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| title: UrbanNest Rent Predictor | |
| emoji: "🏠" | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: docker | |
| app_port: 8501 | |
| pinned: false | |
| # Assignment 4: PropTech Startup Strategy - Rent Prediction Pipeline | |
| **Course**: Software Tools and Techniques for AI | |
| **Total Marks**: 20 | |
| **Submission**: Submit your GitHub repository link containing all code, logs, and screenshots. | |
| ## Problem Statement: UrbanNest Analytics | |
| You have just been hired as the Lead MLOps Engineer at **UrbanNest Analytics**, a fast-growing Property Technology (PropTech) startup. The company wants to launch a **Dynamic House Rent Prediction Engine** for four key cities: Mumbai, Pune, Delhi, and Hisar. | |
| Currently, the data science team has built a basic Random Forest model to predict rent (`price`), but as user demand grows, the startup faces two challenges: | |
| 1. **Cloud Computing Budgets:** Training large models exhaustively is too expensive. The founders want you to rigorously compare Hyperparameter Optimization techniques (Grid Search vs. Random Search vs. Bayesian Optimization) to find the most cost-effective method for achieving high accuracy without wasting compute time. | |
| 2. **Seamless Deployment:** The model needs to be accessible to end-users via a clean UI and safely packaged into Docker containers to avoid "it works on my machine" issues. | |
| Your objective is to finalize the model optimization, track your experiments, build a user-friendly frontend, and deploy it all using Docker and Hugging Face Spaces. | |
| ## Dataset | |
| You are provided with a dataset in the `Dataset` folder: | |
| * `train.csv`: Use this to train and perform Cross-Validation hyperparameter tuning. | |
| * `test.csv`: Use this to report your final model metrics. | |
| **Important Note on Data:** The data contains categorical features like `city`, `Status`, and `property_type`. You must appropriately encode these into numerical formats before feeding them to the Random Forest model and ensure your Streamlit frontend reverses this mapping properly. | |
| --- | |
| ## Tasks & Mark Distribution | |
| ### Task 1: Data Preprocessing, Optimization & Tracking (8 Marks) | |
| **Goal:** Prove to the founders which optimization strategy gives the best "bang for buck" and systematically track all experimental runs. | |
| * **Experiment Tracking:** You must explicitly initialize and utilize `trackio` in your notebook to track all evaluation metrics, parameter variations, and model versions systematically across your different experiments. | |
| * **Preprocessing:** You must keep all features from the dataset for the final UI predictions. Create a preprocessing pipeline that converts string categorical variables (like `city`, `location`, `Status`, `property_type`) into numbers. You must strictly use `sklearn.preprocessing.LabelEncoder` or manual mapping and **save these mappings** so your frontend can use them later. | |
| * **Optimization Comparison:** Using strictly `train.csv` and 5-fold Cross-Validation tune the hyperparameters of a **Random Forest Regressor** (`sklearn.ensemble.RandomForestRegressor`). You must strictly implement and compare: | |
| 1. **Grid Search** (using `sklearn.model_selection.GridSearchCV`) | |
| 2. **Random Search** (using `sklearn.model_selection.RandomizedSearchCV`) | |
| 3. **Bayesian Optimization** (using the `optuna` library, specifically creating a study with `optuna.create_study` and calling `.optimize`) | |
| * **Search Space Definition:** You **must** define your search spaces as follows to ensure a fair 60-trial budgeted comparison: | |
| * **Grid Search Space (exactly 60 combinations):** | |
| * `n_estimators`: `[50, 100, 150, 200]` | |
| * `max_depth`: `[10, 15, 20, 25, 30]` | |
| * `min_samples_split`: `[2, 5, 8]` | |
| * **Random Search & Bayesian Optimization Space:** | |
| * Search across the full integer ranges between the min and max bounds (choose max iterations to 60-100): | |
| * `n_estimators`: Integer values from `50` to `200` | |
| * `max_depth`: Integer values from `10` to `30` | |
| * `min_samples_split`: Integer values from `2` to `10` | |
| * **Evaluation & Plots:** You must generate and **save** (in the `plots/` folder) the following plots: | |
| 1. `trials_vs_error.png`: A line plot comparing the compute budget vs. the error. | |
| * **X-axis:** Number of iterations/trials. | |
| * **Y-axis:** Best mean cross-validation error found up to that iteration. | |
| * *Plot all three strategies (Grid, Random, Bayesian) overlaid on the same graph.* | |
| 2. `optuna_hyperparameter_space.png`: A plot of the hyperparameter space (using `optuna.visualization.plot_optimization_history` or `optuna.visualization.plot_contour`) to demonstrate how the Bayesian method explored the parameters. | |
| * **Final Testing & Reporting:** | |
| * Clearly **print/report** the best hyperparameters found by each of the three methods in your notebook. | |
| * Retrain your overall *best* model (the one with the lowest CV error) on the entire `train.csv`. | |
| * Predict on the untouched `test.csv` and report the final Mean Absolute Error (MAE). | |
| **Expected Outputs for Task 1:** | |
| * `train.ipynb` notebook containing your preprocessing, Cross-Validation tuning, `trackio` integration, final evaluation on `test.csv`, clearly reported best hyperparameters, and plot generating code. | |
| * The `models/` folder containing the final saved model file (e.g., `best_rf_model.pkl`) and necessary preprocessing encoders. | |
| * The `plots/` folder containing the two requested PNG plot images (`trials_vs_error.png` and `optuna_hyperparameter_space.png`). | |
| * A screenshot named `trackio_dashboard.png` (in the `screenshots/` folder) showing your logging dashboard/UI with the compared hyperparameters and scores. | |
| --- | |
| ### Task 2: Web Application Development (4 Marks) | |
| **Goal:** Build a user-facing dashboard for real estate agents. | |
| * **Frontend (Streamlit):** Build a standalone Streamlit dashboard. | |
| * Use `st.selectbox`, `st.number_input`, or `st.slider` for ALL input features corresponding to the dataset. | |
| * Load your saved model and Label Encoders using `pickle.load` directly in the script. | |
| * Upon button click (`st.button("Predict")`), apply the necessary preprocessing via your loaded encoders and invoke your model to get the prediction. | |
| * Display the result using `st.success` or `st.write`. | |
| **Expected Outputs for Task 2:** | |
| * `app.py` (Streamlit application code). | |
| * `requirements.txt` containing all required libraries (e.g., `streamlit`, `scikit-learn`, `trackio`, `optuna`). | |
| --- | |
| ### Task 3: Docker Containerization & Networking (4 Marks) | |
| **Goal:** Package the application so it can be easily scaled and run anywhere. | |
| * **Dockerfile:** Write a single `Dockerfile` in the root repository spanning a Python base image setup for the Streamlit app. Ensure it installs requirements and uses the correct `ENTRYPOINT` or `CMD` to run `streamlit run app.py`. Make sure the container exposes the correct Streamlit port (typically `8501`). | |
| * **Port Forwarding:** When building and running the container, you must bind your local machine's port to the container's exported Streamlit port so that it is accessible via `localhost` (e.g., via the `-p` flag). | |
| **Expected Outputs for Task 3:** | |
| * `Dockerfile` in the root repository. | |
| * A folder named `screenshots/` explicitly containing the following **three screenshots**: | |
| 1. `docker_build.png`: A screenshot showing the successful output of running your `docker build` command sequentially in your terminal. | |
| 2. `docker_ps.png`: A screenshot of the `docker ps` terminal command showing the container actively running with the correct port mappings. | |
| 3. `streamlit_working.png`: A screenshot of your web browser accessing the Streamlit app at `localhost:8501` to successfully get a prediction. | |
| --- | |
| ### Task 4: Cloud Deployment via Hugging Face Spaces (4 Marks) | |
| **Goal:** Show stakeholders a live, working distributed prototype. | |
| * **Streamlit App Deployment:** Deploy your Streamlit application using a **Docker Space template** on Hugging Face. The Space will build your container (using your root `Dockerfile`) and give you a public URL. | |
| * The final Streamlit Space application must be publicly accessible and return predictions. | |
| **Expected Outputs for Task 4:** | |
| * The **Streamlit Space URL** prominently placed at the very top of your GitHub repository's README. | |
| --- | |
| ## Submission Guidelines | |
| Create a GitHub repository and push all your files. **Your repository MUST strictly be Private**. You must add your respective TA as a collaborator who will be taking your viva on the day of your submission so they can review your code. | |
| Your repository MUST strictly look like this: | |
| ```text | |
| Assignment_4/ | |
| ├── README.md # Project description and HF Space URL | |
| ├── requirements.txt # Python dependencies | |
| ├── train.ipynb # Task 1: Tuning, trackio tracking, and Evaluation | |
| ├── app.py # Task 2: Streamlit application code | |
| ├── Dockerfile # Task 3: Streamlit Dockerfile | |
| ├── models/ | |
| │ └── best_rf_model.pkl # Task 1: Saved model and encoders | |
| ├── plots/ | |
| │ ├── trials_vs_error.png # Task 1 plot | |
| │ └── optuna_hyperparameter_space.png # Task 1 plot | |
| ├── Dataset/ # Provided CSVs | |
| └── screenshots/ | |
| ├── trackio_dashboard.png # Task 1 screenshot | |
| ├── docker_build.png # Task 3 screenshot | |
| ├── docker_ps.png # Task 3 screenshot | |
| └── streamlit_working.png # Task 3 screenshot | |
| ``` | |
| Submit the link to your GitHub repository when the google form is shared. Good luck, Lead Engineer! | |