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.

Objective of the Project:

Problem Statement:

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$]$

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$]$

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$]$

Hyperparameter Tuning:

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

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

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

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$]$

If Classification is performed use this table

If Regression is performed use this table

Conclusion:

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

challenges faced and potential areas for future improvement.