Summary of eCommerce Analytics | 8 Hours Course | Day 1 | 360DigiTMG

Main Ideas and Concepts

• Introduction to E-commerce
  • E-commerce, or electronic commerce, refers to online transactions and purchases.
  • The course focuses on the application of Machine Learning (ML) in the E-commerce domain.
• Growth of E-commerce in India
  • India has a large and growing E-commerce market, projected to become the second-largest globally by 2034.
  • Increasing smartphone and internet access has fueled this growth, with significant numbers of users engaging in online shopping.
• Machine Learning Applications
  • The course will explore various ML applications in E-commerce, including exploratory data analysis (EDA) and use cases relevant to E-commerce businesses.
• CRISP-DM Methodology
  • The CRISP-DM framework (Cross Industry Standard Process for Data Mining) is crucial for applying analytics effectively in E-commerce.
  • It includes understanding business objectives, data collection, and analysis steps.
• Data Understanding and Preprocessing
  • Data must be cleaned and understood through EDA and preprocessing, which includes data cleansing and feature engineering.
  • Feature engineering involves selecting and transforming data features to improve model performance.
• Model Building and Evaluation
  • After preprocessing, Machine Learning models are built, evaluated, and tuned to ensure they meet business objectives.
  • The evaluation process assesses model performance based on specific business scenarios.
• Analytics Stages
  • Descriptive Analytics: Understanding what has happened.
  • Diagnostic Analytics: Understanding why it happened.
  • Predictive Analytics: Forecasting future outcomes.
  • Prescriptive Analytics: Providing recommendations based on analysis.
• Practical Applications
  • The course will cover practical applications, including customer segmentation, predicting future sales, and improving marketing strategies.

Methodology and Instructions

• CRISP-DM Framework
  • Understand business objectives and constraints.
  • Collect and analyze data, ensuring it is clean and relevant.
  • Conduct EDA and preprocessing.
  • Build Machine Learning models and evaluate their performance.
  • Deploy the model and monitor its performance over time.
• Data Preprocessing Steps
  • Clean data to remove inaccuracies.
  • Perform exploratory data analysis (EDA) to visualize and understand data trends.
  • Conduct feature engineering to select and transform relevant features for modeling.
• Model Evaluation
  • Use performance metrics aligned with business goals to evaluate model effectiveness.
  • Adjust models based on evaluation results and business needs.

Speakers/Sources Featured

• The video features a single speaker who provides the training on eCommerce analytics and Machine Learning applications, likely a trainer from 360DigiTMG. Specific names were not mentioned in the subtitles.

Notable Quotes

— 40:41 — « Whenever you're facing some issues, take a step back and try to figure out what has happened. »

— 44:40 — « When you go to a doctor, they give you a prescription; similarly, we are making suggestions based on our analysis. »

— 47:36 — « E-commerce is moving us into a different dimension altogether where people are becoming more and more self-independent. »

— 47:59 — « Analytics can help people grow; it can also stop people from collapsing. »

— 56:06 — « I'm just joking about that. »

Category

Educational

Video