MACHINE LEARNING

Objective:

The objective of this project is to explore, analyse, and compare the performance of at least three different machine learning classifiers ar regressons on a merdium$-$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 leaming classifiers or regressors. You can choose from popular algorithms such as Decision Trees, Random Forest, Support Vector Machines, K$-$Nearest Neighbors, etr.

Hyperparameter Tuning:

Perform hyperparameter tuning fur each selected model to uptimize their performance. Submission Guideline

Project Report $-$ Follow the template provided

Dataset

Code $(.$ipyob file$)$

Project Title

Objective of the Project:

Problem Statement:

Dataset Detalls:

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 on insights gained from exploring the dataset$]$

Machine Learning Models Used:

Model $1$: $[$Name of the Hirst 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: $[$Fxplain why this model was chosen$]6.$

$6.$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: ISpecify the evaluation metrics used, such as accuracy, precision, recall, F$1$ score, or relevant metrics for regression tasks$]$

Model Comparisan: $[$Present the results of earh model and cxampare their performance$]$

Feature Seles:tion Intpact: $[$Cistuss the impact of feat ure selection on model performanke, if applicable$]$

Insights and Cbservations: $[$Provide insights gained from the analysis$]$

If Classificution is performed use this table

If Regression is performed use this table

$\backslash$table$[[\backslash$table$[[$Mo$],[$del$],[$Na$],[$me$]],$R$2-$Score,MSE,MAE,MPE$],[\backslash$table$[[$Mo$],[$del$],[1]],\backslash$table$[[$Before$],[$Hyperpa$],[$rameter$],[$Tuning$]],\backslash$table$[[$After$],[$Hyperpa$],[$rameter$],[$Tuning$]],\backslash$table$[[$Before$],[$Hyperpa$],[$rameter$],[$Tuning$]],\backslash$table$[[$After$],[$Hyperpa$],[$rameter$],[$Tuning$]],\backslash$table$[[$Before$],[$Hyperpa$],[$rameter$],[$Tuning$]],\backslash$table$[[$After$],[$Hyperpa$],[$rameter$],[$Tuning$]],\backslash$table$[[$Before$],[$Hyperpa$],[$rameter$],[$Tuning$]],\backslash$table$[[$After$],[$Hyperps$],[$rameter$],[$Tuning$]]],[\backslash$table$[[$Mo$],[$del$],[2]],,,,,,,,],[\backslash$table$[[$Mo$],[$del$],[3]],,,,,,,,]]$