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.

Dataset Selection:

$$Choose a dataset from kaggle or any other website that is 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.

This homework must be provided with the $.$ipynb file. If you can not provide a file then just paste the full code on answer section, we'll turn it to the $.$ipynb file later. This code is going to run on Google Colab.