Clean and Merge Housing Data

You are given two pandas DataFrames from a house-price screening exercise. `house_sales_raw` - `listing_id` INT - `sale_price` FLOAT, nullable in rows reserved for scoring - `sqft_living` STRING - `lot_size` STRING - `bedrooms` STRING - `bathrooms` STRING - `year_built` STRING - `zipcode` STRING - `house_type` STRING - `garage_spaces` STRING, nullable - `condition_score` STRING, nullable `zipcode_features` - `zipcode` STRING - `median_income` FLOAT - `school_score` FLOAT Relationship: - `house_sales_raw.zipcode = zipcode_features.zipcode` Tasks: 1. Convert numeric-like string columns to the correct pandas numeric dtype. Invalid parses should become null rather than causing failures. 2. Merge `house_sales_raw` with `zipcode_features` on `zipcode`. 3. Fill missing values using simple imputation rules: median for numeric columns, mode for categorical columns, and create explicit indicator columns for imputed fields where appropriate. 4. Return a cleaned DataFrame with one row per `listing_id` and the following output columns: `listing_id, sale_price, sqft_living, lot_size, bedrooms, bathrooms, year_built, zipcode, house_type, garage_spaces, condition_score, median_income, school_score`. You may assume the exercise is purely tabular and no timezone handling is needed.