https://github.com/kasyap1234/sales_forecasting

https://github.com/kasyap1234/sales_forecasting

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• Host: GitHub
• URL: https://github.com/kasyap1234/sales_forecasting
• Owner: kasyap1234
• Created: 2023-10-23T13:44:02.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-23T13:46:16.000Z (about 1 year ago)
• Last Synced: 2023-10-23T15:00:42.655Z (about 1 year ago)
• Language: Jupyter Notebook
• Size: 3.91 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Sales Forecasting with Random Forest Regression

This repository contains a Python script for sales forecasting using a Random Forest Regressor. The script reads historical sales data, preprocesses the data, trains a Random Forest model, and evaluates its performance using Mean Squared Error (MSE).

## Table of Contents

1. [Prerequisites](#prerequisites)

2. [Getting Started](#getting-started)

3. [Data Collection and Preprocessing](#data-collection-and-preprocessing)

4. [Data Exploration and Analysis (EDA)](#data-exploration-and-analysis-eda)

5. [Feature Selection](#feature-selection)

6. [Train-Test Split](#train-test-split)

7. [Model Training and Validation](#model-training-and-validation)

8. [Evaluate the Model](#evaluate-the-model)

9. [Forecasting (Optional)](#forecasting-optional)

10. [Mean Squared Error (MSE)](#mean-squared-error-mse)

## Prerequisites

- Python 3.x

- Pandas

- Scikit-Learn

## Getting Started

1. Clone this repository to your local machine.

2. Install the required Python libraries using `pip install pandas scikit-learn`.

3. Download the dataset and place it in the same directory as the script. The dataset should be named `Traindata.csv`.

## Data Collection and Preprocessing

The script reads the historical sales data from `Traindata.csv`, preprocesses the data, and extracts relevant features.

## Data Exploration and Analysis (EDA)

You can perform exploratory data analysis and feature engineering on the dataset.

## Feature Selection

The script selects the following features for training the model: 'year', 'month', 'day', 'store_id', 'sku_id', 'total_price', 'base_price'.

## Train-Test Split

The dataset is split into a training set and a test set for model validation. By default, 80% of the data is used for training, and 20% is used for testing.

## Model Training and Validation

A Random Forest Regressor is trained on the training data. You can modify the hyperparameters as needed.

## Evaluate the Model

The model's performance is evaluated using Mean Squared Error (MSE). The lower the MSE, the better the model's performance.

## Forecasting (Optional)

If you want to use the trained model for forecasting, you can do so by providing new data to the model.

## Mean Squared Error (MSE)

The Mean Squared Error (MSE) is printed to assess the model's performance.