Forecasting Sales With Time Series, Lightgbm & Random Forest

What you'll learn
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
Requirements
No previous experience in sales forecasting is required
Basic knowledge in Python and statistics
Description
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). In the introduction session, you will learn the basic fundamentals of sales forecasting, such as getting to know forecasting models that will be used and also learn how sales forecasting can help us to identify consumer behavior. Then, in the next session, we are going to learn about the full step by step process on how time series forecasting works. This section will cover data collection, preprocessing, splitting the data into training and testing sets, selecting model, training model, and forecasting. Afterward, you will also learn about several factors that contribute to sales performance, for example, product quality, marketing strategies, seasonal trends, market saturation, supply chain efficiency, and macro economic factors. Once you have learnt all necessary knowledge about the sales forecasting model, we will start the project. Firstly you will be guided step by step on how to set up Google Colab IDE. In addition to that, you will also learn to find and download sales report dataset from Kaggle, Once, everything is ready, we will enter the main section of the course which is the project section The project will be consisted of three main parts, the first part is the data analysis and visualization where you will explore the dataset from various angles, in the second part, you will learn step by step on how to conduct extensive customer segmentation analysis, meanwhile, in the third part, you will learn how to forecast sales using time series, LightGBM, Random Forest, LSTM, and Seasonal Autoregressive Integrated Moving Average. At the end of the course, you will also evaluate the sales forecasting model's accuracy and performance using Mean Absolute Error and residual analysis.First of all, before getting into the course, we need to ask ourselves this question: why should we learn to forecast sales? Well, here is my answer, Forecasting sales is a strategic imperative for businesses in today's dynamic market. By mastering the art of sales forecasting, we gain the power to anticipate market trends, understand consumer behavior, and optimize resource allocation. It's not just about predicting numbers, it's about staying ahead of the competition, adapting to changing demands, and making informed decisions that drive business success. In addition to that, by building this sales forecasting project, you will level up your data science and machine learning skills. Last but not least, even though forecasting sales can be very useful, however, you still need to be aware that no matter how advanced your forecasting model is, there is no such thing as 100% accuracy when it comes to forecasting.Below are things that you can expect to learn from this course:Learn the basic fundamentals of sales forecastingLearn how time series forecasting models work. This section will cover data collection, data exploration, preprocessing, train test split, model selection, model training, and forecastingLearn about factors that can contribute to sales performance, such as seasonal trends, market saturation and supply chain efficiencyLearn how to find and download datasets from KaggleLearn how to clean dataset by removing missing rows and duplicate valuesLearn how to conduct customer segmentation analysisLearn how to analyze order fulfillment efficiencyLearn how to analyze sales performance trendLearn how to build sales forecasting model using ARIMA, SARIMA, LightGBM, Random Forest, and LSTMLearn how to evaluate forecasting model's accuracy and performance by calculating mean absolute error and conduct residual analysis
Overview
Section 1: Introduction
Lecture 1 Introduction to the Course
Lecture 2 Table of Contents
Lecture 3 Whom This Course is Intended for?
Section 2: Tools, IDE, and Datasets
Lecture 4 Tools, IDE, and Datasets
Section 3: Introduction to Sales Forecasting
Lecture 5 Introduction to Sales Forecasting
Section 4: How Time Series Forecasting Model Works?
Lecture 6 How Time Series Forecasting Model Works?
Section 5: Factors That Can Contribute to Sales Performance
Lecture 7 Factors That Can Contribute to Sales Performance
Section 6: Setting Up Google Colab IDE
Lecture 8 Setting Up Google Colab IDE
Section 7: Finding & Downloading Sales Report Dataset From Kaggle
Lecture 9 Finding & Downloading Sales Report Dataset From Kaggle
Section 8: Project Preparation
Lecture 10 Uploading Sales Report Dataset to Google Colab
Lecture 11 Quick Overview of Sales Report Dataset
Section 9: Cleaning Dataset by Removing Missing Values & Duplicates
Lecture 12 Cleaning Dataset by Removing Missing Values & Duplicates
Section 10: Customer Segmentation Analysis
Lecture 13 Customer Segmentation Analysis
Section 11: Analyzing Order Fulfilment Efficiency
Lecture 14 Analyzing Order Fulfilment Efficiency
Section 12: Analyzing Sales Performance Trend
Lecture 15 Analyzing Sales Performance Trend
Section 13: Forecasting Sales with ARIMA
Lecture 16 Forecasting Sales with ARIMA
Section 14: Forecasting Sales with SARIMA
Lecture 17 Forecasting Sales with SARIMA
Section 15: Forecasting Sales with LightGBM
Lecture 18 Forecasting Sales with LightGBM
Section 16: Forecasting Sales with Random Forest
Lecture 19 Forecasting Sales with Random Forest
Section 17: Forecasting Sales with LSTM
Lecture 20 Forecasting Sales with LSTM
Section 18: Calculating Mean Absolute Error & Conducting Residual Analysis
Lecture 21 Calculating Mean Absolute Error & Conducting Residual Analysis
Section 19: Conclusion & Summary
Lecture 22 Conclusion & Summary
People who are interested in forecasting sales using ARIMA, SARIMA, LightGBM, Random Forest, and LSTM,People who are interested in performing customer segmentation analysis


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