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Machine Learning · Mini Project
Iris Flower Classification
A complete beginner ML workflow on the classic Iris dataset
1-2 weeks build
Overview
A first machine learning project that walks through every stage properly: loading the UCI Iris dataset, exploring it visually, splitting it, training k-NN, decision tree, logistic regression and SVM classifiers, and evaluating them with cross-validation. The saved model powers a small Flask form where you enter petal and sepal measurements to get a species.
What makes it stand up
- 01Full workflow from EDA to a deployed prediction form
- 02Pair plots and decision boundary visualisations
- 03Four classifiers compared with k-fold cross-validation
- 04Confusion matrix and per-class precision and recall
- 05Well-commented code written for first-time ML students
Module breakdown
- 01
Exploration
Summary statistics, pair plots and correlations.
- 02
Preparation
Train-test split and feature scaling.
- 03
Training
k-NN, tree, logistic regression and SVM.
- 04
Evaluation
Cross-validation scores and confusion matrices.
- 05
Web form
Flask page that predicts a species from four inputs.
After this, you will be able to
- Explain each step of a standard ML workflow
- Defend why cross-validation beats a single split
- Demo a prediction from measurements the examiner supplies
Technology stack
- Python
- Pandas
- scikit-learn
- Matplotlib
- Flask
- Level
- Mini Project
- Domain
- Machine Learning
- Build time
- 1-2 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support