Machine Learning Project for predicting the Ratings of football players in FIFA
The model has been deployed using REST API using flask, on Heroku : https://fifa-soccer-player-rating-pred.herokuapp.com/
Database File : https://www.kaggle.com/hugomathien/soccer
Jupyter Notebook : https://shashank-sundi.github.io/FIFA-FOOTBALL-PLAYER-RATING-PREDICTION/
Jupyter Notebook : https://github.com/Shashank-Sundi/NOTEBOOKS/blob/main/Football%20Player%20Rating%20Prediction.ipynb
| PROBLEM | MODELS USED | LIBRARIES USED |
|---|---|---|
| Predicting the ratings of players in FIFA | XGBOOST,LINEAR-REG ,KMEANS ,KNN ,RANDOM FOREST ,DECISION TREE ,SVR |
Sklearn , Seaborn ,Pandas ,Scipy ,Sqlite3, math ,Xgboost |
| Step | Execution of the project was carried out as given in the following steps : |
|---|---|
| 1 | Extracted data table (PLAYER_ATTRIBUTES) form the SQL database using Sqlite3 |
| 2 | Validated data types of the features and analysed the statistical properties of the features |
| 3 | Checked for null values in feature vectors. Implemented Random Sample and fequent category imputation imputation with categorical feature vectors. Imputed null values in numerical feature vectors using KNN imputer |
| 4 | Encoded the categorical feature vectors using frequency encoding and one-hot encoding |
| 5 | Performed EDA on data - checked the distribution of data using NPP, KDE plots ; checked for outliers via boxplots |
| 6 | Visualised the correlation heatmap and removed highly correlated feature vectors from the data |
| 7 | Clustered the data into 4 clusters ( based on elbow curve ) , using K-Means clustering , to train different models on each cluster |
| 8 | Trained and tested various models on each of the clusters ; chose the model which gave highest r2 score . |
| 9 | Apparently xgboost gave highest r2 for all the data clusters . This also implies that there was no need to cluster the data at all |
| 10 | Exported the xgboost model |
| Step | Execution of the project was carried out as given in the following steps : |
|---|---|
| 1 | Built Log Writer module , for writing the log messages in a centralised log file |
| 2 | Built the Data Formatter module , for aggregating the inputs from the html form ; converting the input to a dataframe |
| 3 | Buil the Valaidator module , to validate the data types of inputs , column names , length etc. |
| 4 | Built the Preprocessing module ,for imputation , encoding and other transformations |
| 5 | Built REST API using Flask framework ; created routes for home page and prediction , by calling all the required modules |
| 6 | Created the requirements.txt , Procfile , etc. and all other requirements to be satisfied for deployment. |
| 7 | Built html pages for data input and results prediction |
| 8 | Deployed the model on Heroku via Git Bash terminal |
