IronKaggle

📈 Predicted Sales Output

This repository contains the output from a machine learning model that predicts sales based on input features. The file predictions_output.csv includes both the features used in prediction and the model's output values.

🧾 File Contents

predictions_output.csv

Column Name	Description
Feature1	Numeric feature used in the prediction
Feature2	Categorical feature used in the prediction
Predicted_Sales	Predicted sales value (model output)

Note: This is a sample structure; in the real dataset, the feature names and count may vary.

🔍 Objective

To use machine learning models to forecast future sales based on historical and contextual data. This output can help support strategic decisions in sales, marketing, and inventory management.

🧠 Model Summary

• The model was trained using supervised learning techniques.
• The final model predicts the target variable Predicted_Sales.
• The data was preprocessed using standard encoding and scaling techniques.

💡 Usage

You can load the CSV file using pandas in Python:

import pandas as pd

df = pd.read_csv("predictions_output.csv")
print(df.head())

✅ Example Output

   Feature1 Feature2  Predicted_Sales
0         1        A             1000
1         2        B             1500
2         3        C             1200

📂 File Location

• predictions_output.csv – Final prediction file from the ML model.

Authors

• Hilena Amare Tadesse [https://github.com/hilu1]
• Jud Saavedra [https://github.com/10197jsg]