Predicting gross sales is essential for enterprise operations, particularly for retail giants like Walmart. Right here’s how one can create an end-to-end pipeline utilizing a regression algorithm to foretell gross sales primarily based on historic knowledge, similar to final month’s gross sales and different elements like holidays or promotions.
1. Downside Assertion
We intention to foretell gross sales for the present month primarily based on:
– Final month’s gross sales knowledge.
– Different elements like holidays, promotions, and seasonal tendencies.
The pipeline includes knowledge preprocessing, characteristic engineering, mannequin coaching, analysis, and deployment.
2. Steps to Construct the Pipeline
Step 1: Load Dataset
We begin by loading a dataset that incorporates gross sales knowledge. Guarantee your dataset has columns like `store_id`, `date`, `gross sales`, `vacation`, and `promotion`. Right here is the Python code:
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split, GridSearchCV
from…
