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End-to-End ML Projectv1.0
✦ project profile ✦

End-to-End ML Project

PythonScikit-learnPandasFlaskRender
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✦ Project Overview

A complete end-to-end machine learning project predicting student performance. It covers the full data science lifecycle: data collection, EDA, preprocessing, model training with Scikit-learn, and deployment as a web application using Flask.

✦ Key Features

  • ♥Full data science lifecycle implementation
  • ♥Exploratory Data Analysis (EDA) and Visualization
  • ♥Model training and hyperparameter tuning
  • ♥Web deployment for real-time predictions

✦ Methodology

Follows the standard CRISP-DM lifecycle methodology:

01.

Exploratory Data Analysis

Used Pandas and Seaborn to visualize distributions and correlations, identifying key drivers of student performance.

02.

Pipeline Construction

Built a Scikit-learn Pipeline to automate preprocessing (OneHotEncoding, Scaling) and ensuring reproducible transformations.

03.

Deployment

Serialized the trained model using Pickle and exposed it via a Flask REST API, hosted on Render for public access.