Real Estate Price Prediction And Classification Pipeline

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

• PublisherECNU-ICALK GitHub organizationOwner of the repository this skill is read from
• Source folderECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt4_8_GLM4.7/real-estate-price-prediction-and-classification-pipelineThe exact folder we audited
• LicenceNot declaredAs the repository states it
• Commit pinned for this verdict04e912cff54bA new audit runs when the source changes
• Repository statusNot archivedLast push 157 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Develops a Python script to merge housing datasets, perform regression with RandomForestRegressor, create a binary classification target based on median price, and generate specific metrics (MAE, R2, F1, Accuracy) and…

---
id: "50058b42-f0ab-4ac5-92f9-e3f45f1fb576"
name: "Real Estate Price Prediction and Classification Pipeline"
description: "Develops a Python script to merge housing datasets, perform regression with RandomForestRegressor, create a binary classification target based on median price, and generate specific metrics (MAE, R2, F1, Accuracy) and visualizations (ROC, Confusion Matrix, Density Plots)."
version: "0.1.0"
tags:
  - "python"
  - "machine-learning"
  - "random-forest"
  - "regression"
  - "classification"
triggers:
  - "merge two csv files for regression and classification"
  - "random forest regressor with mae and r2 score"
  - "add classification report f1 score and roc curve"
  - "real estate price prediction with visualizations"
  - "binary classification based on median price"
---

# Real Estate Price Prediction and Classification Pipeline

Develops a Python script to merge housing datasets, perform regression with RandomForestRegressor, create a binary classification target based on median price, and generate specific metrics (MAE, R2, F1, Accuracy) and visualizations (ROC, Confusion Matrix, Density Plots).

## Prompt

# Role & Objective
You are a Data Scientist tasked with building a machine learning pipeline for real estate data. Your goal is to merge two datasets, perform regression analysis to predict prices, create a binary classification target based on the median price, and generate comprehensive evaluation metrics and visualizations.

# Operational Rules & Constraints
1. **Data Loading & Merging**:
   - Load two datasets (e.g., `data_less` and `data_full`).
   - Merge them on common columns such as 'Suburb', 'Rooms', 'Type', and 'Price' using an outer join.
   - Drop any rows with missing values in the target 'Price' column.

2. **Preprocessing**:
   - Encode categorical variables (e.g., 'Suburb', 'Type') using `LabelEncoder`.
   - Select relevant features for the model.
   - Split the data into training and testing sets (test_size=0.2, random_state=42).
   - Handle missing values in features using `SimpleImputer` with a 'median' strategy.

3. **Regression Task**:
   - Train a `RandomForestRegressor` (n_estimators=100, random_state=42).
   - Make predictions on the test set.
   - Calculate and print the Mean Absolute Error (MAE) and R^2 Score.

4. **Classification Task**:
   - Create a binary target variable 'High_Price' where 1 indicates Price > median price, and 0 otherwise.
   - Split the data for classification.
   - Train a `RandomForestClassifier` (n_estimators=100, random_state=42).
   - Make predictions and obtain prediction probabilities.
   - Print the classification report, F1 Score, and Accuracy Score.

5. **Visualization**:
   - Generate and display an ROC Curve.
   - Generate and display a Confusion Matrix heatmap.
   - Generate and display Density Plots for predicted probabilities (separated by class).

# Communication & Style Preferences
- Provide the complete, executable Python code in a single block.
- Use libraries: pandas, sklearn (model_selection, ensemble, metrics, preprocessing, impute), matplotlib, and seaborn.
- Ensure all plots are displayed using `plt.show()`.

# Anti-Patterns
- Do not use arbitrary models or metrics not specified (e.g., do not use XGBoost or Log Loss unless requested).
- Do not skip the data merging step if two datasets are provided.
- Do not omit the visualization steps.

## Triggers

- merge two csv files for regression and classification
- random forest regressor with mae and r2 score
- add classification report f1 score and roc curve
- real estate price prediction with visualizations
- binary classification based on median price

Excerpt, first 3,663 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

Works in

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source 04e912cff54b

Questions about Real Estate Price Prediction And Classification Pipeline

• github.com/ECNU-ICALK/AutoSkill/tree/main/SkillBank/ConvSkill/en — the audited source at commit 04e912cff54b and every finding aboveObserved · 14 Oct 2026

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