Sales Forecasting using Predictive Analytics

This project implements a machine learning-based sales forecasting tool using historical sales data. By applying models like Linear Regression, Decision Trees, and Random Forests, the goal is to accurately predict future sales based on key product and transaction features.

🔍 Project Overview

Objective: Build a predictive model to forecast product sales using machine learning algorithms.

Key Steps:

• Data cleaning and preprocessing
• Feature engineering and encoding
• Model training and evaluation (Linear Regression, Decision Tree, Random Forest)
• Performance comparison using RMSE and R² scores
• Visual analysis and feature importance
• Predict sales for new, unseen data

📁 Dataset

The dataset used is sales_data_sample.csv, which includes historical order and product-level data.

Features used:

• QUANTITYORDERED
• PRICEEACH
• ORDERLINENUMBER
• MONTH_ID
• YEAR_ID
• PRODUCTLINE
• DEALSIZE

Target:

• SALES – The actual sales value to be predicted.

📦 Dependencies

Install the required Python packages using pip:

pip install pandas numpy matplotlib seaborn scikit-learn