Actividad de Regresión Lineal

Aplicar una técnica de regresión (Regresión Lineal) sobre el valor medio de un hogar según distritos en California.

Recordad:

El proceso de aprendizaje automático suele constar de una serie de pasos. Por lo general, estos son: - Preparación de datos. Carga y limpieza de datos > Dataset: California-housing-dataset - Selección de atributos o métricas adecuadas - Selección de la técnica a aplicar > LinearRegression - Ajuste de los hiperparámetros. - Evaluación del modelo.

[1]:

# 0. Librerías !

from sklearn.datasets import fetch_california_housing
import pandas as pd
#https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html
from sklearn.linear_model import LinearRegression


[2]:

# 1º. Cargamos datos (de un dataset de ejemplo)

# https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_california_housing.html
# https://scikit-learn.org/stable/datasets/real_world.html#california-housing-dataset

data = fetch_california_housing(as_frame=True)
print(data.feature_names)

df = data.frame
print(df.head())

['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude']
   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \
0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88
1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86
2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85
3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85
4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85

   Longitude  MedHouseVal
0    -122.23        4.526
1    -122.22        3.585
2    -122.24        3.521
3    -122.25        3.413
4    -122.25        3.422

[3]:

df.describe()

[3]:
MedInc HouseAge AveRooms AveBedrms Population AveOccup Latitude Longitude MedHouseVal
count 20640.000000 20640.000000 20640.000000 20640.000000 20640.000000 20640.000000 20640.000000 20640.000000 20640.000000
mean 3.870671 28.639486 5.429000 1.096675 1425.476744 3.070655 35.631861 -119.569704 2.068558
std 1.899822 12.585558 2.474173 0.473911 1132.462122 10.386050 2.135952 2.003532 1.153956
min 0.499900 1.000000 0.846154 0.333333 3.000000 0.692308 32.540000 -124.350000 0.149990
25% 2.563400 18.000000 4.440716 1.006079 787.000000 2.429741 33.930000 -121.800000 1.196000
50% 3.534800 29.000000 5.229129 1.048780 1166.000000 2.818116 34.260000 -118.490000 1.797000
75% 4.743250 37.000000 6.052381 1.099526 1725.000000 3.282261 37.710000 -118.010000 2.647250
max 15.000100 52.000000 141.909091 34.066667 35682.000000 1243.333333 41.950000 -114.310000 5.000010

Attribute information: - MedInc median income in block group - HouseAge median house age in block group - AveRooms average number of rooms per household - AveBedrms average number of bedrooms per household - Population block group population - AveOccup average number of household members - Latitude block group latitude - Longitude block group longitude

The target variable is the median house value for California districts, expressed in hundreds of thousands of dollars ($100,000).

Tu turno

[9]:

#Todo empieza aquí...

License: CC BY 4.0 Isaac Lera and Gabriel Moya Universitat de les Illes Balears isaac.lera@uib.edu, gabriel.moya@uib.edu