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.

Submission Guideline:

$$ Project Report $$ Follow the template provided

$$ Dataset

$$ Code $(.$ipynb file$)$

$$ Provide a shareable link for the recorded video

Instructions for the recorded video:

$$ Create a recorded video $($screencast or presentation format$)$ where team members explain their project.

$$ Cover key aspects of the project, including problem statement, dataset characteristics, data preprocessing, model selection rationale, feature selection, hyperparameter tuning, and results analysis.

$$ The video should be well$-$structured, clear, and provide a comprehensive overview of the entire project Project Report

Team Members with Student Number

Project Title

$1.$ Objective of the Project:

$2.$ Problem Statement:

$3.$ Dataset Details:

$$ Dataset Name: $[$Name of the Dataset$]$

$$ Source: $[$Provide the source or origin of the dataset$]$

$$ Size: $[$Number of instances, features, and target variable$]$

$$ Description: $[$Brief overview of the dataset, including the nature of features and the target variable$]$

$4.$ Data Preprocessing:

$$ Handling Missing Values: $[$Describe the approach taken to handle missing data$]$

$$ Encoding Categorical Variables: $[$Explain how categorical variables were encoded$]$

$$ Feature Scaling$/$Normalization: $[$Specify if any scaling or normalization was applied$]$

$$ Exploratory Data Analysis: $[$Include any relevant visualizations or insights gained

from exploring the dataset$]$

$5.$ Machine Learning Models Used:

$$ Model $1$: $[$Name of the First Model$]$

Justification: $[$Explain why this model was chosen$]$

$$ Model $2$: $[$Name of the Second Model$]$

Justification: $[$Explain why this model was chosen$]$

$$ Model $3$: $[$Name of the Third Model$]$

Justification: $[$Explain why this model was chosen$]$

$6.$ Hyperparameter Tuning:

$$ Model $1$: $[$Specify Hyperparameters and Tuning Process$]$

$$ Model $2$: $[$Specify Hyperparameters and Tuning Process$]$

$$ Model $3$: $[$Specify Hyperparameters and Tuning Process$]$

$7.$ Results:

Performance Metrics: $[$Specify the evaluation metrics used, such as accuracy, precision, recall, F$1$ score, or relevant metrics for regression tasks$]$

Model Comparison: $[$Present the results of each model and compare their performance$]$

Feature Selection Impact: $[$Discuss the impact of feature selection on model performance, if applicable$]$

Insights and Observations: $[$Provide insights gained from the analysis $8.$ Conclusion:

Summarize the key findings, lessons learned, and implications of the project. Discuss any

challenges faced and potential areas for future improvement