1. Conduct a simple exploratory data analysis (EDA) to gain insights into the data's characteristics. Throughout this process, identify the attributes that you would select to predict the 'SalePrice' and assess the presence of multicollinearity. 
2. Examine the data for potential issues such as duplicates and formatting inconsistencies in the selected attributes. Perform data preprocessing if necessary. If data preprocessing is not required, provide explanations supported by relevant validations.
3. Develop a machine learning model to predict the 'Sale Price' and evaluate its performance. Describe the specific type of model you selected, the rationale behind your choice, and provide justification for your model selection. Additionally, comment on the accuracy of the model. 
4. Determine whether the developed model is statistically significant. Explain

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