Assessfy Foundation Projects Lab Beginner 5 milestones 100 marks

Simple SMS Spam Detector with Python

Discipline: AI & Machine Learning Level: Foundation Industry: AI & Data Function: Engineering Team: up to 3 Assessment: 5 milestones (100 marks)

Real-world project · AICTE-aligned · AI-graded · Audit-ready certificate

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Milestones

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Core skills

About this project

What you'll build: You'll create a program that can tell if a text message (SMS) is spam or not, using real message data and the popular scikit-learn library in Python.

Why it's a good starter: This project keeps things simple-no complicated math or advanced code. You'll follow clear steps and see results quickly, making it perfect for your first machine learning experience.

What you'll learn: You'll discover how to load data, turn words into numbers a computer can understand, train a machine learning model, and check how well your spam detector works.

Milestones

1. Setup & Plan

10 marks 5d

Submit a Jupyter notebook (or Colab link) with your project plan, data source link (e.g., the SMS Spam Collection dataset), and a brief summary of the steps you'll take. This shows you've set up your tools and understand the project goal.

2. Core Build: Data Loading & Cleaning

25 marks 10d

Show code and comments for loading the SMS spam data and basic cleaning (removing blanks, lowercasing, etc.). Submit plots or printouts of data samples before and after cleaning. This proves you can handle the data.

3. Core Build: Model Training & Prediction

35 marks 14d

Submit code and output for transforming text into features (e.g., with CountVectorizer or TfidfVectorizer), training a scikit-learn classifier (e.g., LogisticRegression), and making predictions. Include comments explaining each step.

4. Testing & Polish

20 marks 8d

Submit code and results for evaluating your model (accuracy score, simple confusion matrix). Add one improvement-like a clearer printout, a visualization, or testing on sample messages. Show your code is tidy and your notebook is easy to read.

5. Demo & Short Report

10 marks 5d

Submit a 1-page summary (in your notebook or as a PDF) explaining your approach, what worked, what you learned, and a demo (screenshots or output) of your model classifying at least 5 example messages.

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Skills you'll learn

AI & Machine LearningLoading and exploring a dataset with pandasText preprocessing and cleaning basicsBuilding a classifier with scikit-learnEvaluating model accuracySimple result visualization

Tools used

Python (3.x)Jupyter Notebook or Google Colabscikit-learnpandas

Prerequisites

Basic Python programming (loopsfunctions)Very simple maths (countingpercentages)

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You'll earn — Certificate (PDF)

AICTE-aligned Project Completion Certificate

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Simple SMS Spam Detector with Python

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Simple SMS Spam Detector with Python

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