Update README.md

# Smart Inventory Advisor for Retail Sales

This repository contains a machine learning project that analyzes retail sales data to provide actionable inventory management recommendations. The primary goal is to help a store owner identify which products to restock and how many units to order, based on a data-driven sales forecast.

This project was developed for the Datathon Nexus Crew.

## Project Overview

The project follows a complete machine learning workflow:

1. **Exploratory Data Analysis (EDA):** Initial analysis to understand trends, patterns, and relationships within the data.

2. **Preprocessing & Feature Engineering:** Cleaning the data, converting categorical features to numerical values, and creating new time-based features from dates.

3. **Model Training & Tuning:** Building a `RandomForestRegressor` to predict actual `Units Sold`. The model was tuned using `RandomizedSearchCV` to improve its reliability and prevent overfitting.

4. **Actionable Recommendations:** Using the trained model to create a "Smart Inventory Advisor" that generates a prioritized list of products that need to be reordered.

## Model Description

The core of this project is a **Random Forest Regressor** (`scikit-learn`) trained to forecast the number of units sold (`Sales`) for a given product. It uses a variety of features to make its predictions, including:

* **Inventory & Order Data:** `Inventory Level`, `Units Ordered`

* **Pricing Factors:** `Price`, `Discount`, `Competitor Price`

* **Promotional & Environmental Factors:** `Holiday/Promotion`, `Weather`, `Seasonality`

* **Categorical Information:** `Product Category`, `Region`

* **Time-based Features:** `DayOfWeek`, `Month`, `Day`

## How It Works: The Smart Inventory Advisor

The final output is a "Store Owner's Action Plan." This tool automates the following process:

1. **Analyzes All Products:** It iterates through every unique product in the dataset.

2. **Forecasts Future Sales:** For each product, it predicts the expected sales for the next 30 days.

3. **Calculates a Reorder Point:** It determines the stock level at which a reorder is necessary (based on a 7-day supply).

4. **Generates an Action Plan:** It creates a simple table showing only the products that are below their reorder point and require immediate attention.

This provides a clear, data-driven to-do list that helps a business owner focus on the most critical inventory needs to maximize sales and prevent stockouts.

README.md

@@ -1,3 +1,19 @@
----
-license: mit
----
+---
+license: mit
+datasets:
+- jerewy/Dataset_NexusCrew
+language:
+- en
+metrics:
+- r_squared
+- mae
+- mse
+pipeline_tag: tabular-regression
+library_name: sklearn
+tags:
+- scikit-learn
+- random-forest
+- retail
+- demand-forecasting
+- inventory-management
+---