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).
npx skills add ECNU-ICALK/AutoSkill --skill real-estate-price-prediction-and-classification-pipeline --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 v
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What does the Real Estate Price Prediction and Classification Pipeline skill do?
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).
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill real-estate-price-prediction-and-classification-pipeline --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.