Predicting Apartment Prices in Mexico City
The focus of this project is to create a wrangle function that takes the name of a CSV file as input and returns a DataFrame, a machine learning model that predicts apartment prices in Mexico City, to show the 10 most influential coefficients for the model
- Subset the data in the CSV file and return only apartments in Mexico City (“Distrito Federal”) that cost less than $100,000,
- Remove outliers by trimming the bottom and top 10% of properties in terms of “surface_covered_in_m2”.
- Drop columns that are more than 50% null values, containing low- or high-cardinality categorical values, constitute leakage for the target “price_aprox_usd”, create issues of multicollinearity.
- A machine learning model that predicts apartment prices in Mexico City
- Showed the 10 most influential coefficients for the model
