Maximizing Accuracy with Multiple Machine Learning Models

[darshbaxi]

The dataset will be split into training and testing sets using a robust method, ensuring the validity of the evaluation process. Models to be explored may include but are not limited to:

1. Linear Regression
2. Logistic Regression
3. Decision Trees
4. Random Forest
5. Support Vector Machines (SVM)
6. K-Nearest Neighbors (KNN)
7. Gradient Boosting

The implementation for each model will include data preprocessing, model training, and evaluation metrics such as accuracy, precision, etc.