In [7]:
#exploring our data and visualization
# train and create a linear regression model
#Evaluate our model
#Make prediction
#Calculate RMSE
#What if we consider mode/less features?
#acompanhado desde https://www.youtube.com/watch?v=URLwFGwCBb0&t=3835s
In [12]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
In [4]:
#import dataset y salvando en la variable house
house = pd.read_csv('kc_house_data.csv')
In [6]:
#ver las 5 primeras entradas do dataset
house.head()
Out[6]:
id date price bedrooms bathrooms sqft_living sqft_lot floors waterfront view ... grade sqft_above sqft_basement yr_built yr_renovated zipcode lat long sqft_living15 sqft_lot15
0 7129300520 20141013T000000 221900.0 3 1.00 1180 5650 1.0 0 0 ... 7 1180 0 1955 0 98178 47.5112 -122.257 1340 5650
1 6414100192 20141209T000000 538000.0 3 2.25 2570 7242 2.0 0 0 ... 7 2170 400 1951 1991 98125 47.7210 -122.319 1690 7639
2 5631500400 20150225T000000 180000.0 2 1.00 770 10000 1.0 0 0 ... 6 770 0 1933 0 98028 47.7379 -122.233 2720 8062
3 2487200875 20141209T000000 604000.0 4 3.00 1960 5000 1.0 0 0 ... 7 1050 910 1965 0 98136 47.5208 -122.393 1360 5000
4 1954400510 20150218T000000 510000.0 3 2.00 1680 8080 1.0 0 0 ... 8 1680 0 1987 0 98074 47.6168 -122.045 1800 7503

5 rows × 21 columns

In [8]:
#sacar informacoes do dataset
house.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 21613 entries, 0 to 21612
Data columns (total 21 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   id             21613 non-null  int64  
 1   date           21613 non-null  object 
 2   price          21613 non-null  float64
 3   bedrooms       21613 non-null  int64  
 4   bathrooms      21613 non-null  float64
 5   sqft_living    21613 non-null  int64  
 6   sqft_lot       21613 non-null  int64  
 7   floors         21613 non-null  float64
 8   waterfront     21613 non-null  int64  
 9   view           21613 non-null  int64  
 10  condition      21613 non-null  int64  
 11  grade          21613 non-null  int64  
 12  sqft_above     21613 non-null  int64  
 13  sqft_basement  21613 non-null  int64  
 14  yr_built       21613 non-null  int64  
 15  yr_renovated   21613 non-null  int64  
 16  zipcode        21613 non-null  int64  
 17  lat            21613 non-null  float64
 18  long           21613 non-null  float64
 19  sqft_living15  21613 non-null  int64  
 20  sqft_lot15     21613 non-null  int64  
dtypes: float64(5), int64(15), object(1)
memory usage: 3.5+ MB
In [10]:
#sacar principales datos generales como media, min, max etc...
house.describe()
Out[10]:
id price bedrooms bathrooms sqft_living sqft_lot floors waterfront view condition grade sqft_above sqft_basement yr_built yr_renovated zipcode lat long sqft_living15 sqft_lot15
count 2.161300e+04 2.161300e+04 21613.000000 21613.000000 21613.000000 2.161300e+04 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000
mean 4.580302e+09 5.400881e+05 3.370842 2.114757 2079.899736 1.510697e+04 1.494309 0.007542 0.234303 3.409430 7.656873 1788.390691 291.509045 1971.005136 84.402258 98077.939805 47.560053 -122.213896 1986.552492 12768.455652
std 2.876566e+09 3.671272e+05 0.930062 0.770163 918.440897 4.142051e+04 0.539989 0.086517 0.766318 0.650743 1.175459 828.090978 442.575043 29.373411 401.679240 53.505026 0.138564 0.140828 685.391304 27304.179631
min 1.000102e+06 7.500000e+04 0.000000 0.000000 290.000000 5.200000e+02 1.000000 0.000000 0.000000 1.000000 1.000000 290.000000 0.000000 1900.000000 0.000000 98001.000000 47.155900 -122.519000 399.000000 651.000000
25% 2.123049e+09 3.219500e+05 3.000000 1.750000 1427.000000 5.040000e+03 1.000000 0.000000 0.000000 3.000000 7.000000 1190.000000 0.000000 1951.000000 0.000000 98033.000000 47.471000 -122.328000 1490.000000 5100.000000
50% 3.904930e+09 4.500000e+05 3.000000 2.250000 1910.000000 7.618000e+03 1.500000 0.000000 0.000000 3.000000 7.000000 1560.000000 0.000000 1975.000000 0.000000 98065.000000 47.571800 -122.230000 1840.000000 7620.000000
75% 7.308900e+09 6.450000e+05 4.000000 2.500000 2550.000000 1.068800e+04 2.000000 0.000000 0.000000 4.000000 8.000000 2210.000000 560.000000 1997.000000 0.000000 98118.000000 47.678000 -122.125000 2360.000000 10083.000000
max 9.900000e+09 7.700000e+06 33.000000 8.000000 13540.000000 1.651359e+06 3.500000 1.000000 4.000000 5.000000 13.000000 9410.000000 4820.000000 2015.000000 2015.000000 98199.000000 47.777600 -121.315000 6210.000000 871200.000000
In [11]:
#sacar os nomes das columnas
house.columns
Out[11]:
Index(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',
       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',
       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',
       'lat', 'long', 'sqft_living15', 'sqft_lot15'],
      dtype='object')
In [15]:
#para dejar el grafico mayor
plt.figure(figsize=(10, 6))
#sacar un grafico price x sqft_living
plt.scatter(house.sqft_living, house.price)
plt.xlabel('sqft of house')
plt.ylabel('price of house')
Out[15]:
Text(0, 0.5, 'price of house')
In [24]:
#para dejar el grafico mayor
plt.figure(figsize=(10, 6))
#por la linea de regresion
sns.lmplot('sqft_living', 'price', data=house)
Out[24]:
<seaborn.axisgrid.FacetGrid at 0x7f55f6e64208>
<Figure size 720x432 with 0 Axes>
In [28]:
#por a heatmap donde se puede sacar el visual de la correlación
sns.heatmap(house.corr())
Out[28]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f55f48f2cf8>
In [30]:
#para ver la distribuición normal
sns.distplot(house['price'], color='red')
Out[30]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f55f4c04208>
In [35]:
#sacar boxplot
sns.boxplot(x='zipcode', y='price', data=house)
Out[35]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f55f3a1b2b0>
In [ ]: