Research paper House Price Prediction System
Sample solution
Dante Alighieri played a critical role in the literature world through his poem Divine Comedy that was written in the 14th century. The poem contains Inferno, Purgatorio, and Paradiso. The Inferno is a description of the nine circles of torment that are found on the earth. It depicts the realms of the people that have gone against the spiritual values and who, instead, have chosen bestial appetite, violence, or fraud and malice. The nine circles of hell are limbo, lust, gluttony, greed and wrath. Others are heresy, violence, fraud, and treachery. The purpose of this paper is to examine the Dante’s Inferno in the perspective of its portrayal of God’s image and the justification of hell.
In this epic poem, God is portrayed as a super being guilty of multiple weaknesses including being egotistic, unjust, and hypocritical. Dante, in this poem, depicts God as being more human than divine by challenging God’s omnipotence. Additionally, the manner in which Dante describes Hell is in full contradiction to the morals of God as written in the Bible. When god arranges Hell to flatter Himself, He commits egotism, a sin that is common among human beings (Cheney, 2016). The weakness is depicted in Limbo and on the Gate of Hell where, for instance, God sends those who do not worship Him to Hell. This implies that failure to worship Him is a sin.
God is also depicted as lacking justice in His actions thus removing the godly image. The injustice is portrayed by the manner in which the sodomites and opportunists are treated. The opportunists are subjected to banner chasing in their lives after death followed by being stung by insects and maggots. They are known to having done neither good nor bad during their lifetimes and, therefore, justice could have demanded that they be granted a neutral punishment having lived a neutral life. The sodomites are also punished unfairly by God when Brunetto Lattini is condemned to hell despite being a good leader (Babor, T. F., McGovern, T., & Robaina, K. (2017). While he commited sodomy, God chooses to ignore all the other good deeds that Brunetto did.
Finally, God is also portrayed as being hypocritical in His actions, a sin that further diminishes His godliness and makes Him more human. A case in point is when God condemns the sin of egotism and goes ahead to commit it repeatedly. Proverbs 29:23 states that “arrogance will bring your downfall, but if you are humble, you will be respected.” When Slattery condemns Dante’s human state as being weak, doubtful, and limited, he is proving God’s hypocrisy because He is also human (Verdicchio, 2015). The actions of God in Hell as portrayed by Dante are inconsistent with the Biblical literature. Both Dante and God are prone to making mistakes, something common among human beings thus making God more human.
To wrap it up, Dante portrays God is more human since He commits the same sins that humans commit: egotism, hypocrisy, and injustice. Hell is justified as being a destination for victims of the mistakes committed by God. The Hell is presented as being a totally different place as compared to what is written about it in the Bible. As a result, reading through the text gives an image of God who is prone to the very mistakes common to humans thus ripping Him off His lofty status of divine and, instead, making Him a mere human. Whether or not Dante did it intentionally is subject to debate but one thing is clear in the poem: the misconstrued notion of God is revealed to future generations.
References
Babor, T. F., McGovern, T., & Robaina, K. (2017). Dante’s inferno: Seven deadly sins in scientific publishing and how to avoid them. Addiction Science: A Guide for the Perplexed, 267.
Cheney, L. D. G. (2016). Illustrations for Dante’s Inferno: A Comparative Study of Sandro Botticelli, Giovanni Stradano, and Federico Zuccaro. Cultural and Religious Studies, 4(8), 487.
Verdicchio, M. (2015). Irony and Desire in Dante’s” Inferno” 27. Italica, 285-297.
Sample Answer
Sample Answer
Predictive Modeling for House Prices: A Research Study
Abstract
Predicting house prices accurately is crucial for both buyers and sellers in the real estate market. This research paper aims to develop a robust house price prediction system using machine learning algorithms. By analyzing various features such as location, size, amenities, and historical sales data, the model will forecast house prices with high accuracy. The study will compare the performance of different algorithms and evaluate the impact of feature selection and data preprocessing techniques on prediction accuracy.
Introduction
The real estate market is dynamic and influenced by numerous factors that can impact house prices. Traditional methods of pricing properties often rely on manual appraisal processes or historical trends, which may not always reflect the current market conditions accurately. In this research study, we will leverage machine learning techniques to build a predictive model for house prices. By training the model on a dataset comprising relevant features such as square footage, number of bedrooms and bathrooms, location desirability, and recent sales data, we aim to create a tool that can provide reliable price estimates for residential properties.
Methodology
The research will involve the following key steps:
1. Data Collection: Gathering a comprehensive dataset containing information on house features, neighborhood characteristics, and historical sales prices.
2. Data Preprocessing: Cleaning the dataset, handling missing values, encoding categorical variables, and standardizing numerical features.
3. Feature Selection: Identifying the most relevant features that have a significant impact on house prices using techniques like correlation analysis and feature importance.
4. Model Development: Implementing machine learning algorithms such as Linear Regression, Decision Trees, Random Forest, and Gradient Boosting to build predictive models.
5. Model Evaluation: Assessing the performance of each model using metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared value.
6. Hyperparameter Tuning: Fine-tuning the models to optimize performance and improve prediction accuracy.
7. Deployment: Creating a user-friendly interface for the house price prediction system that allows users to input property details and obtain estimated prices.
Conclusion
Through this research study, we aim to develop an accurate and reliable house price prediction system that can benefit both buyers and sellers in the real estate market. By leveraging machine learning algorithms and advanced data analysis techniques, we aim to enhance the efficiency and effectiveness of property valuation processes. The findings of this study will contribute to the field of real estate analytics and provide valuable insights into optimizing house price predictions for informed decision-making in the housing market.
