Supervised learning projects in this repository span two core problem types: regression, where the goal is to predict a continuous numeric value, and classification, where models assign inputs to discrete categories. Each project in this section uses real-world tabular datasets sourced primarily from Kaggle, and follows a consistent architecture that includes a preprocessing pipeline, trained model artifacts, and a Flask-based prediction API.Documentation Index
Fetch the complete documentation index at: https://mintlify.com/dronabopche/100-ML-AI-Project/llms.txt
Use this file to discover all available pages before exploring further.
House Price Prediction
Predict residential sale prices using an ensemble of Linear, Ridge, and Lasso regression models with a Gemini-powered preprocessing layer.
Employee Retention Prediction
Classify employee turnover risk using three logistic regression variants with confidence scoring across all model outputs.
Medical Cost Prediction
Estimate individual insurance charges from demographic and health features using linear models and CatBoost.
Titanic Survival Prediction
Apply binary classification to the classic Titanic dataset to predict passenger survival based on socioeconomic and demographic factors.
Email Spam Classification
Detect spam emails using NLP-based feature extraction and classification models trained on labeled message corpora.
Credit Loan Approval
Predict binary loan approval outcomes from applicant financial and credit profile features using ensemble classifiers.
All supervised learning projects
The table below lists all supervised learning projects in the repository (range 01–27), their problem type, and dataset source.| # | Project | Problem type | Dataset source |
|---|---|---|---|
| 01 | House Price Prediction | Regression | Kaggle |
| 02 | Employee Retention Prediction | Classification | Kaggle |
| 03 | Iris Flower Classification | Classification | Kaggle / UCI |
| 04 | Medical Cost Prediction | Regression | Kaggle |
| 05 | Titanic Survival Prediction | Classification | Kaggle |
| 06 | Email Spam Classification | Classification | Public ML repositories |
| 07 | Energy Power Prediction | Regression | Kaggle |
| 08 | Smart Shop Prediction | Regression | Kaggle |
| 10 | Used Car Price Prediction | Regression | Kaggle |
| 11 | Mobile Price Range Prediction | Classification | Kaggle |
| 13 | Hotel Booking Cancellation Prediction | Classification | Kaggle |
| 14 | Crop Yield Prediction | Regression | Kaggle |
| 18 | Password Strength Prediction | Classification | Kaggle |
| 19 | Health Risk Prediction | Classification | Kaggle |
| 20 | Personality Prediction | Classification | Kaggle |
| 23 | YouTube Video Popularity Prediction | Regression | Kaggle |
| 26 | Credit Loan Approval | Classification | Kaggle |
| 27 | Amazon Product Price Determination | Regression | Kaggle |
Standard project structure
Every supervised learning project follows the same directory layout, making it easy to navigate between projects and extend any pipeline.Projects that follow the full standard structure expose a
POST /predict endpoint through App.py. Some earlier projects (05, 06, 26) are notebook-only and do not yet include a deployed API layer.