In [ ]:
plt.figure(figsize=(20,20))
sns.heatmap(data.corr(),annot=True)
plt.show()
#Importing the libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import folium
from folium.plugins import FastMarkerCluster
from sklearn import preprocessing
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.metrics import mean_absolute_error
from sklearn.linear_model import Ridge
# Importing the dataset
data = pd.read_csv('https://raw.githubusercontent.com/rashida048/Datasets/master/home_data.csv')
data.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 | 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 | 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 | 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 | 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 | 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
#droping the unnecessary columns such as id, date, zipcode , lat and long
data.drop(['id','date'],axis=1,inplace=True)
data.head()
| 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 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 221900 | 3 | 1.00 | 1180 | 5650 | 1.0 | 0 | 0 | 3 | 7 | 1180 | 0 | 1955 | 0 | 98178 | 47.5112 | -122.257 | 1340 | 5650 |
| 1 | 538000 | 3 | 2.25 | 2570 | 7242 | 2.0 | 0 | 0 | 3 | 7 | 2170 | 400 | 1951 | 1991 | 98125 | 47.7210 | -122.319 | 1690 | 7639 |
| 2 | 180000 | 2 | 1.00 | 770 | 10000 | 1.0 | 0 | 0 | 3 | 6 | 770 | 0 | 1933 | 0 | 98028 | 47.7379 | -122.233 | 2720 | 8062 |
| 3 | 604000 | 4 | 3.00 | 1960 | 5000 | 1.0 | 0 | 0 | 5 | 7 | 1050 | 910 | 1965 | 0 | 98136 | 47.5208 | -122.393 | 1360 | 5000 |
| 4 | 510000 | 3 | 2.00 | 1680 | 8080 | 1.0 | 0 | 0 | 3 | 8 | 1680 | 0 | 1987 | 0 | 98074 | 47.6168 | -122.045 | 1800 | 7503 |
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 21613 entries, 0 to 21612 Data columns (total 19 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 price 21613 non-null int64 1 bedrooms 21613 non-null int64 2 bathrooms 21613 non-null float64 3 sqft_living 21613 non-null int64 4 sqft_lot 21613 non-null int64 5 floors 21613 non-null float64 6 waterfront 21613 non-null int64 7 view 21613 non-null int64 8 condition 21613 non-null int64 9 grade 21613 non-null int64 10 sqft_above 21613 non-null int64 11 sqft_basement 21613 non-null int64 12 yr_built 21613 non-null int64 13 yr_renovated 21613 non-null int64 14 zipcode 21613 non-null int64 15 lat 21613 non-null float64 16 long 21613 non-null float64 17 sqft_living15 21613 non-null int64 18 sqft_lot15 21613 non-null int64 dtypes: float64(4), int64(15) memory usage: 3.1 MB
data.describe()
| 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 | 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 | 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 | 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 | 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% | 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% | 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% | 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 | 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 |
# checking for null values/missing values
data.isnull().sum()
price 0 bedrooms 0 bathrooms 0 sqft_living 0 sqft_lot 0 floors 0 waterfront 0 view 0 condition 0 grade 0 sqft_above 0 sqft_basement 0 yr_built 0 yr_renovated 0 zipcode 0 lat 0 long 0 sqft_living15 0 sqft_lot15 0 dtype: int64
data.nunique()
price 4032 bedrooms 13 bathrooms 30 sqft_living 1038 sqft_lot 9782 floors 6 waterfront 2 view 5 condition 5 grade 12 sqft_above 946 sqft_basement 306 yr_built 116 yr_renovated 70 zipcode 70 lat 5034 long 752 sqft_living15 777 sqft_lot15 8689 dtype: int64
# changing float to integer
data['bathrooms'] = data['bathrooms'].astype(int)
data['floors'] = data['floors'].astype(int)
# renaming the column yr_built to age and changing the values to age
data.rename(columns={'yr_built':'age'},inplace=True)
data['age'] = 2023 - data['age']
# changing the column yr_renovated to renovated and changing the values to 0 and 1
data.rename(columns={'yr_renovated':'renovated'},inplace=True)
data['renovated'] = data['renovated'].apply(lambda x: 0 if x == 0 else 1)
# using simple feature scaling
data['sqft_living'] = data['sqft_living']/data['sqft_living'].max()
data['sqft_living15'] = data['sqft_living15']/data['sqft_living15'].max()
data['sqft_lot'] = data['sqft_lot']/data['sqft_lot'].max()
data['sqft_above'] = data['sqft_above']/data['sqft_above'].max()
data['sqft_basement'] = data['sqft_basement']/data['sqft_basement'].max()
data['sqft_lot15'] = data['sqft_lot15']/data['sqft_lot15'].max()
data.head()
| price | bedrooms | bathrooms | sqft_living | sqft_lot | floors | waterfront | view | condition | grade | sqft_above | sqft_basement | age | renovated | zipcode | lat | long | sqft_living15 | sqft_lot15 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 221900 | 3 | 1 | 0.087149 | 0.003421 | 1 | 0 | 0 | 3 | 7 | 0.125399 | 0.000000 | 68 | 0 | 98178 | 47.5112 | -122.257 | 0.215781 | 0.006485 |
| 1 | 538000 | 3 | 2 | 0.189808 | 0.004385 | 2 | 0 | 0 | 3 | 7 | 0.230606 | 0.082988 | 72 | 1 | 98125 | 47.7210 | -122.319 | 0.272142 | 0.008768 |
| 2 | 180000 | 2 | 1 | 0.056869 | 0.006056 | 1 | 0 | 0 | 3 | 6 | 0.081828 | 0.000000 | 90 | 0 | 98028 | 47.7379 | -122.233 | 0.438003 | 0.009254 |
| 3 | 604000 | 4 | 3 | 0.144756 | 0.003028 | 1 | 0 | 0 | 5 | 7 | 0.111583 | 0.188797 | 58 | 0 | 98136 | 47.5208 | -122.393 | 0.219002 | 0.005739 |
| 4 | 510000 | 3 | 2 | 0.124077 | 0.004893 | 1 | 0 | 0 | 3 | 8 | 0.178533 | 0.000000 | 36 | 0 | 98074 | 47.6168 | -122.045 | 0.289855 | 0.008612 |
# using correlation statistical method to find the relation between the price and other features
data.corr()['price'].sort_values(ascending=False)
price 1.000000 sqft_living 0.702035 grade 0.667434 sqft_above 0.605567 sqft_living15 0.585379 bathrooms 0.510072 view 0.397293 sqft_basement 0.323816 bedrooms 0.308350 lat 0.307003 waterfront 0.266369 floors 0.237211 renovated 0.126092 sqft_lot 0.089661 sqft_lot15 0.082447 condition 0.036362 long 0.021626 zipcode -0.053203 age -0.054012 Name: price, dtype: float64
plt.figure(figsize=(20,20))
sns.heatmap(data.corr(),annot=True)
plt.show()
data.corr()['price'][:-1].sort_values().plot(kind='bar')
<Axes: >
# visualizing the relation between price and sqft_living, sqft_lot, sqft_above, sqft_basement, sqft_living15, sqft_lot15, age, renovated, bedrooms, bathrooms, floors, waterfront, view, condition, grade
fig, ax = plt.subplots(4,4,figsize=(20,20))
sns.scatterplot( x = data['sqft_living'], y = data['price'],ax=ax[0,0])
sns.scatterplot( x = data['sqft_lot'], y = data['price'],ax=ax[0,1])
sns.scatterplot( x = data['sqft_above'], y = data['price'],ax=ax[0,2])
sns.scatterplot( x = data['sqft_basement'], y = data['price'],ax=ax[0,3])
sns.scatterplot( x = data['sqft_living15'], y = data['price'],ax=ax[1,0])
sns.scatterplot( x = data['sqft_lot15'], y = data['price'],ax=ax[1,1])
sns.lineplot( x = data['age'], y = data['price'],ax=ax[1,2])
sns.boxplot( x = data['renovated'], y = data['price'],ax=ax[1,3])
sns.scatterplot( x = data['bedrooms'], y = data['price'],ax=ax[2,0])
sns.lineplot( x = data['bathrooms'], y = data['price'],ax=ax[2,1])
sns.barplot( x = data['floors'], y = data['price'],ax=ax[2,2])
sns.boxplot( x = data['waterfront'], y = data['price'],ax=ax[2,3])
sns.barplot( x = data['view'], y = data['price'],ax=ax[3,0])
sns.barplot( x = data['condition'], y = data['price'],ax=ax[3,1])
sns.lineplot( x = data['grade'], y = data['price'],ax=ax[3,2])
sns.lineplot( x = data['age'], y = data['renovated'],ax=ax[3,3])
plt.show()
# adding a new column price_range and categorizing the price into 4 categories
data['price_range'] = pd.cut(data['price'],bins=[0,321950,450000,645000,1295648],labels=['Low','Medium','High','Very High'])
map = folium.Map(location=[47.5480, -121.9836],zoom_start=8)
marker_cluster = FastMarkerCluster(data[['lat', 'long']].values.tolist()).add_to(map)
map
data.drop(['price_range'],axis=1,inplace=True)
X_train, X_test, y_train, y_test = train_test_split(data.drop('price',axis=1),data['price'],test_size=0.3,random_state=10)
input = [('scale',StandardScaler()),('polynomial', PolynomialFeatures(degree=2)),('model',LinearRegression())]
pipe = Pipeline(input)
pipe
Pipeline(steps=[('scale', StandardScaler()),
('polynomial', PolynomialFeatures()),
('model', LinearRegression())])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scale', StandardScaler()),
('polynomial', PolynomialFeatures()),
('model', LinearRegression())])StandardScaler()
PolynomialFeatures()
LinearRegression()
#training the model
pipe.fit(X_train,y_train)
pipe.score(X_test,y_test)
0.8271896429378042
#testing the model
pipe_pred = pipe.predict(X_test)
r2_score(y_test,pipe_pred)
0.8271896429378042
Ridgemodel = Ridge(alpha = 0.001)
Ridgemodel
Ridge(alpha=0.001)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
Ridge(alpha=0.001)
# training the model
Ridgemodel.fit(X_train,y_train)
Ridgemodel.score(X_test,y_test)
#testing the model
r_pred = Ridgemodel.predict(X_test)
r2_score(y_test,r_pred)
0.7123220593275169
from sklearn.ensemble import RandomForestRegressor
regressor = RandomForestRegressor(n_estimators=100, random_state=0)
regressor
RandomForestRegressor(random_state=0)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
RandomForestRegressor(random_state=0)
# training the model
regressor.fit(X_train,y_train)
regressor.score(X_test,y_test)
0.878968081057204
#testing the model
yhat = regressor.predict(X_test)
r2_score(y_test,yhat)
0.878968081057204
# displot of the actual price and predicted price for all models
fig, ax = plt.subplots(1,3,figsize=(20,5))
sns.distplot(y_test,ax=ax[0])
sns.distplot(pipe_pred,ax=ax[0])
sns.distplot(y_test,ax=ax[1])
sns.distplot(r_pred,ax=ax[1])
sns.distplot(y_test,ax=ax[2])
sns.distplot(yhat,ax=ax[2])
# legends
ax[0].legend(['Actual Price','Predicted Price'])
ax[1].legend(['Actual Price','Predicted Price'])
ax[2].legend(['Actual Price','Predicted Price'])
#model name as title
ax[0].set_title('Linear Regression')
ax[1].set_title('Ridge Regression')
ax[2].set_title('Random Forest Regression')
plt.show()
C:\Users\DELL\AppData\Local\Temp\ipykernel_16880\114450484.py:3: UserWarning: `distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751 sns.distplot(y_test,ax=ax[0]) C:\Users\DELL\AppData\Local\Temp\ipykernel_16880\114450484.py:4: UserWarning: `distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751 sns.distplot(pipe_pred,ax=ax[0]) C:\Users\DELL\AppData\Local\Temp\ipykernel_16880\114450484.py:5: UserWarning: `distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751 sns.distplot(y_test,ax=ax[1]) C:\Users\DELL\AppData\Local\Temp\ipykernel_16880\114450484.py:6: UserWarning: `distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751 sns.distplot(r_pred,ax=ax[1]) C:\Users\DELL\AppData\Local\Temp\ipykernel_16880\114450484.py:7: UserWarning: `distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751 sns.distplot(y_test,ax=ax[2]) C:\Users\DELL\AppData\Local\Temp\ipykernel_16880\114450484.py:8: UserWarning: `distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751 sns.distplot(yhat,ax=ax[2])
#plot the graph to compare mae, mse, rmse for all models
fig, ax = plt.subplots(1,3,figsize=(20,5))
sns.barplot(x=['Linear Regression','Ridge Regression','Random Forest'],y=[mean_absolute_error(y_test,pipe_pred),mean_absolute_error(y_test,r_pred),mean_absolute_error(y_test,yhat)],ax=ax[0])
sns.barplot(x=['Linear Regression','Ridge Regression','Random Forest'],y=[mean_squared_error(y_test,pipe_pred),mean_squared_error(y_test,r_pred),mean_squared_error(y_test,yhat)],ax=ax[1])
sns.barplot(x=['Linear Regression','Ridge Regression','Random Forest'],y=[np.sqrt(mean_squared_error(y_test,pipe_pred)),np.sqrt(mean_squared_error(y_test,r_pred)),np.sqrt(mean_squared_error(y_test,yhat))],ax=ax[2])
# label for the graph
ax[0].set_ylabel('Mean Absolute Error')
ax[1].set_ylabel('Mean Squared Error')
ax[2].set_ylabel('Root Mean Squared Error')
plt.show()
# plot accuracy of all models in the same graph
fig, ax = plt.subplots(figsize=(7,5))
sns.barplot(x=['Linear Regression','Ridge Regression','Random Forest Regression'],y=[metrics.r2_score(y_test,pipe_pred),metrics.r2_score(y_test,r_pred),metrics.r2_score(y_test,yhat)])
ax.set_title('Accuracy of all models')
plt.show()
#input the values
bedrooms = 3
bathrooms = 2
sqft_living = 2000
sqft_lot = 10000
floors = 2
waterfront = 0
view = 0
condition = 3
grade = 8
sqft_above = 2000
sqft_basement = 0
yr_built = 1990
yr_renovated = 0
zipcode = 98001
lat = 47.5480
long = -121.9836
sqft_living15 = 2000
sqft_lot15 = 10000
#predicting the price using random forest regression
price = regressor.predict([[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]])
print('The price of the house is $',price[0])
The price of the house is $ 1078694.0533333335
C:\Users\DELL\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\sklearn\base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names warnings.warn(
From the analysis, we can see that the Random Forest Regression model performed better than the Ridge Regression model and Polynomial Regression model.
During the EDA process, we found out that the location of the house is a very important factor in determining the price of the house, since houese with similar area and other features can have different prices depending on the location of the house.
The location of the houses has been plotted on the map using the longitude and latitude values which makesrole of location in determining the price of the house more clear.