Launch a Project: What You'll Build in Our AI/ML Capstone Track

Say Yes to Our AI/ML Capstone Track

🧠 1. Problem Statement – Real, Relevant, and Impactful You’ll choose a practical use case like:

• Predicting student dropout risk
• Recommending products or content
• Detecting fake news or spam
• Classifying customer feedback

🎯 We focus on problems companies care about.

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🛠 2. Data Collection & Cleaning You’ll work with real-world datasets (CSV, APIs, or scrapers), and:

• Handle missing values
• Normalize and clean text/numbers
• Perform EDA (Exploratory Data Analysis)

🎯 Good models start with clean, meaningful data.

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📊 3. Model Building & Training You’ll build ML pipelines using:

• Scikit-learn, Pandas, NumPy
• Logistic Regression, Decision Trees, SVMs
• Advanced models: Random Forest, XGBoost, or basic Neural Nets

🎯 Train, validate, and tune your model for real performance.

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📈 4. Evaluation & Visualization You’ll learn to measure:

• Accuracy, Precision, Recall, F1-score
• Confusion matrix, ROC curves
• Insights using Matplotlib, Seaborn

🎯 Not just building models—understanding them.

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💻 5. Project Deployment (Optional but Powerful) Push your project online:

• Use Flask or Streamlit
• Host on Render, Hugging Face, or GitHub Pages
• Add a simple UI to interact with your model

🎯 Now it’s portfolio-ready and recruiter-friendly.

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✅ Final Output: A Complete, Polished ML Project You’ll graduate with:

• Clean code + well-documented notebooks
• A GitHub repo + demo link
• A project that solves a real-world problem

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🔚 Final Thought AI/ML careers don’t start with theory.
They start with proof of work—and that’s exactly what you’ll build in Meander’s Capstone Track. One project. One outcome. One real opportunity.