Brewery Sales Forecasting🍺

Project Overview

This project involves forecasting total sales for a brewery using a dataset from Kaggle. The models used for forecasting include Linear Regression, Random Forest, and Decision Tree. The project has been executed and tested on Google Cloud Platform's DataProc service.

Dataset

The dataset used in this project can be found at Kaggle Brewery Operations and Market Analysis Dataset. It provides extensive data related to brewery operations and market analysis, which is suitable for developing predictive models.

Requirements

• Google Cloud Platform account
• Access to GCP DataProc
• Apache Spark
• PySpark
• Python 3.x

Installation and Setup

1. Set up GCP DataProc Cluster: Ensure that you have a GCP account and create a DataProc cluster to run PySpark jobs.

2. Install PySpark:

   pip install pyspark

Download the Dataset:

Download the dataset from Kaggle and upload it to a bucket in Google Cloud Storage accessible by your DataProc cluster.

EDA

Refer to notebooks/Brewery data analysis.ipynb and databricks notebook

Models Used

• Linear Regression: A basic model for establishing a baseline in forecasting performance. Refer to brewery_pyspark_lr.py
• Random Forest: An ensemble model that uses multiple decision trees to improve the predictive accuracy and control over-fitting. Refer to brewery_pyspark_rf.py
• Decision Tree: A model that splits the data into subsets while at the same time developing a corresponding decision tree. The final decision tree can be used to make predictions. Refer to brewery_pyspark_dt.py

Training:

• Initialize SparkSession
• Load dataset
• Drop duplicates and NAs
• Cast the target variable to Float
• Split DateTime column into Year, Month and Day
• Convert categorical columns to numeric values or one hot encoding-Linear Regression
• Initialize VectorAssembler
• Split dataset into Train and Test
• Initialize Model
• [Optional] Initialize params for grid search
• [Optional] Initialize the CrossValidator along with metric
• Train the model
• Initialize evaluator
• Compute RMSE and R squared

NOTE: As we're not treating this as a time series problem we can split it randomly into Train and Test

Parameter Selection

brewery_pyspark_rf_grid.py

Results

RMSE: 2486