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Regression Analysis Gpt Prompt

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Embark on a data-driven journey with this comprehensive regression analysis prompt. Uncover intricate relationships within your dataset, exploring the impact of variables on your outcomes. From data cleaning to model interpretation, this prompt equips you with a step-by-step roadmap, ensuring a thorough understanding of your data's story. Enhance your analysis with diverse regression techniques and insightful model evaluations, guiding you to meaningful conclusions and actionable insights.
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After purchasing, you will gain access to the prompt file which you can use with GPT or the app builder. You'll receive 20 free generation credits with this purchase. By purchasing this prompt, you agree to our terms of service.
Over 1 month ago

Prompt Details

Model
Chat - GPT-4 (gpt-4)
Token size
291 ($0.06111 / call)
Example input
[describe your dataset briefly] [independent variable placeholder]
Example output
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.
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