Predicting e-Commerce Sales with a Random Forest Regression

The Data Set

In order to demonstrate a random forest regression, a data set of e-commerce sales from popular online retailer, Wish, will be used. The data comes from Kaggle and only features sales information on summer clothing. Among the attributes include product descriptions, rating, whether ad boosts were used, whether urgency text was added to the product listing, and the number of units sold, among others.

To show the power of the random forest regression, the number of units sold will be predicted. Making good, accurate predictions would be invaluable to not only inventory planners, who need to make estimates on how much product to order or produce, but also sales, who need to understand how product moves in an e-commerce setting.

Importing and Cleaning the Data

All data imports and manipulations will be done through python along with the pandas and numpy libraries.

import pandas as pd
import numpy as np

## import the data saved as a csv
df = pd.read_csv("Summer_Sales_08.2020.csv")

The first two lines simply import the pandas and numpy libraries. The final line reads a CSV file previously saved and renamed to “Summer_Sales_08.2020” and creates a data frame.

df["has_urgency_banner"] = df["has_urgency_banner"].fillna(0)

df["discount"] = (df["retail_price"] - df["price"])/df["retail_price"]

When reviewing the data, the “has_urgency_banner” column, which indicates whether an urgency banner was applied to the product listing, was coded improperly. Instead of using 1’s and 0’s, it simply leaves a blank when a banner wasn’t used. The first line code fills those blanks with 0’s.

The second line creates a new column called “discount” which calculates the discount on the product compared to the listed retail price.

#machine-learning #python #statistics #data-science #random-forest-algorithm

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