Serving ML Model 2 : Olympic Medal Prediction model serving using Flask | Pickle | HTML | CSS

Tools and Technologies Implemented:
Building a UI for Olympic Medal Prediction Model using:
Flask for creating a web application to take user inputs and display predictions.
Pandas for handling and processing input data in a structured format.
Scikit-learn for StandardScaler for feature scaling and LinearRegression for building the predictive model.
Pickle for saving and loading the trained model and scaler.
HTML/CSS for building the user interface.


Project Overview:
The code starts by importing the necessary libraries, including Flask, Pandas, and Pickle.
The trained Linear Regression model and the StandardScaler are loaded from a .pkl file using Pickle.
A Flask application is initialized to create routes and handle HTTP requests.
When the user submits the form, the input data (such as the number of events, female athletes, male athletes, and previous medals) is captured.
The input data is structured into a Pandas DataFrame and then scaled using the loaded scaler.
The scaled input data is fed into the trained Linear Regression model to predict the number of medals.
The predicted value is rounded and passed to the HTML template for display.
The HTML form takes user inputs and submits them to the Flask app.
The predicted number of medals is displayed on the same page after the form submission.
A CSS file is used to style the HTML form and result display, making the web application visually appealing.

Data Link:
olympic_medal_predictor.pkl – https://drive.google.com/file/d/1jF75...

Code Link:
app.py – https://drive.google.com/file/d/1ZVlZ...
style.css – https://drive.google.com/file/d/18RO0...
index.html – https://drive.google.com/file/d/1XGMb...


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