Walmart-Weekly-Sales-Prediction

This Project compares the different machine learning models on Walmart Weekly Sales Data and predicts the weekly sales for the test data.

This Project has 4 phases:-

1. PreProcessing Phase:- PreProcessing Phase includes Data Cleaning, Scaling the data, and Splitting the data into Test and train.
2. Learning Phase:- In the Learning phase, I used Random Forest Model, Extreme Gradient Boosting, Gradient Boosting, and Elastic Net Regression to fit the data into the model and tune the hyperparameters until we get the optimal performance.
3. Evaluation Phase:- In the Evaluation Phase, I compared the performance of all the models on parameters like Coefficient of Regression, Mean Absolute Error, and Root mean square error.
4. Prediction Phase:- In the Prediction Phase, I chose the best model and used that to predict the Weekly Sales for the test data.

Applications:- Jupyter Notebook Programming language:- Python Libraries used:- pandas, matplotlib, numpy, sklearn, xgboost, seaborn, prettytable OS:- Windows You can download the .ipynb File Attached and run it in jupyter notebook or Google Colab