Forecasting Sales with Time Series, LightGBM & Random Forest
Learn how to build sales forecasting models using Time Series, ARIMA, SARIMA, LightGBM, Random Forest, and LSTM
Learn how to build sales forecasting model using ARIMA, SARIMA, LightGBM, Random Forest, and LSTM
Learn how to conduct customer segmentation analysis
Learn how to analyze sales performance trend
Learn how to evaluate forecasting model’s accuracy and performance by calculating mean absolute error and conduct residual analysis
Learn how time series forecasting model work. This section will cover data collection, preprocessing, train test split, model selection, and model training
Learn about factors that can contribute to sales performance, such as seasonal trends, market saturation and supply chain efficiency
Learn how to find and download datasets from Kaggle
Learn how to clean dataset by removing missing rows and duplicate values
Learn how to analyze order fulfilment efficiency
Learn the basic fundamentals of sales forecasting
Welcome to Forecasting Sales with Time Series, LightGBM & Random Forest course. This is a comprehensive project based course where you will learn step by step on how to build sales forecasting models. This course is a perfect combination between machine learning and sales analytics, making it an ideal opportunity to enhance your data science skills. This course will be mainly concentrating on three major aspects, the first one is data analysis where you will explore the sales report dataset from multiple angles, the second one is to conduct customer segmentation analysis, and the third one is to build sales forecasting models using time series, LightGBM, Random Forest, LSTM, and SARIMA (Seasonal Autoregressive Integrated Moving Average).
Below are things that you can expect to learn from this course:
- Learn the basic fundamentals of sales forecasting
- Learn how time series forecasting models work. This section will cover data collection, data exploration, preprocessing, train test split, model selection, model training, and forecasting
- Learn about factors that can contribute to sales performance, such as seasonal trends, market saturation and supply chain efficiency
- Learn how to find and download datasets from Kaggle
- Learn how to clean dataset by removing missing rows and duplicate values
- Learn how to conduct customer segmentation analysis
- Learn how to analyze order fulfillment efficiency
- Learn how to analyze sales performance trend
- Learn how to build sales forecasting model using ARIMA, SARIMA, LightGBM, Random Forest, and LSTM
- Learn how to evaluate forecasting model’s accuracy and performance by calculating mean absolute error and conduct residual analysis
Tools, IDE, and Datasets
Introduction to Sales Forecasting
How Time Series Forecasting Model Works?
Factors That Can Contribute to Sales Performance
Setting Up Google Colab IDE
Finding & Downloading Sales Report Dataset From Kaggle
Project Preparation
Cleaning Dataset by Removing Missing Values & Duplicates
Customer Segmentation Analysis
Analyzing Order Fulfilment Efficiency
Analyzing Sales Performance Trend
Forecasting Sales with ARIMA
Forecasting Sales with SARIMA
Forecasting Sales with LightGBM
Forecasting Sales with Random Forest
Forecasting Sales with LSTM
Calculating Mean Absolute Error & Conducting Residual Analysis
Conclusion & Summary
You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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