This project applies a **Decision Tree Classifier** to predict whether a user will purchase a product based on social network ad data. The dataset contains user attributes such as **age** and **estimated salary**, and the binary target variable indicates whether the product was purchased (`1`) or not (`0`).

This project applies K-Nearest Neighbors (KNN) to the Breast Cancer dataset from scikit-learn to classify tumors as malignant or benign. The goal is to explore how KNN behaves on medical data using numerical features extracted from digitized breast mass images.

The objective of this project is to build a Naive Bayes classification model to predict whether a user will purchase a product after viewing a social media advertisement. This project is designed to demonstrate practical understanding of probabilistic classification and model evaluation using a real-world-like dataset.

This project uses a Random Forest Classifier to predict loan approval (Y/N) based on features like gender, income, and credit history. It involves data preprocessing, encoding, and missing value imputation. Feature importance is visualized to highlight key predictors, helping financial institutions automate loan decisions.