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.