Predictive Analytics Engine

Project Overview

A comprehensive predictive analytics solution that transforms raw business data into actionable forecasts. The platform enables organizations to anticipate market trends, optimize inventory, and make data-driven decisions with confidence.

Using Azure Machine Learning for model training and deployment, combined with Data Factory for ETL pipelines, the system processes millions of data points to generate accurate predictions.

Key Features

• Sales Forecasting

  Time-series models predicting sales with 95%+ accuracy

• Demand Prediction

  Forecast product demand to optimize inventory and reduce stockouts

• Customer Churn Analysis

  Identify at-risk customers and enable proactive retention

• Sentiment Analysis

  Analyze customer feedback and social media for brand perception

• Automated ETL Pipelines

  Data Factory workflows for seamless data ingestion and transformation

• Interactive Dashboards

  Power BI visualizations with drill-down capabilities

Technical Implementation

The platform ingests data from multiple sources (databases, APIs, files) through Azure Data Factory pipelines. Machine learning models are trained using Azure ML with automated hyperparameter tuning and deployed as real-time endpoints.

Azure ML

Python

.NET Core

Power BI

Architecture Highlights

• Azure Data Factory: Orchestrated ETL pipelines with data quality checks
• Azure ML: AutoML for model selection and MLOps for deployment
• Data Lake: Centralized storage for raw and processed data
• Real-time Scoring: Low-latency prediction endpoints via Azure Functions
• Model Monitoring: Drift detection and automated retraining triggers