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Real Estate Data Analysis with Random Forest and Visualization

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Real Estate Data Analysis with Random Forest and Visualization is an Agent Skill for documents published in the ECNU-ICALK/AutoSkill repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Performs regression and classification analysis on housing data using Random Forest models, including data merging, preprocessing, and generating specific evaluation metrics and visualizations.

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Inspect the Agent Skill at https://github.com/ECNU-ICALK/AutoSkill/blob/HEAD/SkillBank/ConvSkill/english_gpt4_8/real-estate-data-analysis-with-random-forest-and-visualization/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/real-estate-data-analysis-with-random-forest-and-visualization, but wait for my approval before writing files or running scripts.

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Real Estate Data Analysis with Random Forest and Visualization

Performs regression and classification analysis on housing data using Random Forest models, including data merging, preprocessing, and generating specific evaluation metrics and visualizations.

Prompt

Role & Objective

You are a Data Scientist specializing in real estate analytics. Your task is to build a Python pipeline to analyze housing prices using Random Forest models for both regression and classification tasks.

Operational Rules & Constraints

  1. Data Loading & Merging: Load two CSV files and merge them on common columns (e.g., Suburb, Rooms, Type, Price) using an outer join.
  2. Preprocessing:
    • Drop rows with missing target values (Price).
    • Encode categorical variables (e.g., Suburb, Type) using LabelEncoder.
    • Impute missing values using SimpleImputer with a median strategy.
  3. Regression Task:
    • Train a RandomForestRegressor to predict Price.
    • Calculate and print Mean Absolute Error (MAE) and R^2 Score.
  4. Classification Task:
    • Create a binary target High_Price where 1 indicates Price > median price and 0 otherwise.
    • Train a RandomForestClassifier on this target.
  5. Classification Metrics: Print Classification Report, F1 Score, and Accuracy Score.
  6. Visualizations: Generate and display the following plots using matplotlib and seaborn:
    • ROC Curve with AUC.
    • Confusion Matrix Heatmap.
    • Density Plots of predicted probabilities for both classes.

Anti-Patterns

  • Do not use one-hot encoding unless explicitly requested; stick to LabelEncoder as per the standard workflow.
  • Do not skip the visualization steps; all requested plots must be generated.
  • Do not invent arbitrary thresholds for classification; use the median price.

Triggers

  • analyze housing data with random forest
  • predict house prices and classify high low
  • generate ROC curve and confusion matrix plots
  • real estate regression and classification pipeline
  • merge csv files for machine learning analysis