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.
0 likes · 10 views · Jul 24, 2024
I did this project from end to end to build a model which predicts Fifa players' value by considering different factors.
