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Buenos_Aires_Real_Estate: Predicting Price with Location

# Imported libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

import plotly.express as px
import plotly.graph_objects as go

from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline, make_pipeline

In this lesson, We're going to create a more complex wrangle function, use it to clean more data, and build a model that considers more features when predicting apartment price.

Import data

df = pd.read_csv("buenos-aires-real-estate-1.csv")
df.head()

Task 2.1.1: Write a function named wrangle that takes a file path as an argument and returns a DataFrame.

def wrangle(filepath):
    
    # Read CSV file
    df = pd.read_csv(filepath)

    # Subset data: Apartments in "Capital Federal", less than 400,000
    mask_ba = df["place_with_parent_names"].str.contains("Capital Federal")
    mask_apt = df["property_type"] == "apartment"
    mask_price = df["price_aprox_usd"] < 400_000
    df = df[mask_ba & mask_apt & mask_price]

    # Subset data: Remove outliers for "surface_covered_in_m2"
    low, high = df["surface_covered_in_m2"].quantile([0.1, 0.9])
    mask_area = df["surface_covered_in_m2"].between(low, high)
    df = df[mask_area]
    
    #Subset split for "lat" and "lon"
    df[["lat", "lon"]] = df["lat-lon"].str.split(",", expand=True).astype(float)
    df.drop(columns=["lat-lon"] , inplace=True)

    return df

Task 2.1.2: Use your wrangle function to create a DataFrame from the CSV file data/buenos-aires-real-estate-1.csv.

frame1 = wrangle("buenos-aires-real-estate-1.csv")
print(frame1.info())
frame1.head()

Task 2.2.3: Use you revised wrangle function create a DataFrames frame2 from the file data/buenos-aires-real-estate-2.csv.

frame2 = wrangle("buenos-aires-real-estate-2.csv")
print("Frame2 Shape:", frame2.shape)

frame2.head()

Task 2.2.4: Use pd.concat to concatenate frame1 and frame2 into a new DataFrame df. Make sure you set the ignore_index argument to True

df = pd.concat([frame1, frame2])
print(df.info())
df.head()

Explore

Task 2.2.5: Create a Mapbox scatter plot that shows the location of the apartments in df.

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