$**$PYTHON PLEASE$**$

Objective: Build a classification model to predict the quality of wines based on various features using both Decision Trees and Support Vector Machines.

Dataset: You can use the Wine Quality dataset, which is available on the UCI Machine Learning Repository. This dataset contains various chemical properties of wines and their associated quality ratings.

Tasks:

$$ Data Exploration and Preprocessing:

$$ Load the dataset and explore its features and target variable.

$$ Check for missing values and handle them appropriately.

$$ Explore the distribution of the target variable $($wine quality$).$

$$ Data Visualization:

$$ Visualize the distribution of each feature and their relationship with the target variable.

$$ Utilize visualizations such as histograms, scatter plots, or box plots to gain insights into the data.

$$ Decision Tree Model:

$$ Implement a Decision Tree classification model to predict wine quality.

$$ Tune hyperparameters $($e$.$g$.,$ max depth, min samples split$)$ using cross$-$validation.

$$ Evaluate the model's performance using metrics such as accuracy, precision, recall, and F$1-$score.

$$ Support Vector Machine $($SVM$)$ Model:

$$ Implement an SVM classification model to predict wine quality.

$$ Experiment with different kernel functions $($e$.$g$.,$ linear, radial basis function$)$ and tune other hyperparameters using cross$-$validation.

$$ Evaluate the SVM model's performance using the same metrics as the Decision Tree model.

$$ Comparison and Ensemble:

$$ Compare the performance of the Decision Tree and SVM models.

$$ Explore the possibility of creating an ensemble model that combines the predictions of both models and evaluate its performance.

$$ Model Interpretation:

$$ Visualize the decision tree to understand how the model is making decisions.

$$ Explore the support vectors in the SVM model to gain insights into its decision boundaries.

$$ Deployment:

$$ If feasible, deploy the final model as a simple web application or API using frameworks like Flask or FastAPI.

Deliverables:

$$ Python script containing the code.

$$ Visualizations showcasing data exploration and model performance.

$$ A brief report discussing the findings, challenges encountered, and potential improvements.

This project will provide hands$-$on experience in implementing and comparing classification models using both Decision Trees and Support Vector Machines. Additionally, it offers an opportunity to explore ensemble techniques for improved predictive performance.