Predict FIFA Players Value | Machine Learning
Zeeshan Akram
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Data Scientist
ML Engineer
AI Developer
Flask
Heroku
scikit-learn
Project Steps (End-to-End):
Gather data from Kaggle.
Perform data cleaning and preprocessing.
Perform exploratory data analysis.
Perform feature engineering.
Build an ensemble technique model.
Build client-facing API with Flask.
Deployed solution to Heroku.
Project Details:
Python: 3.7
Packages: numpy, pandas, seaborn, matplotlib, missingno, plotly, sklearn, boruta_py, lightgbm, xgboost, pickle
Model: XGBoost Regressor
RMSE Score:: 0.0056
EDA Charts:
Code Snippets:
Data Cleaning:
EDA:
Feature Engineering:
Modelling:
For full source code and project details please visit the GitHub repository.
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I did this project from end to end to build a model which predicts Fifa players' value by considering different factors.
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Data Scientist
ML Engineer
AI Developer
Flask
Heroku
scikit-learn
Zeeshan Akram
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