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

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