Retail Sales Forecasting Machine Learning Project
Python + Time Series
₹499.00₹399.00
Retail Store Sales Forecasting using Python
Want to learn how companies predict future sales using data science and machine learning?
This project walks you through a complete real-world retail sales forecasting pipeline using Python, time-series analysis, and deep learning models.
You will learn how to analyze historical sales data, detect trends, build forecasting models, and evaluate predictions using modern machine learning techniques.
Perfect for students, beginners in data science, and anyone building a machine learning portfolio.
Key features:
Data cleaning and preprocessing
✔ Exploratory Data Analysis (EDA)
✔ Time Series Forecasting techniques
✔ ARIMA forecasting model
✔ SARIMA seasonal forecasting
✔ Facebook Prophet model
✔ Deep Learning forecasting using LSTM
✔ Model evaluation using RMSE and MSE
✔ Sales classification and feature engineering
✔ Business analytics interpretation
Models Implemented in the Project
The project implements multiple forecasting techniques to compare performance:
ARIMA (AutoRegressive Integrated Moving Average)
SARIMA (Seasonal ARIMA)
Facebook Prophet Forecasting
LSTM Neural Network (Deep Learning)
This allows learners to understand traditional vs modern forecasting
Technologies Used
Programming Language
Python
Libraries Used
Pandas
NumPy
Matplotlib
Seaborn
Stats Models
Pmdarima
Prophet
TensorFlow / Keras
Scikit-Learn
Perfect for (Who This Project Is For)
This project is perfect for:
BCA / BSc / MSc Data Science students
Machine Learning beginners
Data Science learners
Python developers
Students building data science portfolios
Anyone preparing ML academic projects
Files Included in the Download
You will get:
Complete Jupyter Notebook (.ipynb)
Retail sales dataset
Fully implemented forecasting models
Visualization graphs
Model evaluation results
Clean and well-commented code
Documentation
Powerpoint Presentation
You will get the following files:
CSV (6KB)
PPTX (1MB)
DOCX (1MB)
IPYNB (1MB)