Objective:

The objective of this project is to explore, analyze, and compare the performance of at least

three different machine learning classifiers or regressors on a medium$-$sized dataset.

Requirements:

Dataset Selection:

Choose a dataset suitable for classification or regression tasks. Ensure that the dataset

has enough instances and features to allow for meaningful analysis.

Data Preprocessing:

Handle missing values appropriately $($e$.$g$.,$ imputation or removal$).$

Encode categorical variables using suitable techniques $($e$.$g$.,$ one$-$hot encoding or label

encoding$).$

Normalize or scale numerical features if necessary.

Perform exploratory data analysis $($EDA$)$ to gain insights into the dataset.

Feature Selection:

Implement a feature selection process to identify relevant features in the dataset.

Classifier$/$Regressor Selection:

Select at least three machine learning classifiers or regressors. You can choose from

popular algorithms such as Decision Trees, Random Forest, Support Vector Machines,

K$-$Nearest Neighbors, etc.

Hyperparameter Tuning:

Perform hyperparameter tuning for each selected model to optimize their

performance. can you do the coding