Ozyegin University CS 452 - Data Science with Python course project on Melbourne Housing data.
This paper focuses on analyzing the Melbourne housing market by analyzing various factors that affect house prices. To ensure data quality, the project includes extensive data cleaning, preprocessing, and feature engineering and to gain understanding of the dataset, descriptive statistics and exploratory data analysis approaches are applied. These approaches reveal correlations between the target variable(price) and categorical and numerical variables. Finally, various regression models are implemented to predict house prices, with assessment metrics showing the effectiveness of the models. These models include Multi-Linear Regression, Multi-Linear Regression with categoical variables, and Random Forest Regression.
Note: Note that this is a project made by beginners, so there may be fatal errors such as overfitting etc in the results.