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aksace/
superkart-sales-forecasting-model

Name: superkart-sales-forecasting-model
Availability: InStock

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SuperKart Sales Forecasting Model

This is a Random Forest Regressor model trained to predict product sales at SuperKart stores.

Model Details

Model Type: Random Forest Regressor
Task: Regression (Sales Forecasting)
Training Data: SuperKart historical sales data (8,763 records)
Test R² Score: 0.9319
Test RMSE: $278.68

Best Hyperparameters

n_estimators: 200
max_depth: None
min_samples_split: 5
min_samples_leaf: 2

Features

The model uses the following features:

Product_Weight
Product_Sugar_Content
Product_Allocated_Area
Product_Type
Product_MRP
Store_Size
Store_Location_City_Type
Store_Type
Store_Age
Price_Category

Usage

python

import joblib
import pandas as pd

# Load model
model = joblib.load('best_model.pkl')

# Load label encoders
label_encoders = joblib.load('label_encoders.pkl')

# Make predictions
predictions = model.predict(X_test)

Performance Comparison

Model	Test R² Score	Test RMSE
Random Forest	0.9319	$278.68
XGBoost	0.9314	$279.69
Gradient Boosting	0.9290	$284.58

Training Details

Train-Test Split: 80-20
Cross-Validation: 3-fold
Evaluation Metrics: RMSE, MAE, R²

aksace/superkart-sales-forecasting-model