This study conducted a thorough examination of residential property data in the Abbotsford area of M elbourne with the goal of identifying significant trends in housing, regional patterns, and variables aff ecting home values.The dataset contained a number of elements, such as neighborhood information, g eographic coordinates, transaction details, and property attributes.Using a variety of techniques, inclu ding mean imputation, forward filled imputation, machine learning algorithms, and discarding missin g data, the study started with the identification and treatment of missing values.Particularly in the pric e variable, outliers were found, and boxplots and other visualization tools were used for outlier analysi s.For additional analysis, numerical values representing the categorical variables were converted.To in vestigate the distributions of numerical variables and comprehend connections between variables with a focus on correlations with home prices-univariate and bivariate analyses were carried out. Feature engineering, covariance analysis, ANOVA testing, and predictive modeling with regression algorithms like Random Forest, XGBoost, and Support Vector Machine (SVM) were all part of the quantitative analysis process. Metrics like Mean Absolute Error (MAE) were used to assess the performance of the model; the results showed that XGBoost was the most accurate predictor of housing prices. Significant factors influencing home prices were identified by the study, such as building area, property type, number of rooms, and geographic considerations including proximity to important sites. Each component was analyzed in terms of its relative relevance, and the building area and land size. It was noted how the constructed model has limits, such as overfitting and the need for more model refining. The results offer insightful information to scholars, politicians, and real estate professionals who are interested in the dynamics of the housing market.

This research intends to use machine learning techniques to estimate the heating load class of residential buildings, allowing for the design of more energy-efficient structures. The dataset utilized in the analysis comprises of seven hundred residential building examples, each with variables such as relative compactness, surface area, roof area, overall height, orientation, glazing area, and glazing area distribution. The target variable, heating load class, is classified as low, medium, or high. The analysis begins with a thorough overview of the major variables, which includes summary statistics, correlation analysis, and visuals. Decision tree models are built in two ways: using the complete dataset and adopting a 10-fold cross-validation strategy. Additionally, a Neural Net Multi-Layer Perceptron (NN-MLP) model is run and compared to the decision tree models. The results show that all models have high accuracy rates, with the NN-MLP model having the best at 99.57%. Comparative research indicates that the NN-MLP model outperforms decision tree approaches in forecasting heating load classes. However, both models perform well in accurately categorizing heating load classes. Overall, this work emphasizes the effectiveness of machine learning algorithms in forecasting heating load classes, as well as their potential for improving the energy efficiency of residential structures.

The post-pandemic rebound in Sri Lanka's tourism sector offers a vital opportunity for well-thought-out hotel development. This study identifies the best places for new hotels across the nation using machine learning and geospatial data science methods. Through an extensive dataset analysis of the remaining hotels, which includes district-level characteristics, room capacity, and geographic coordinates, we reveal spatial patterns and clusters that influence the choice of sites. The research methodology evaluates the spatial distribution of hotels and pinpoints market gaps by integrating data processing, geospatial visualization, and K-means clustering. Different geographic clusters that indicate high potential areas for new hotel development are revealed by our findings. Predictive analytics is also used to recommend the best locations based on variables like hotel quality, room capacity, and proximity to popular tourist destinations. The study's conclusions offer insightful recommendations for the hospitality sector's players, offering a data-driven basis for choices about where to locate hotels, how to distribute resources, and how to organize regions. This research helps Sri Lanka's hotel industry grow strategically by utilizing geospatial data and machine learning to make sure that new projects are positioned for long-term success and in line with market demand.