Here are some hands-on project ideas for Supervised Learning, ranging from beginner to advanced levels:
1️⃣ Spam Email Classifier (Beginner)
🔹 Goal: Build a model to classify emails as spam or not spam.
📂 Dataset: SpamAssassin Public Corpus or UCI’s SMS Spam Collection.
📌 Steps:
✅ Preprocess email text (tokenization, stopwords removal, TF-IDF).
✅ Train a classifier (Logistic Regression, Naïve Bayes, or Random Forest).
✅ Evaluate with accuracy, precision, recall, and F1-score.
🚀 Bonus: Deploy as a simple web app using Flask or Streamlit.
2️⃣ Customer Churn Prediction (Intermediate)
🔹 Goal: Predict whether a customer will leave a company based on behavior.
📂 Dataset: Telco Customer Churn Dataset (Kaggle).
📌 Steps:
✅ Perform exploratory data analysis (EDA) to understand churn patterns.
✅ Train a classification model (Decision Tree, SVM, or XGBoost).
✅ Interpret feature importance (e.g., monthly charges, contract type).
✅ Deploy using Streamlit for interactive user predictions.
3️⃣ House Price Prediction (Intermediate)
🔹 Goal: Predict house prices based on features like location, size, and amenities.
📂 Dataset: Boston Housing Dataset or Zillow’s dataset.
📌 Steps:
✅ Data preprocessing (handling missing values, feature scaling).
✅ Train Regression models (Linear Regression, Random Forest, XGBoost).
✅ Use GridSearchCV to tune hyperparameters.
✅ Deploy as a simple app where users input features & get price estimates.
4️⃣ Fake News Detection (Intermediate)
🔹 Goal: Classify news articles as real or fake using NLP techniques.
📂 Dataset: Fake News Dataset (Kaggle).
📌 Steps:
✅ Preprocess text (vectorization using TF-IDF, word embeddings).
✅ Train classifiers (Logistic Regression, LSTM, BERT).
✅ Evaluate using Confusion Matrix & ROC Curve.
✅ Deploy as a web app where users enter a news headline & get predictions.
5️⃣ Credit Card Fraud Detection (Advanced)
🔹 Goal: Identify fraudulent transactions from credit card data.
📂 Dataset: Credit Card Fraud Detection Dataset (Kaggle).
📌 Steps:
✅ Handle class imbalance using SMOTE (Synthetic Minority Over-sampling Technique).
✅ Train classification models (Random Forest, XGBoost, Neural Networks).
✅ Evaluate using AUC-ROC & Precision-Recall Curve.
✅ Implement anomaly detection techniques like Autoencoders.
6️⃣ Medical Diagnosis – Diabetes Prediction (Advanced)
🔹 Goal: Predict whether a person has diabetes based on health indicators.
📂 Dataset: PIMA Indians Diabetes Dataset (Kaggle).
📌 Steps:
✅ Perform feature engineering (BMI, blood pressure, insulin levels).
✅ Train classifiers (KNN, SVM, Neural Networks).
✅ Evaluate with Confusion Matrix & Precision-Recall F1 Score.
✅ Deploy an interactive web app for patient diagnosis.
7️⃣ Loan Approval Prediction (Advanced)
🔹 Goal: Predict whether a loan application will be approved or rejected.
📂 Dataset: Loan Prediction Dataset (Kaggle).
📌 Steps:
✅ Perform EDA and visualize important trends (e.g., income vs. loan approval).
✅ Train models (Decision Tree, Random Forest, XGBoost).
✅ Explain model decisions using SHAP values.
✅ Deploy an AI-powered Loan Approval System for users to test.
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