This is the continuation of my first post published here. Similar to the other article, it will be simple and easy to follow tutorial.
Housing dataset in which you have to predict the price of the house from the given parameters
import os os.listdir() ['.ipynb_checkpoints', 'housingData-Real.csv', 'Untitled.ipynb']
Importing libraries
import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sbn from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import r2_score,mean_squared_error
Reading the dataset
data = pd.read_csv("https://raw.githubusercontent.com/Afsaan/Data-Science/master/Linear%20Regression/housing/housingData-Real.csv")
df=pd.DataFrame(data)
Checking the info of the dataset
df.info() RangeIndex: 21613 entries, 0 to 21612 Data columns (total 21 columns): id 21613 non-null int64 date 21613 non-null object price 21613 non-null float64 bedrooms 21613 non-null int64 bathrooms 21613 non-null float64 sqft_living 21613 non-null int64 sqft_lot 21613 non-null int64 floors 21613 non-null float64 waterfront 21613 non-null int64 view 21613 non-null int64 condition 21613 non-null int64 grade 21613 non-null int64 sqft_above 21613 non-null int64 sqft_basement 21613 non-null int64 yr_built 21613 non-null int64 yr_renovated 21613 non-null int64 zipcode 21613 non-null int64 lat 21613 non-null float64 long 21613 non-null float64 sqft_living15 21613 non-null int64 sqft_lot15 21613 non-null int64 dtypes: float64(5), int64(15), object(1) memory usage: 3.5+ MB
First 5 rows of the dataset
df.head() 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 750
Dropping the unwanted column
df.drop(['id','date'],axis=1,inplace=True)
Univariate Linear Regression
Finding the best fit model with only one dependent variable and try to predict the model (ie. Univariate Linear Regression)
Selecting the column sqft_living
X = df.sqft_living Y =df.price
Converting into 2d array
X=np.array(X).reshape(-1,1) Y=np.array(Y).reshape(-1,1)
Splitting into training and testing dataset
X_train,X_test,Y_train,Y_test = train_test_split(X,Y,random_state=101) model1 = LinearRegression() model1.fit(X_train,Y_train)
Predicting the value of y
Y_pred = model1.predict(X_test)
Evaluation metric to check how close the predicted value is
a=r2_score(Y_test,Y_pred) a 0.5185480212648037
Multiple Regression
Splitting into training and testing dataset
x=df.drop(['price'],axis=1) y=df.price x_train,x_test,y_train,y_test = train_test_split(x,y,random_state=101) models=LinearRegression() model = models.fit(x_train,y_train) y_predict = models.predict(x_test)
b=r2_score(y_test,y_predict) b 0.7097583909083975
print("r2 score of the Univariate linear Regression is : {}".format(a))
print("r2 score of the Multiple linear Regression is : {}".format(b))
r2 score of the Univariate linear Regression is : 0.5185480212648037
r2 score of the Multiple linear Regression is : 0.7097583909083975
As you can see that the r2 score of the multiple regression is more as compared to the univariate linear regression.
If you want to see more example like this then visit my GitHub.