rossmann_TSA_forecasts

This project is built using the data from Rossmann competition hosted at Kaggle and then published for comfortable reading as the Jupyter notebook.

To check out the project open an .ipynb file.

Time Series Analysis & Forecasting

• Exploratory Data Analysis with Python (ECDF, missing values, Correlation analysis ...)
• Time Series Analysis per store type (Seasonal decomposition, Autocorrelation)
• Forecasting with Prophet
• Predictive modeling with XGboost

Libraries used: numpy, pandas, matplotlib, seaborn, statsmodel, fbprophet (Facebook), xgboost, sklearn.

Thank you for reading!