifapredict

3. Activate environment

source fifapredict/bin/activate

4. Install required packages

pip install -r requirements.txt

5. Download FIFA csv files from Kaggle , unzip and store in /data/external/

6. Set up fifapredict.env file with the following structure to connect to a database instance:

• Doing the following format in the config.py:

DIALECT = 'mysql'
DRIVER = 'pymysql'
USERNAME = '   '
PASSWORD = '   '
HOST = '   '
PORT = '   '
DATABASE = '   '
SQLALCHEMY_DATABASE_URI = "{}+{}://{}:{}@{}:{}/{}".format(DIALECT,DRIVER,USERNAME,PASSWORD,HOST,PORT,DATABASE)
SQLALCHEMY_TRACK_MODIFICATIONS=False

6. Set your environment

source fifapredict.env

7. Load and clean the dataset

python develop/src/data/dataLoading.py

8. Create the model

python develop/src/models/trainModel.py

9. Run the model

make all
python app/Flask/application.py

You should be able to go to the IP address that it responds with and see your FIFA web app.

Preprocessing Data

• Value String Cleansing
• Country to Continent
• Potential Points
• Unique Position Name
• Preferred Position Number

EDA

• Initial Feature Selection
• Top 20 players
• Group Players by Overall
• Age vs Potential Points
• Group by Preferred Positions

Modeling

• Linear Regression
• Ridge Regression
• Lasso Regression
• Random Forest
• Neural Network

Project Organization

├── LICENSE
├── Makefile                    <- Makefile with commands like `make data` or `make train`
├── README.md                   <- The top-level README for developers using this project.
├── develop                     <- The top-level develop for developers developing this project.
│   ├── data
│   │   ├── external            <- Data from third party sources.
│   │   ├── interim             <- Intermediate data that has been transformed.
│   │   ├── processed           <- The final, canonical data sets for modeling.
│   │   └── raw                 <- The original, immutable data dump.
│   │
│   ├── models                  <- Trained and serialized models, model predictions, or model summaries
│   │    ├── trainModel.py      <- Python Script to train and pickle models including linear, ridge, lasso, randomforest, neural network.
│   │    ├── lm.pkl             <- The pickle linear regression model.
│   │    └── rfr.pkl            <- The pickle randomforest model.
│   │
│   ├── notebooks               <- Jupyter notebooks. Naming convention is a number (for ordering),
│   │    |                      the creator's initials, and a short `-` delimited description, e.g.
│   │    |                      `1.0-jqp-initial-data-exploration`.
|   │    └── EDA.ipynb          <- EDA Notebook to explain the FIFA Dataset.
│   │
│   └── src                     <- Source code for use in this project.
│       │
│       ├── data                <- Scripts to download or generate data
│       │   ├── __init__.py     <- Makes src a Python module
│       │   └── dataLoading.py  <- Data Loading and Processing Python Script
│       │
│       ├── models              <- Scripts to train models and then use trained models to make predictions
│       │   ├── predictModel.py <- Model to predict the result
│       │   └── trainModel.py   <- Model Training Python Script
│       │
|       └── tests
|           ├── make_dataset_test.py
|           ├── test_basic.py
|           ├── test_linear_model.py
|           ├── test_lasso_model.py
|           ├── test_ridge_model.py
|           ├── test_nnet_model.py
|           └── test_randomforest_model.py
│
├── app                         <- Web Application
│   └── Flask                   <- Web Application constructed by Flask platform
│       ├── application.py
|       ├── requirements.txt
|       ├── config.py
|       ├── static
|       └── templates
| 
│
├── requirements.txt            <- The requirements file for reproducing the analysis environment, e.g.
│                                  generated with `pip freeze > requirements.txt
│
└── tox.ini                     <- tox file with settings for running tox; see tox.testrun.org

---

Project based on the cookiecutter data science project template. #cookiecutterdatascience