In this assignment, you will work with different classification algorithms, including logistic regression, KNN, Naive Bayes, SVM, decision trees, and random forests. You will be using a publicly available dataset and will be required to perform the following tasks: 1. Choose a publicly available dataset for classification. Some popular sources for datasets include Kaggle and UCI Machine Learning Repository. 2. Explore the data and understand its characteristics. 3. Split the data into training and testing sets or use k-fold cross-validation to evaluate the performance of the models. 4. Build and evaluate the following classification algorithms: Logistic regression KNN Naive Bayes SVM Decision tree Random forest 5. Tune the hyperparameters of each algorithm using GridSearchCV or trial and error. 6. Evaluate the performance of each algorithm using appropriate evaluation metrics such as accuracy. 7. a clear and concise conclusion summarizing your findings and comparing the different algorithms' performance. 8. Submit the following deliverables: Jupyter notebook containing all the codes A report of your findings