Objective:The objective of this project is to explore, analyze, and compare the performance of at leastthree different machine learning classifiers or regressors on a medium$-$sized dataset.Requirements:Dataset Selection:$$ Choose a dataset suitable for classification or regression tasks. Ensure that the datasethas enough instances and features to allow for meaningful analysis.Data Preprocessing:$$ Handle missing values appropriately $($e$.$g$.,$ imputation or removal$).$ Encode categorical variables using suitable techniques $($e$.$g$.,$ one$-$hot encoding or labelencoding$).$ 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:$$ Select at least three machine learning classifiers or regressors. You can choose frompopular algorithms such as Decision Trees, Random Forest, Support Vector Machines,K$-$Nearest Neighbors, etc.Hyperparameter Tuning:$$ Perform hyperparameter tuning for each selected model to optimize theirperformance.Submission Guideline$$ Project Report $$ Follow the template provided$$ Dataset$$ Code $(.$ipynb file$)$ Provide a shareable link for the recorded video