It's simple to buy any product with a click and have it delivered to your door. Online shopping has been rapidly evolving over the last few years, making our lives easier. But behind the scenes, e-commerce companies face a complex challenge that needs to be addressed.
Uncertainty plays a big role in how the supply chains plan and organize their operations to ensure that the products are delivered on time. These uncertainties can lead to challenges such as stockouts, delayed deliveries, and increased operational costs.
You work for the Sales & Operations Planning (S&OP) team at a multinational e-commerce company. They need your help to assist in planning for the upcoming end-of-the-year sales. They want to use your insights to plan for promotional opportunities and manage their inventory. This effort is to ensure they have the right products in stock when needed and ensure their customers are satisfied with the prompt delivery to their doorstep.
The Data
You are provided with a sales dataset to use. A summary and preview are provided below.
Online Retail.csv
| Column | Description |
|---|---|
'InvoiceNo' | A 6-digit number uniquely assigned to each transaction |
'StockCode' | A 5-digit number uniquely assigned to each distinct product |
'Description' | The product name |
'Quantity' | The quantity of each product (item) per transaction |
'UnitPrice' | Product price per unit |
'CustomerID' | A 5-digit number uniquely assigned to each customer |
'Country' | The name of the country where each customer resides |
'InvoiceDate' | The day and time when each transaction was generated "MM/DD/YYYY" |
'Year' | The year when each transaction was generated |
'Month' | The month when each transaction was generated |
'Week' | The week when each transaction was generated (1-52) |
'Day' | The day of the month when each transaction was generated (1-31) |
'DayOfWeek' | The day of the weeke when each transaction was generated ( 0 = Monday, 6 = Sunday) |
# Import required libraries
from pyspark.sql import SparkSession
from pyspark.ml.feature import VectorAssembler
from pyspark.ml import Pipeline
from pyspark.ml.regression import RandomForestRegressor
from pyspark.sql.functions import col, dayofmonth, month, year, to_date, to_timestamp, weekofyear, dayofweek
from pyspark.ml.feature import StringIndexer
from pyspark.ml.evaluation import RegressionEvaluator
# Initialize Spark session
my_spark = SparkSession.builder.appName("SalesForecast").getOrCreate()
# Importing sales data
sales_data = my_spark.read.csv(
"Online Retail.csv", header=True, inferSchema=True, sep=",")
sales_data.show(5)# Convert InvoiceDate to datetime
sales_data = sales_data.withColumn("InvoiceDate", to_date(
to_timestamp(col("InvoiceDate"), "d/M/yyyy H:mm")))# Insert the code necessary to solve the assigned problems. Use as many code cells as you need.
sales_data.count()# View first few rows of sales_data
sales_data.show(5)1 - Aggregate the data
# Create an aggregated dataset
agg_data = sales_data.groupBy(["Country", "StockCode", "InvoiceDate", "Year", "Month", "Day", "Week", "DayOfWeek"]).agg({"Quantity": "sum", "UnitPrice": "avg"}).withColumnRenamed("sum(Quantity)", "Quantity")
agg_data.show()2 - Splitting your dataset
# Split dataset based on the date provided
train_data = agg_data.filter(col("InvoiceDate") <= "2011-09-25")
test_data = agg_data.filter(col("InvoiceDate") > "2011-09-25")
# Transforming to a pandas DataFrame
pd_daily_train_data = train_data.toPandas()pd_daily_train_data.head()3 - Building your regression model
# Build your Random Forest Regression model
# Encode categorical columns (index)
country_indexer = StringIndexer(inputCol="Country", outputCol="CountryIndex").setHandleInvalid("keep")
stock_code_indexer = StringIndexer(inputCol="StockCode",
outputCol="StockCodeIndex").setHandleInvalid("keep")
# Combine all selected features into a vector
assembler = VectorAssembler(inputCols=["CountryIndex", "StockCodeIndex", "Year", "Month", "Day", "Week", "DayOfWeek"], outputCol="features")
# Initialize the RandomForestRegressor model
rf = RandomForestRegressor(
featuresCol="features",
labelCol="Quantity",
maxBins=4000
)
# Create Pipeline to organize modeling process
ppln = Pipeline(stages=[country_indexer, stock_code_indexer, assembler, rf])
# Create model by fitting pipeline to training set
model = ppln.fit(train_data)4 - Evaluating the model
# Transform the test data and make predictions
test_predictions = model.transform(test_data).withColumn("prediction", col("prediction").cast("double"))