shriman/storelift_sales_prediction

Storelift sales prediction

Colab notebooks and preliminary data explorations

The repository contains two colab notebooks, which can be viewed either here in GitHub or in Google Colab (links are in files.)

• storelift.ipynb :

  • Initial data exploration and data cleaning along with XGBoost model
  • Data exploration and cleaning and feature engineering: Read the dataset and generate categorical and numerical features
  • Data visualization
  • Build the train and test set: Build the cleaned train, test and evaluation data set
  • Machine learning model: Test Linear regression and XGBoost models

• storelift_model_random_forest.ipynb:

  • ML Model: random forest regressor: Train and test the data set with random forest regressor

• storelift_model_gradient_boosting_regressor.ipynb:

  • ML Model: gradient boosting regressor: Train and test the data set with random forest regressor