In Machine learning $,$ this project, students will focus on regression analysis using health$-$related data. They will

implement two machine learning models: a simple learner $($e$.$g$.,$ Linear Regression$)$ and

Support Vector Regression $($SVR$).$ Students will preprocess the data, train the models,

evaluate their performance using appropriate regression metrics, and save the models.

Finally, they will deploy the best$-$performing model on a website for real$-$time health

outcome predictions.

Extended Objectives:

$$ Understand and preprocess health$-$related data.

$$ Perform data visualization and feature selection.

$$ Implement two regression machine learning models: Simple Learner and SVR$.$

$$ Compare the performance of the models using suitable regression metrics.

$$ Save and deploy the best model into a website.

Steps:

$1.$ Data Preprocessing

$$ Load the health$-$related dataset.

$$ Handle missing values and outliers appropriately.

$$ Perform feature encoding if necessary $($e$.$g$.,$ one$-$hot encoding for categorical

variables$).$

$$ Normalize or standardize the features if required.

$$ Split the dataset into training and testing sets.

$2.$ Exploratory Data Analysis

$$ Visualize the data to understand relationships between features and the target

variable.

$$ Use correlation matrices and scatter plots to identify significant features.

$$ Perform feature selection based on the analysis.

$3.$ Model $1$: Simple Learner $($e$.$g$.,$ Linear Regression$)$

$$ Train the Linear Regression model on the training data.

$$ Evaluate the model using regression metrics:

o Mean Absolute Error $($MAE$)$

o Mean Squared Error $($MSE$)$

o Root Mean Squared Error $($RMSE$)$

o R$-$squared $($Coefficient of Determination$)$

$$ Analyze residuals to check for patterns that might indicate issues with the model.

$4.$ Model $2$: Support Vector Regression $($SVR$)$

$$ Train the SVR model on the training data.

$$ Perform hyperparameter tuning $($e$.$g$.,$ using GridSearchCV$)$ to optimize the

model parameters.

$$ Evaluate the model using the same regression metrics as above.

$$ Analyze residuals for the SVR model as well.

$5.$ Model Comparison and Selection

$$ Compare the performance of the two models using the evaluation metrics.

$$ Discuss the trade$-$offs between the models $($e$.$g$.,$ complexity vs$.$ performance$).$

$$ Based on the evaluation, choose the best$-$performing model.

$$ Save the best model for future use.

$6.$ Deployment

$$ Deploy the best$-$performing model into a website using a web framework like

Flask or Django.

$$ Create a user$-$friendly interface where users can input health$-$related features.

$$ Ensure the website returns the predicted health outcome based on user inputs.

$$ Test the website for various input scenarios to ensure reliability.

Deliverables:

$$ Code Notebook: A Jupyter notebook or Python script with the implementation of

data preprocessing, both models, and their evaluations.

$$ Report: A brief report summarizing the preprocessing steps, visualizations, model

implementations, performance comparisons, and the deployment process.

$$ Deployed Website: A working demo of the website where users can input data and

receive predictions. Provide the link or instructions to access the demo.

Evaluation Criteria:

$$ Data Preprocessing: Proper handling of missing values, outliers, and appropriate

feature encoding.

$$ Data Visualization: Clear and insightful visualizations that aid in understanding the

data.

$$ Model Implementation: Correct implementation of Linear Regression and SVR

models, including hyperparameter tuning for SVR$.$

$$ Model Evaluation: Use of appropriate regression metrics and thorough analysis of

results.

$$ Model Selection: Logical comparison and rationale for selecting the best model.

$$ Deployment: Successful deployment of the best model into a functional and user$-$

friendly website

you must explain each step you gonna do in sentences especially the mean square eror