A Straightforward Introduction To Machine Learning With Python Implementation by Hossain Md. Akramul

Author:Hossain, Md. Akramul [Hossain, Md. Akramul]
Language: eng
Format: epub
Publisher: UNKNOWN
Published: 2021-07-12T00:00:00+00:00

5. House Price Prediction: A Regression Problem

[1]: # importing basic libaries

import numpy as np # fundamental package for array computing import pandas as pd # Powerful data structures for data analysis, time → series, and statistics

import matplotlib.pyplot as plt # basic plotting library

[2]: train = pd. read_csv("train.csv" ) test = pd. read_csv("test.csv" )

[3]: with open ("data_description.txt" , 'r' ) as f: description = f. read()

#print(description)

5.0.1 Preprocessing of Data [4]: train. head() [4]: Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape
0 1 60 RL 65.0 8450 Pave NaN Reg

1 2 20 RL 80.0 9600 Pave NaN Reg

2 3 60 RL 68.0 11250 Pave NaN IR1

3 4 70 RL 60.0 9550 Pave NaN IR1

4 5 60 RL 84.0 14260 Pave NaN IR1

LandContour Utilities ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold → Lvl AllPub ... 0 NaN NaN NaN 0 2

1 Lvl AllPub ... 0 NaN NaN NaN 0 5

2 Lvl AllPub ... 0 NaN NaN NaN 0 9

3 Lvl AllPub ... 0 NaN NaN NaN 0 2

4 Lvl AllPub ... 0 NaN NaN NaN 0 12

YrSold SaleType SaleCondition SalePrice

0 2008 WD Normal 208500

1 2007 WD Normal 181500

2 2008 WD Normal 223500

3 2006 WD Abnorml 140000

4 2008 WD Normal 250000

[5 rows x 81 columns]

[5]: test. head() [5]: Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape
0 1461 20 RH 80.0 11622 Pave NaN Reg

1 1462 20 RL 81.0 14267 Pave NaN IR1

2 1463 60 RL 74.0 13830 Pave NaN IR1

3 1464 60 RL 78.0 9978 Pave NaN IR1

4 1465 120 RL 43.0 5005 Pave NaN IR1

LandContour Utilities ... ScreenPorch PoolArea PoolQC Fence MiscFeature
0 Lvl AllPub ... 120 0 NaN MnPrv NaN

1 Lvl AllPub ... 0 0 NaN NaN Gar2

2 Lvl AllPub ... 0 0 NaN MnPrv NaN

3 Lvl AllPub ... 0 0 NaN NaN NaN

4 HLS AllPub ... 144 0 NaN NaN NaN

MiscVal MoSold YrSold SaleType SaleCondition

0 0 6 2010 WD Normal

1 12500 6 2010 WD Normal

2 0 3 2010 WD Normal

3 0 6 2010 WD Normal

4 0 1 2010 WD Normal

[5 rows x 80 columns]

[6]: train. info()<class 'pandas.core.frame.DataFrame'> RangeIndex: 1460 entries, 0 to 1459 Data columns (total 81 columns):

# Column

--- -----

0 Id

1 MSSubClass

2 MSZoning

3 LotFrontage

4 LotArea

Non-Null Count Dtype

-------------- ----1460 non-null int64 1460 non-null int64 1460 non-null object 1201 non-null float64 1460 non-null int64 5 Street

6 Alley

7 LotShape

8 LandContour 9 Utilities 10 LotConfig 11 LandSlope 12 Neighborhood 13 Condition1 14 Condition2 15 BldgType

16 HouseStyle 17 OverallQual 18 OverallCond 19 YearBuilt 20 YearRemodAdd 21 RoofStyle 22 RoofMatl

23 Exterior1st 24 Exterior2nd 25 MasVnrType 26 MasVnrArea 27 ExterQual 28 ExterCond 29 Foundation 30 BsmtQual

31 BsmtCond

32 BsmtExposure 33 BsmtFinType1 34 BsmtFinSF1 35 BsmtFinType2 36 BsmtFinSF2 37 BsmtUnfSF 38 TotalBsmtSF 39 Heating

40 HeatingQC 41 CentralAir 42 Electrical 43 1stFlrSF

44 2ndFlrSF

45 LowQualFinSF 46 GrLivArea 47 BsmtFullBath 48 BsmtHalfBath 49 FullBath

50 HalfBath

51 BedroomAbvGr 52 KitchenAbvGr 53 KitchenQual 54 TotRmsAbvGrd 55 Functional 1460 non-null object 91 non-null object 1460 non-null object 1460 non-null object 1460 non-null object 1460 non-null object 1460 non-null object 1460 non-null object 1460 non-null object 1460 non-null object 1460 non-null object 1460 non-null object

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.