House-Price-Prediction-Using-ML

Step 1: Import Libraries

import sys
!{sys.executable} -m pip install --upgrade pip --user
!{sys.executable} -m pip install xlrd
!{sys.executable} -m pip install statsmodels
!{sys.executable} -m pip install requests
!{sys.executable} -m pip install seaborn
!{sys.executable} -m pip install scikit-learn

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import shutil
import pandas as pd
import numpy as np
import scipy as sp
import seaborn as sns
import matplotlib.pyplot as plt
import os
import requests
import sklearn as sc
from zipfile import ZipFile
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import statsmodels.api as sm

Downloading House Data

r = requests.get('https://www.dropbox.com/s/1fcws6aaodry54n/partii.zip?dl=1', allow_redirects=True)
open('partii.zip', 'wb').write(r.content);
with ZipFile('partii.zip', 'r') as zipObj:
   zipObj.extractall('DATA')
shutil.rmtree('DATA/__MACOSX')

df = pd.read_csv('DATA/house_dataset.csv').iloc[:,2:]

Step 2: Exploratory Data Analysis (EDA)

df.head()

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	...	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition	SalePrice
0	1461	20	RH	80.0	11622	Pave	NaN	Reg	Lvl	AllPub	...	NaN	MnPrv	NaN	0	6	2010	WD	Normal	NaN
1	1462	20	RL	81.0	14267	Pave	NaN	IR1	Lvl	AllPub	...	NaN	NaN	Gar2	12500	6	2010	WD	Normal	NaN
2	1463	60	RL	74.0	13830	Pave	NaN	IR1	Lvl	AllPub	...	NaN	MnPrv	NaN	0	3	2010	WD	Normal	NaN
3	1464	60	RL	78.0	9978	Pave	NaN	IR1	Lvl	AllPub	...	NaN	NaN	NaN	0	6	2010	WD	Normal	NaN
4	1465	120	RL	43.0	5005	Pave	NaN	IR1	HLS	AllPub	...	NaN	NaN	NaN	0	1	2010	WD	Normal	NaN

5 rows × 81 columns

df['SalePrice'].describe()

count      1460.000000
mean     180921.195890
std       79442.502883
min       34900.000000
25%      129975.000000
50%      163000.000000
75%      214000.000000
max      755000.000000
Name: SalePrice, dtype: float64

numeric_features = df.select_dtypes(include=[np.number])
catagorical_features = df.select_dtypes(include="object")
print("No of Numerical Features ",len(numeric_features.columns))
print("No of catagorica Features ",len(catagorical_features.columns))

No of Numerical Features  38
No of catagorica Features  43

sns.distplot(df['SalePrice']);

  warnings.warn(msg, FutureWarning)

Let’s have a more general view on the top 10 correlated features with the sale price:

k = 10 #number of variables for heatmap
corrmat = df.corr()
cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index
f, ax = plt.subplots(figsize=(14, 10))
sns.heatmap(df[cols].corr(), vmax=.8, square=True);

cols = ['SalePrice', 'OverallQual', 'GrLivArea', 'GarageCars', 'TotalBsmtSF', 'FullBath','GarageArea','1stFlrSF']
sns.pairplot(df[cols])
plt.show()

Do we have missing data?

total = df.isnull().sum().sort_values(ascending=False)
percent = (df.isnull().sum()/df.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
missing_data.head(20)

	Total	Percent
PoolQC	2909	0.996574
MiscFeature	2814	0.964029
Alley	2721	0.932169
Fence	2348	0.804385
SalePrice	1459	0.499829
FireplaceQu	1420	0.486468
LotFrontage	486	0.166495
GarageQual	159	0.054471
GarageYrBlt	159	0.054471
GarageFinish	159	0.054471
GarageCond	159	0.054471
GarageType	157	0.053786
BsmtExposure	82	0.028092
BsmtCond	82	0.028092
BsmtQual	81	0.027749
BsmtFinType2	80	0.027407
BsmtFinType1	79	0.027064
MasVnrType	24	0.008222
MasVnrArea	23	0.007879
MSZoning	4	0.001370

clean_data=df[cols]
clean_data.quantile([0.0, 0.01, 0.05, 0.10, 0.25, 0.50, 0.75, 0.90, 0.95, 0.99, 1.0])

	SalePrice	OverallQual	GrLivArea	GarageCars	TotalBsmtSF	FullBath	GarageArea	1stFlrSF
0.00	34900.00	1.0	334.00	0.0	0.00	0.0	0.00	334.00
0.01	61815.97	3.0	675.42	0.0	0.00	1.0	0.00	520.00
0.05	88000.00	4.0	861.00	0.0	455.25	1.0	0.00	665.90
0.10	106475.00	5.0	923.80	1.0	600.00	1.0	240.00	744.80
0.25	129975.00	5.0	1126.00	1.0	793.00	1.0	320.00	876.00
0.50	163000.00	6.0	1444.00	2.0	989.50	2.0	480.00	1082.00
0.75	214000.00	7.0	1743.50	2.0	1302.00	2.0	576.00	1387.50
0.90	278000.00	8.0	2153.20	3.0	1614.00	2.0	758.00	1675.00
0.95	326100.00	8.0	2464.20	3.0	1776.15	2.0	856.15	1830.10
0.99	442567.01	10.0	2935.72	3.0	2198.30	3.0	1019.49	2288.02
1.00	755000.00	10.0	5642.00	5.0	6110.00	4.0	1488.00	5095.00

low = .01
high = .99

quant_df = clean_data.quantile([low, high])
quant_df.head()

	SalePrice	OverallQual	GrLivArea	GarageCars	TotalBsmtSF	FullBath	GarageArea	1stFlrSF
0.01	61815.97	3.0	675.42	0.0	0.0	1.0	0.00	520.00
0.99	442567.01	10.0	2935.72	3.0	2198.3	3.0	1019.49	2288.02

clean_data = clean_data.loc[(clean_data["GrLivArea"] < quant_df.loc[high, "GrLivArea"])&
                            (clean_data["TotalBsmtSF"] > quant_df.loc[low, "TotalBsmtSF"]) &
                            (clean_data["TotalBsmtSF"] < quant_df.loc[high, "TotalBsmtSF"]) &
                            (clean_data["GarageArea"] > quant_df.loc[low, "GarageArea"]) &
                            (clean_data["GarageArea"] < quant_df.loc[high, "GarageArea"])&
                            (clean_data["1stFlrSF"] < quant_df.loc[high, "1stFlrSF"])&
                            (clean_data["SalePrice"] > quant_df.loc[low, "SalePrice"])&
                            (clean_data["SalePrice"] < quant_df.loc[high, "SalePrice"])]

X = clean_data.loc[:, ["OverallQual","GrLivArea","GarageCars","TotalBsmtSF","FullBath","GarageArea","1stFlrSF"]]
y = np.log(clean_data["SalePrice"])

plt.figure(figsize=(30,10))
sns.heatmap(clean_data.corr(), linewidth=1, annot=True)
plt.show()

Preparing the data

Feature scaling

We will do a little preprocessing to our data using the following formula (standardization):

$$x'= \frac{x - \mu}{\sigma}$$

where $\mu$ is the population mean and $\sigma$ is the standard deviation.

X = (X - X.mean()) / X.std()
X = np.c_[np.ones(X.shape[0]), X]

x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 50)

Linear Regression

Simple Linear Regression

Simple linear regression uses a traditional slope-intercept form, where $a$ and $b$ are the coefficients that we try to “learn” and produce the most accurate predictions. $X$ represents our input data and $Y$ is our prediction.

$$Y = bX + a$$

Multivariable Regression

A more complex, multi-variable linear equation might look like this, where w represents the coefficients, or weights, our model will try to learn.

$$ Y(x_1,x_2,x_3) = w_1 x_1 + w_2 x_2 + w_3 x_3 + w_0$$

The variables $x_1, x_2, x_3$ represent the attributes, or distinct pieces of information, we have about each observation.

lm = LinearRegression()
lm.fit(x_train, y_train)
print("Coefficients: ", lm.coef_)

result = lm.predict(x_test)

Coefficients:  [ 0.          0.13065804  0.11642741  0.03006242  0.07691202  0.00802758
  0.03020288 -0.00485464]

plt.scatter(y_test, result)
plt.xlabel("Actual values")
plt.ylabel("Predicted values")

Text(0, 0.5, 'Predicted values')

X2 = sm.add_constant(X)
est = sm.OLS(y, X2)
est2 = est.fit()
print(est2.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:              SalePrice   R-squared:                       0.797
Model:                            OLS   Adj. R-squared:                  0.796
Method:                 Least Squares   F-statistic:                     725.0
Date:                Wed, 16 Dec 2020   Prob (F-statistic):               0.00
Time:                        01:25:48   Log-Likelihood:                 590.35
No. Observations:                1298   AIC:                            -1165.
Df Residuals:                    1290   BIC:                            -1123.
Df Model:                           7                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const         12.0472      0.004   2818.002      0.000      12.039      12.056
x1             0.1430      0.006     23.817      0.000       0.131       0.155
x2             0.1062      0.006     17.373      0.000       0.094       0.118
x3             0.0293      0.008      3.504      0.000       0.013       0.046
x4             0.0752      0.010      7.718      0.000       0.056       0.094
x5             0.0077      0.006      1.320      0.187      -0.004       0.019
x6             0.0270      0.008      3.381      0.001       0.011       0.043
x7            -0.0005      0.010     -0.054      0.957      -0.020       0.019
==============================================================================
Omnibus:                      205.633   Durbin-Watson:                   2.029
Prob(Omnibus):                  0.000   Jarque-Bera (JB):              515.814
Skew:                          -0.857   Prob(JB):                    9.82e-113
Kurtosis:                       5.570   Cond. No.                         6.30
==============================================================================

Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
HousePricePrediction.ipynb		HousePricePrediction.ipynb
README.md		README.md
output_10_1.png		output_10_1.png
output_12_0.png		output_12_0.png
output_13_0.png		output_13_0.png
output_20_0.png		output_20_0.png
output_26_1.png		output_26_1.png

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House-Price-Prediction-Using-ML

Step 1: Import Libraries

Downloading House Data

Step 2: Exploratory Data Analysis (EDA)

Do we have missing data?

Preparing the data

Feature scaling

Linear Regression

Simple Linear Regression

Multivariable Regression

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Junaid-Bashir/House-Price-Prediction-Using-ML

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House-Price-Prediction-Using-ML

Step 1: Import Libraries

Downloading House Data

Step 2: Exploratory Data Analysis (EDA)

Do we have missing data?

Preparing the data

Feature scaling

Linear Regression

Simple Linear Regression

Multivariable Regression

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