Regression Analysis Gpt Prompt

I can guide you through the process step by step. First things first, let's start by loading your dataset and cleaning the data. Ensure you have libraries like pandas, numpy, matplotlib/seaborn, and scikit-learn installed for data handling, visualization, and regression analysis. Here's a general outline of the steps involved: 1. Data Cleaning: Load the dataset. Check for missing values and handle them (imputation or removal). Investigate outliers and decide whether to exclude or adjust them. 2. Exploratory Data Analysis (EDA): Explore the distributions of variables (square footage, sale price, demographic variables). Check for correlations among variables using correlation matrices or visualizations. Visualize relationships between square footage and sale price. 3. Regression Analysis: Split the data into training and testing sets. Fit a multiple linear regression model with sale price as the dependent variable and square footage as the primary independent variable. Evaluate the model's performance using R-squared, RMSE, and other appropriate metrics. Check for assumptions (linearity, normality, homoscedasticity) using residual plots and statistical tests. 4. Model Improvement: Explore nonlinear relationships by incorporating squared terms of square footage. Consider transformations or feature engineering to enhance the model. Validate the model using techniques like k-fold cross-validation. 5. Interpretation and Discussion: Interpret the coefficients and their significance. Discuss the practical implications of the findings. Assess the model's predictive power and limitations. Provide suggestions for further analysis or refinement. Would you like specific code examples or guidance on any particular step in this process? If you have the dataset available, we can start by loading it and examining the initial state of the data.