Create a new environment
''' conda create -p realestate python==3.11 -y '''
The dataset used for this project contains the following variables:
RealEstate.csv dataset (414)
X2 house age: Age of the house in yearsX3 distance to the nearest MRT station: Distance from the house to the nearest Mass Rapid Transit (MRT) station, in meters.X4 number of convenience stores: Number of convenience stores nearbyX5 latitude: Latitude of the location of the houseX6 longitude: Longitude of the location of the houseY house price of unit area: Price of the house per unit area (price per square foot or square meter)(target)
The aim of this project is to deploy a machine learning model using Azure cloud services. For demo purposes, a clean dataset was selected, and only the Random Forest Regressor algorithm was trained. Hyperparameters for the model were optimized using Optuna.
Learning curve was plotted to conduct a Bias-Variance Analysis. Additionally, the feature importances were visualized to understand the relative importance of each feature in the model's predictions. The performance of the model was evaluated by comparing training and test results based on metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared (R^2), Adjusted R-squared, Mean Absolute Percentage Error (MAPE), and Max Error. training-notebook
Furthermore, a single decision tree from the forest was visualized to provide insight into the model's decision-making process. Residuals and prediction error plots were generated to assess the model's performance and identify areas for improvement.
Once the model was trained and evaluated, it was saved as a pickle file and utilized in the deployment process. An application was created using Flask, and Postman was used to obtain predictions via JSON data. The correctness of the predictions was verified in the created app.py file. Finally, the model was deployed via a web application hosted on Azure cloud services, providing users with access to real-time predictions.
