Supervised Learning in Machine Learning
Definition:
Supervised learning is a type of machine learning where a model is trained on a labeled dataset, meaning that the input data (features) has corresponding correct outputs (labels). The goal is for the model to learn the mapping from inputs to outputs, enabling it to make accurate
predictions on new, unseen data.
- Regression:
- Predicts continuous values.
- Example: Predicting house prices or stock values.
- Classification:
- Predicts discrete classes or categories.
- Example: Identifying email as spam or not spam.
- Linear Regression
- Logistic Regression
- Decision Trees
- Support Vector Machines (SVMs)
- Random Forest
- Gradient Boosting Machines (e.g., XGBoost, LightGBM)
- Neural Networks
- House Price Prediction
- Goal: Predict house prices based on features like area, number of bedrooms, and location.
- Dataset: Kaggle House Price Dataset
- Algorithm: Linear Regression.
- Iris Flower Classification
- Goal: Classify iris flowers into species based on petal and sepal dimensions.
- Dataset: Iris Dataset
- Algorithm: Logistic Regression or Decision Tree.
- Customer Churn Prediction
- Goal: Predict whether customers will leave a service based on their usage data.
- Dataset: Telco Customer Churn
- Algorithm: Random Forest.
- Loan Default Prediction
- Goal: Predict whether a loan applicant will default.
- Dataset: Lending Club Dataset
- Algorithm: Random Forest or Gradient Boosting.