Day 2 · Machine Learning x Flask

AI Powered Web App

Build, train, and deploy your own machine learning models as production-ready web applications with real-time predictions.

House Price Prediction Customer Clustering
Python Flask Scikit-Learn
POST /api/predict 
{
  "MedInc": 6.8,
  "HouseAge": 27,
  "AveRooms": 9.33,
  "AveBedrms": 0.97,
  "Population": 2401,
  "AveOccup": 1.4,
  "Latitude": 34.0,
  "Longitude": -118.2
}
Model ready · 99.9% uptime

How It Works

Build production-ready ML apps in three simple steps

Step 1

Train Models

Build regression and clustering models in notebooks with clear EDA, preprocessing, and evaluation metrics.

  • Exploratory Data Analysis
  • Feature Engineering
  • Model Evaluation
Step 2

Expose APIs

Load the trained models in Flask and expose them as REST endpoints ready to be consumed by any client.

  • RESTful API Design
  • Model Serialization
  • Error Handling
Step 3

Connect the UI

Use simple HTML and JavaScript to send requests to the APIs and show predictions in real time.

  • Interactive Forms
  • Real-time Updates
  • Result Visualization

Available Endpoints

Two powerful ML models ready to serve predictions

POST /api/predict
Regression Model

Takes 8 numerical features and returns the predicted median house value with confidence score.

Input Features
MedInc HouseAge AveRooms +5 more
Try it now
POST /api/cluster
Clustering Model

Takes age, annual income, and spending score to identify customer segments and behavior patterns.

Input Features
Age Annual Income Spending Score
Try it now
Get Started

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Try House Prediction Try Clustering