In this assignment, you will build a complete end$-$to$-$end machine learning $($ML$)$ workflow using KizenML and Explainable AI $($XAI$)$ tools. You are free to choose any other tools or frameworks that support the workflow, including cloud services like AWS or Azure for deployment. The final deliverables include a screen recording explaining your process and detailed documentation.

Tasks and Marks Distribution:

$1.$Data Collection and Preprocessing $(4$ Marks$)$:

$$Task: Select an appropriate dataset and perform data preprocessing, including data cleaning, feature engineering, and scaling$/$normalization$.$

$$Details: Explain the choices made during preprocessing and how they impact the model. Use KizenML or other tools for AutoEDA if applicable.

$$Marks: $4$ Marks

$2.$Model Selection, Training, and Hyperparameter Tuning $(6$ Marks$)$:

$$Task: Train multiple models, tune hyperparameters, and select the best$-$performing model.

$$Details: Utilize tools like AutoML, KizenML, or others for model selection and hyperparameter tuning. Document the experimentation process and justify your model choice.

$$Marks: $6$ Marks

$3.$Explainable AI $($XAI$)$ Implementation $(6$ Marks$)$:

$$Task: Apply Explainable AI techniques to make your model$$s predictions interpretable.

$$Details: Use XAI tools such as SHAP, LIME, or others to provide insights into your model$$s decision$-$making process. Discuss the importance of interpretability in your model and how XAI tools helped achieve it$.$

$$Marks: $6$ Marks

$4.$Model Deployment Using Cloud Services $(6$ Marks$)$:

$$Task: Deploy your trained model using free$-$tier cloud services such as AWS or Azure.

$$Details: Create an API or web interface using services like AWS Lambda, Azure Functions, or any other cloud service. Demonstrate how to make predictions using the deployed model.

$$Marks: $6$ Marks

$5.$Workflow Documentation and Explanation $(8$ Marks$)$:

$$Task: Document the entire end$-$to$-$end workflow and provide a screen recording that explains the process.

$$Details:

$$Screen Recording $(4$ Marks$)$: Record a video walkthrough of your workflow, explaining each step and the tools used.

$$Documentation $(4$ Marks$)$: Provide detailed written documentation that includes code snippets, explanations of each step, challenges encountered, and how they were resolved.

$$Marks: $8$ Marks

Deliverables:

$$Screen Recording: A video explaining the end$-$to$-$end workflow, from data collection to model deployment and XAI implementation.

$$Code Repository: Submit a well$-$organized repository containing all code, including scripts for data preprocessing, model training, XAI implementation, and deployment.

$$Documentation: A detailed report that covers each step of the workflow, with explanations, challenges, and solutions.