Day-12

[Audio] ETL & Machine Learning Fundamentals A comprehensive guide to understanding data pipelines and machine learning basics for predictive analytics and business intelligence..

[Audio] ETL / Data Pipelines Why We Use It Key Tools Automates data extraction, transformation, and loading processes Apache Airflow - Workflow orchestration platform Talend - Data integration and transformation Ensures clean, structured data is available for analysis Power BI Dataflows - Microsoft's ETL solution Reduces manual effort and human error Enables consistent data quality across systems.

[Audio] The ETL Process Extract Transform Load Pull data from multiple sources including databases, APIs, files, and cloud services Clean, standardize, aggregate, and prepare data for analysis using business rules Insert processed data into target systems like data warehouses, databases, or BI tools The ETL pipeline ensures that raw data from disparate sources is systematically processed and made available for business intelligence and analytics. This automated workflow is the backbone of modern data-driven organizations..

[Audio] Machine Learning Basics Why Learn ML? Regression Machine learning enables predictive and advanced analytics, allowing organizations to forecast trends, automate decisions, and uncover hidden patterns in data. Predict numeric values Classification Categorize data ML transforms historical data into actionable insights for future planning and strategic decision-making. Clustering Segment data Time Series Trend prediction.

[Audio] Regression: Predicting Continuous Values Regression is a machine learning technique used to predict numeric or continuous values. It finds relationships between input features and output numbers, making it ideal for forecasting scenarios. Example Predictions Types of Regression House price prediction Linear Regression - Simple straight-line relationships Salary prediction Multiple Regression - Multiple input variables Sales forecast Temperature prediction Polynomial Regression - Curved relationships Ridge/Lasso Regression - Regularized models Output Example: "Predict the price of a house: ₹50,20,000".

[Audio] How Regression Works Data Collection Gather historical data with input features (size, location, age) and output values (price) Pattern Recognition The algorithm analyzes relationships between features and identifies mathematical patterns Model Training The model learns the best-fit line or curve that minimizes prediction errors Prediction Apply the trained model to new data to forecast numeric outcomes Regression tries to find a relationship between input features and output numbers, creating a mathematical function that can predict future values based on new inputs..

[Audio] Classification: Predicting Categories Classification is used when you want the result in categories or labels, not numbers. The model learns patterns and divides data into distinct classes based on input features. Binary Classification Multi-Class Classification Two classes: Spam or Not Spam, Loan Approved or Not, Disease Positive/Negative More than 2 classes: Product categories, customer segments, risk levels Multi-Label Classification Multiple outputs: Movie genres, article topics, product attributes.

[Audio] Classification Use Cases Output Example: "Email is SPAM" Common Applications Email Filtering: Spam or Not Spam Demographics: Male or Female Credit Decisions: Loan Approved or Not Customer Analytics: Churn prediction (Yes/No) Healthcare: Disease detection (Positive/Negative) Classification models categorize data into predefined groups, enabling automated decision-making across industries from finance to healthcare to marketing..

[Audio] Time Series Forecasting Time series forecasting predicts future values based on historical time-ordered data, identifying trends and patterns that repeat over time. Seasonality Lag Repeating patterns at regular intervals, such as festival sales, holiday shopping, or quarterly business cycles Using previous time period values to predict future outcomes, leveraging historical data dependencies Models ARIMA, SARIMA for statistical forecasting, LSTM neural networks for complex patterns Output Example: "Predict sales for next week: 120 units".

[Audio] Choosing the Right ML Technique Use Regression When Use Classification When Use Time Series When You need to forecast future values based on historical time-ordered data with seasonal patterns You need to predict continuous numeric values like prices, temperatures, or quantities You need to categorize data into distinct groups or labels like spam detection or risk assessment Understanding these fundamental ML techniques empowers data professionals to select the right approach for their specific business problems, whether predicting sales, classifying customers, or forecasting demand..