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About the Course - Retail Demand Prediction Using Machine Learning

Are you wondering how is machine learning being used in the industry today? Are you looking for some hands-on experience on a real-world dataset? Do you want to learn how to solve a business problem using machine learning?

If yes, then you have landed at the right place. This course is the perfect balance of theoretical learning and hands-on exercises.

The course is aimed at enabling you to build an end-to-end Machine Learning model on a real-world dataset - starting from converting a business objective into a machine learning problem, to building a complete machine learning model.

The business problem addressed as a part of the course is that of demand forecasting. 


Key Takeaways from the Course

  • Understand the importance of demand forecasting in the industry
  • Learn how to Structure a Business Problem
  • Converting a Business Problem to Machine Learning Problem
  • Derive Interesting Insights from the data
  • We will cover the following steps in ML Lifecycle:
  • Hypothesis Generation

  • Exploratory Data Analysis 

  • Data Cleaning and Preprocessing

  • Feature Engineering

  • Model Building

  • Evaluating Model Performance 

  • Create ML solution for real-world problems
  • Presenting Model results


Prerequisites

This course assumes that you are familiar with the basic concepts of Machine learning and comfortable with coding in python.

Tools covered in Retail Demand Prediction using Machine Learning Course

  • Python
  • Pandas
  • scikitlearn
  • matplotlib

Course curriculum

  • 1
    Welcome to Retail Demand Prediction Course
  • 2
    Problem Statement Definition
    • Defining the Business Problem FREE PREVIEW
    • Quiz: Defining the Business Problem
    • Reaching the Right Business Objective - Revenue vs Costs FREE PREVIEW
    • Quiz: Reaching the Right Business Objective - Revenue vs Costs
    • Reaching the Right Business Objective - Identifying objectives from revenue
    • Quiz: Reaching the Right Business Objective - Identifying objectives from revenue
    • Reaching the right business objective - Identifying objective to reduce cost
    • Reaching the right business objective - Comparing Impact & Effort
    • Quiz: Reaching the right business objective - Comparing Impact & Effort
    • Final Problem Statement
    • Hypothesis Generation
    • Data Extraction
    • Available Datasets
    • Quiz: Available Datasets
    • Make your own Data Science Problem Statement
  • 3
    Exploratory Data Analysis
    • Understanding and Validating Data
    • Understanding Product related Features
    • Understanding Store related Features
    • Data Exploration Summary
    • Validating the Hypothesis
    • Summarising Key Insights
    • Dataset & Notebooks for EDA
    • EDA Assignment
  • 4
    Data preprocessing
    • What is Preprocessing?
    • Preprocessing: Categorical
    • Preprocessing: Numerical
    • Quiz: Data Preprocessing
    • Datasets and Notebooks for Preprocessing
  • 5
    Understand Decision Tree and Random Forest (Optional)
    • Introduction to Decision Tree
    • Purity in Decision Trees
    • Terminologies related to Decision Trees
    • Selecting Best split in Decision Tree
    • Chi-square
    • Information Gain
    • Reduction in Variance
    • Optimising Performance of Decision Trees
    • Bootstrap Sampling for Random Forest
    • Introduction to Random Forest
    • Random Forest Hyperparameters
  • 6
    Baseline Model and Validation Strategy
    • Defining Evaluation Metric
    • Baseline Model
    • Quiz: Baseline Models
    • Datasets and Notebooks for Baseline Model
    • Defining Validation Strategy
    • Quiz: Validation Strategy
    • Datasets and Notebooks for Validation Strategy
  • 7
    Feature Engineering
    • Getting Random Forest ready for Feature Engineering
    • Feature Engineering
    • Datasets and Notebook for Feature Engineering
    • Feature Engineering Assignment
  • 8
    Gradient Boosting Methods & Hyperparameter Tuning
    • Introduction to Boosting (Optional)
    • Introduction to Boosting Techniques
    • XGboost Hyperparameter Tuning
    • Catboost Hyperparameter Tuning
    • Introduction to LightGBM (Optional)
    • LightGBM Implementation
    • Datasets and Notebooks for Boosting Techniques
  • 9
    Advanced Ensemble Methods
    • Introduction to Ensemble Learning (optional)
    • Implementation for Ensemble Technique
    • Datasets and Notebooks for Ensemble Models
  • 10
    Sharing Model Results & Feature Importance
    • Checking Performance on the Test set
    • Dataset and Notebook for Feature Importance
    • Sharing Model Results & Report
    • Interpretability of Black Box Models
    • Share LightGBM Model Results & Feature Importance

Course of Completion

Upon successful completion of the course, you will be provided a block chain enabled certificate by Analytics Vidhya with lifetime validity.
Course of Completion

Instructor(s)

  • Aishwarya Singh

    Aishwarya Singh

    Aishwarya is currently working as a Data Scientist at Analytics Vidhya. She is one of the primary content curators and an instructor for Analytics Vidhya’s most popular course – Applied Machine Learning. She is also an avid reader and blogger who loves exploring the endless world of data science and artificial intelligence. She has written over 70 articles in recent years on various machine learning and deep learning topics and applications.
  • Ankit Choudhary

    Ankit Choudhary

    Ankit is an IIT Bombay Graduate with a Masters and Bachelors in Electrical Engineering. He is a corporate trainer and leads the hackathon category at Analytics Vidhya. He is responsible for liaison with various companies to transform their data into data science competitions. He has conducted corporate trainings for a BFSI client on Basic and Advanced Machine Learning. He has finished in top 5 of multiple data science competitions and also conducted a workshop on how to win data science competitions at DataHack Summit 2019. He has previously worked as a lead decision scientist for Indian National Congress deploying statistical models (Segmentation, K-Nearest Neighbours) to help party leadership/Team make data-driven decisions. His motivation lies in putting data at the heart of business for data-driven decision making.

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