Objective: Compare the performance of logistic regression and K$-$Nearest Neighbor $($KNN$)$ on a classification problem.

Requirements:

$1.$ Dataset Selection:

$-$ Choose a dataset suitable for binary classification $($e$.$g$.,$ email spam detection, disease diagnosis, or loan default prediction$).$

$-$ Ensure the dataset has more than two features for meaningful comparison.

$2.$ Data Preprocessing:

$-$ Split the dataset into training and test sets $($e$.$g$.,\backslash (70\backslash \%\backslash )$ training and $\backslash (30\backslash \%\backslash )$ testing$).$

$3.$ Model Implementation:

$-$ Implement logistic regression and KNN using libraries like scikit$-$learn.

$-$ Experiment with different values of $\text{'}$ K $\text{'}$ for the KNN model to find the optimal value.

$-$ Train both models on the training set and evaluate them on the test set.

$4.$ Evaluation:

$-$ Use evaluation metrics such as accuracy, precision, recall, and F$1-$score to compare the models.

$5.$ Report:

$-$ Submit a report comparing the performance of logistic regression and KNN$.$

$-$ Include observations on which algorithm performed better, under what conditions, and why.

$-$ Discuss any trade$-$offs between the models based on their evaluation metrics.