Store Item Demand Forecasting Challenge

This repository contains code and resources for the "Store Item Demand Forecasting Challenge" hosted on Kaggle. The goal of this challenge is to accurately forecast the demand for 10 different items in 10 different stores over a period of time. Accurate demand forecasting is crucial for effective inventory management, which in turn can lead to cost savings and improved customer satisfaction.

Tutorial: Getting Started

This tutorial will guide you through the process of training a machine learning model to forecast store item demand using the code provided in this repository.

Step 1: Clone the Repository

First, clone this repository to your local machine using the following command:

git clone https://github.com/aakashsyadav1999/Store-Item-Demand-Forecasting-Challenge.git

Step 2: Install Dependencies

Navigate to the project directory and install the required dependencies by running:

pip install -r requirements.txt

Step 3: Explore the Data

Explore the dataset provided in the `data/` directory. You can use Jupyter notebooks in the `notebooks/` directory for exploratory data analysis (EDA).

Step 4: Train the Model

Train the machine learning model by running the `train.py` script:

Step 5: Evaluate the Model

Evaluate the trained model and analyze its performance using the provided notebooks in the `notebooks/` directory.

Step 6: Make Predictions

Once the model is trained, you can make predictions on new data using the trained model.

Tutorial: Using Docker

This tutorial will guide you through using Docker to run this project in a containerized environment.

Step 1: Build the Docker Image

Navigate to the directory containing the Dockerfile and run the following command to build the Docker image:

docker build -t store-item-forecast .

Step 2: Run the Docker Container

After the image is built, run the Docker container using the following command:

docker run store-item-forecast

This will start the container and execute the training script automatically.