{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fetch the dataset"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.datasets import fetch_california_housing\n",
"california = fetch_california_housing()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dataset characteristics"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
".. _california_housing_dataset:\n",
"\n",
"California Housing dataset\n",
"--------------------------\n",
"\n",
"**Data Set Characteristics:**\n",
"\n",
" :Number of Instances: 20640\n",
"\n",
" :Number of Attributes: 8 numeric, predictive attributes and the target\n",
"\n",
" :Attribute Information:\n",
" - MedInc median income in block\n",
" - HouseAge median house age in block\n",
" - AveRooms average number of rooms\n",
" - AveBedrms average number of bedrooms\n",
" - Population block population\n",
" - AveOccup average house occupancy\n",
" - Latitude house block latitude\n",
" - Longitude house block longitude\n",
"\n",
" :Missing Attribute Values: None\n",
"\n",
"This dataset was obtained from the StatLib repository.\n",
"http://lib.stat.cmu.edu/datasets/\n",
"\n",
"The target variable is the median house value for California districts.\n",
"\n",
"This dataset was derived from the 1990 U.S. census, using one row per census\n",
"block group. A block group is the smallest geographical unit for which the U.S.\n",
"Census Bureau publishes sample data (a block group typically has a population\n",
"of 600 to 3,000 people).\n",
"\n",
"It can be downloaded/loaded using the\n",
":func:`sklearn.datasets.fetch_california_housing` function.\n",
"\n",
".. topic:: References\n",
"\n",
" - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n",
" Statistics and Probability Letters, 33 (1997) 291-297\n",
"\n"
]
}
],
"source": [
"print(california.DESCR)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Data shape = (20640, 8)\n",
"Target shape = (20640,)\n"
]
}
],
"source": [
"print(f' Data shape = {california.data.shape}')\n",
"print(f'Target shape = {california.target.shape}')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['MedInc',\n",
" 'HouseAge',\n",
" 'AveRooms',\n",
" 'AveBedrms',\n",
" 'Population',\n",
" 'AveOccup',\n",
" 'Latitude',\n",
" 'Longitude']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"california.feature_names"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load data into a dataframe and explore"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5.00001"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"california_df = pd.DataFrame(california.data, \n",
" columns=california.feature_names)\n",
"california_df['MedHouseValue'] = pd.Series(california.target)\n",
"\n",
"max(california_df['MedHouseValue'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Explore"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" MedInc | \n",
" HouseAge | \n",
" AveRooms | \n",
" AveBedrms | \n",
" Population | \n",
" AveOccup | \n",
" Latitude | \n",
" Longitude | \n",
" MedHouseValue | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 8.3252 | \n",
" 41.0 | \n",
" 6.9841 | \n",
" 1.0238 | \n",
" 322.0 | \n",
" 2.5556 | \n",
" 37.88 | \n",
" -122.23 | \n",
" 4.526 | \n",
"
\n",
" \n",
" | 1 | \n",
" 8.3014 | \n",
" 21.0 | \n",
" 6.2381 | \n",
" 0.9719 | \n",
" 2401.0 | \n",
" 2.1098 | \n",
" 37.86 | \n",
" -122.22 | \n",
" 3.585 | \n",
"
\n",
" \n",
" | 2 | \n",
" 7.2574 | \n",
" 52.0 | \n",
" 8.2881 | \n",
" 1.0734 | \n",
" 496.0 | \n",
" 2.8023 | \n",
" 37.85 | \n",
" -122.24 | \n",
" 3.521 | \n",
"
\n",
" \n",
" | 3 | \n",
" 5.6431 | \n",
" 52.0 | \n",
" 5.8174 | \n",
" 1.0731 | \n",
" 558.0 | \n",
" 2.5479 | \n",
" 37.85 | \n",
" -122.25 | \n",
" 3.413 | \n",
"
\n",
" \n",
" | 4 | \n",
" 3.8462 | \n",
" 52.0 | \n",
" 6.2819 | \n",
" 1.0811 | \n",
" 565.0 | \n",
" 2.1815 | \n",
" 37.85 | \n",
" -122.25 | \n",
" 3.422 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" MedInc | \n",
" HouseAge | \n",
" AveRooms | \n",
" AveBedrms | \n",
" Population | \n",
" AveOccup | \n",
" Latitude | \n",
" Longitude | \n",
" MedHouseValue | \n",
"
\n",
" \n",
" \n",
" \n",
" | count | \n",
" 20640.0000 | \n",
" 20640.0000 | \n",
" 20640.0000 | \n",
" 20640.0000 | \n",
" 20640.0000 | \n",
" 20640.0000 | \n",
" 20640.0000 | \n",
" 20640.0000 | \n",
" 20640.0000 | \n",
"
\n",
" \n",
" | mean | \n",
" 3.8707 | \n",
" 28.6395 | \n",
" 5.4290 | \n",
" 1.0967 | \n",
" 1425.4767 | \n",
" 3.0707 | \n",
" 35.6319 | \n",
" -119.5697 | \n",
" 2.0686 | \n",
"
\n",
" \n",
" | std | \n",
" 1.8998 | \n",
" 12.5856 | \n",
" 2.4742 | \n",
" 0.4739 | \n",
" 1132.4621 | \n",
" 10.3860 | \n",
" 2.1360 | \n",
" 2.0035 | \n",
" 1.1540 | \n",
"
\n",
" \n",
" | min | \n",
" 0.4999 | \n",
" 1.0000 | \n",
" 0.8462 | \n",
" 0.3333 | \n",
" 3.0000 | \n",
" 0.6923 | \n",
" 32.5400 | \n",
" -124.3500 | \n",
" 0.1500 | \n",
"
\n",
" \n",
" | 25% | \n",
" 2.5634 | \n",
" 18.0000 | \n",
" 4.4407 | \n",
" 1.0061 | \n",
" 787.0000 | \n",
" 2.4297 | \n",
" 33.9300 | \n",
" -121.8000 | \n",
" 1.1960 | \n",
"
\n",
" \n",
" | 50% | \n",
" 3.5348 | \n",
" 29.0000 | \n",
" 5.2291 | \n",
" 1.0488 | \n",
" 1166.0000 | \n",
" 2.8181 | \n",
" 34.2600 | \n",
" -118.4900 | \n",
" 1.7970 | \n",
"
\n",
" \n",
" | 75% | \n",
" 4.7432 | \n",
" 37.0000 | \n",
" 6.0524 | \n",
" 1.0995 | \n",
" 1725.0000 | \n",
" 3.2823 | \n",
" 37.7100 | \n",
" -118.0100 | \n",
" 2.6472 | \n",
"
\n",
" \n",
" | max | \n",
" 15.0001 | \n",
" 52.0000 | \n",
" 141.9091 | \n",
" 34.0667 | \n",
" 35682.0000 | \n",
" 1243.3333 | \n",
" 41.9500 | \n",
" -114.3100 | \n",
" 5.0000 | \n",
"
\n",
" \n",
"
\n",
"