# Product Demand Prediction with Spatial Graph Neural Networks
## 4 Experiments
### Data Analysis  
Univariate analysis: In the univariate analysis section of our study, we delve into an in-depth examination of six pivotal features deemed to have substantial importance in understanding the dynamics of our dataset. These features include deal probability, price, region, city, top-level category, and fine-grain category. This analysis aims to shed light on the individual characteristics and distributions of these features, providing foundational insights for further multivariate analysis. The distribution histograms for such features have been provided in the Figures 1 and 2. From such figures, we can obtain the following observations:  
* deal probability: An initial observation from the distribution histograms indicates a pronounced long-tail distribution issue with the deal probability feature. Notably, approximately 65% of the ads exhibit a zero deal probability, signifying a substantial portion of ads that do not culminate in a transaction. Conversely, a minimal fraction of ads achieve a deal probability of 1, indicating a successful sale. This distribution suggests a high variance in the likelihood of deals being closed across the dataset. * price: Prior to analysis, the price feature undergoes a logarithmic transformation to normalize its distribution. Post-transformation, the price distribution approximates a normal distribution, as evidenced by the histograms. This transformation mitigates the skewness originally present in the data, facilitating more meaningful statistical analysis and interpretation. * region: The analysis of the region feature reveals a geographical disparity in ad postings. The Krasnodar region emerges as the most prominent area for ad postings, followed closely by the Sverdlovsk and Rostov regions. This distribution highlightsthe regional variances in marketplace activity, potentially influenced by factors such as population density and economic conditions. * City: Delving into the city feature, we observe that the highest number of ads are posted in Krasnodar and Ekaterinburg cities. Subsequent rankings include Novosibirsk, Rostov-na-Donu, and Nizhny Novgorod cities. This urban-centric distribution underscores the role of major cities as hubs for online marketplace transactions, possibly attributed to their larger populations and higher internet penetration rates. * top-level category: The top-level category feature analysis reveals a dominant preference for posting ads in the \"Personal things\" category, accounting for more than 0.6 million users. Following this, the categories for \"Home and Cottages\" and \"Consumer Electronics\" are notable, with approximately 0.2 million users posting ads in each. This distribution indicates a significant inclination towards selling personal belongings, with a notable interest in home-related items and electronics. * fine-grain category: Within the subcategories, \"Clothes, shoes, accessories\", \"Children's clothing and footwear\", and \"Goods for children and toys\" emerge as the top three, each with around 0.3 million postings. This detailed breakdown within the fine-grain category feature further elucidates consumer behavior, highlighting a strong market for personal and children-related items. The univariate analysis of these selected features, supported by the distribution histograms in Figures 1 and 2, provides a comprehensive overview of the dataset's characteristics. This foundational understanding paves the way for more intricate multivariate analyses and predictive modeling, with the ultimate goal of enhancing the accuracy and efficiency of product demand prediction in the online marketplace. Bivariate analysis: In the exploration of bivariate relationships within our dataset, we delve into the interactions between deal probability and four critical features: region, city, top-level category, and user type. This analysis aims to uncover the nuanced dynamics that these features may have with the likelihood of a deal being closed.