# Import the Python Libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv("Real estate.csv")
data.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 414 entries, 0 to 413
Data columns (total 8 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 No 414 non-null int64
1 X1 transaction date 414 non-null float64
2 X2 house age 414 non-null float64
3 X3 distance to the nearest MRT station 414 non-null float64
4 X4 number of convenience stores 414 non-null int64
5 X5 latitude 414 non-null float64
6 X6 longitude 414 non-null float64
7 Y house price of unit area 414 non-null float64
dtypes: float64(6), int64(2)
memory usage: 26.0 KB
# Viewing the top half of the data
data.head()
No X1 transaction date X2 house age X3 distance to the nearest MRT station X4 number of convenience stores X5 latitude X6 longitude Y house price of unit area
0 1 2012.917 32.0 84.87882 10 24.98298 121.54024 37.9
1 2 2012.917 19.5 306.59470 9 24.98034 121.53951 42.2
2 3 2013.583 13.3 561.98450 5 24.98746 121.54391 47.3
#3 4 2013.500 13.3 561.98450 5 24.98746 121.54391 54.8
4 5 2012.833 5.0 390.56840 5 24.97937 121.54245 43.1
# Viewing the lower half of the data
data.tail()
No X1 transaction date X2 house age X3 distance to the nearest MRT station X4 number of convenience stores X5 latitude X6 longitude Y house price of unit area
409 410 2013.000 13.7 4082.01500 0 24.94155 121.50381 15.4
410 411 2012.667 5.6 90.45606 9 24.97433 121.54310 50.0
411 412 2013.250 18.8 390.96960 7 24.97923 121.53986 40.6
412 413 2013.000 8.1 104.81010 5 24.96674 121.54067 52.5
413 414 2013.500 6.5 90.45606 9 24.97433 121.54310 63.9
# To know the relationship/correlation between the dependent variables and the independent variables; this was what led to the exclusion of the transaction date column while building the model.
data.corr()
No X1 transaction date X2 house age X3 distance to the nearest MRT station X4 number of convenience stores X5 latitude X6 longitude Y house price of unit area
No 1.000000 -0.048658 -0.032808 -0.013573 -0.012699 -0.010110 -0.011059 -0.028587
X1 transaction date -0.048658 1.000000 0.017549 0.060880 0.009635 0.035058 -0.041082 0.087491
X2 house age -0.032808 0.017549 1.000000 0.025622 0.049593 0.054420 -0.048520 -0.210567
X3 distance to the nearest MRT station -0.013573 0.060880 0.025622 1.000000 -0.602519 -0.591067 -0.806317 -0.673613
X4 number of convenience stores -0.012699 0.009635 0.049593 -0.602519 1.000000 0.444143 0.449099 0.571005
X5 latitude -0.010110 0.035058 0.054420 -0.591067 0.444143 1.000000 0.412924 0.546307
X6 longitude -0.011059 -0.041082 -0.048520 -0.806317 0.449099 0.412924 1.000000 0.523287
Y house price of unit area -0.028587 0.087491 -0.210567 -0.673613 0.571005 0.546307 0.523287 1.000000
X = data.iloc[:, 2:-1].values
y = data.iloc[:, -1].values
print(X)
[[ 32. 84.87882 10. 24.98298 121.54024]
[ 19.5 306.5947 9. 24.98034 121.53951]
[ 13.3 561.9845 5. 24.98746 121.54391]
...
[ 18.8 390.9696 7. 24.97923 121.53986]
[ 8.1 104.8101 5. 24.96674 121.54067]
[ 6.5 90.45606 9. 24.97433 121.5431 ]]
print(y)
[ 37.9 42.2 47.3 54.8 43.1 32.1 40.3 46.7 18.8 22.1 41.4 58.1
39.3 23.8 34.3 50.5 70.1 37.4 42.3 47.7 29.3 51.6 24.6 47.9
38.8 27. 56.2 33.6 47. 57.1 22.1 25. 34.2 49.3 55.1 27.3
22.9 25.3 47.7 46.2 15.9 18.2 34.7 34.1 53.9 38.3 42. 61.5
13.4 13.2 44.2 20.7 27. 38.9 51.7 13.7 41.9 53.5 22.6 42.4
21.3 63.2 27.7 55. 25.3 44.3 50.7 56.8 36.2 42. 59. 40.8
36.3 20. 54.4 29.5 36.8 25.6 29.8 26.5 40.3 36.8 48.1 17.7
43.7 50.8 27. 18.3 48. 25.3 45.4 43.2 21.8 16.1 41. 51.8
59.5 34.6 51. 62.2 38.2 32.9 54.4 45.7 30.5 71. 47.1 26.6
34.1 28.4 51.6 39.4 23.1 7.6 53.3 46.4 12.2 13. 30.6 59.6
31.3 48. 32.5 45.5 57.4 48.6 62.9 55. 60.7 41. 37.5 30.7
37.5 39.5 42.2 20.8 46.8 47.4 43.5 42.5 51.4 28.9 37.5 40.1
28.4 45.5 52.2 43.2 45.1 39.7 48.5 44.7 28.9 40.9 20.7 15.6
18.3 35.6 39.4 37.4 57.8 39.6 11.6 55.5 55.2 30.6 73.6 43.4
37.4 23.5 14.4 58.8 58.1 35.1 45.2 36.5 19.2 42. 36.7 42.6
15.5 55.9 23.6 18.8 21.8 21.5 25.7 22. 44.3 20.5 42.3 37.8
42.7 49.3 29.3 34.6 36.6 48.2 39.1 31.6 25.5 45.9 31.5 46.1
26.6 21.4 44. 34.2 26.2 40.9 52.2 43.5 31.1 58. 20.9 48.1
39.7 40.8 43.8 40.2 78.3 38.5 48.5 42.3 46. 49. 12.8 40.2
46.6 19. 33.4 14.7 17.4 32.4 23.9 39.3 61.9 39. 40.6 29.7
28.8 41.4 33.4 48.2 21.7 40.8 40.6 23.1 22.3 15. 30. 13.8
52.7 25.9 51.8 17.4 26.5 43.9 63.3 28.8 30.7 24.4 53. 31.7
40.6 38.1 23.7 41.1 40.1 23. 117.5 26.5 40.5 29.3 41. 49.7
34. 27.7 44. 31.1 45.4 44.8 25.6 23.5 34.4 55.3 56.3 32.9
51. 44.5 37. 54.4 24.5 42.5 38.1 21.8 34.1 28.5 16.7 46.1
36.9 35.7 23.2 38.4 29.4 55. 50.2 24.7 53. 19.1 24.7 42.2
78. 42.8 41.6 27.3 42. 37.5 49.8 26.9 18.6 37.7 33.1 42.5
31.3 38.1 62.1 36.7 23.6 19.2 12.8 15.6 39.6 38.4 22.8 36.5
35.6 30.9 36.3 50.4 42.9 37. 53.5 46.6 41.2 37.9 30.8 11.2
53.7 47. 42.3 28.6 25.7 31.3 30.1 60.7 45.3 44.9 45.1 24.7
47.1 63.3 40. 48. 33.1 29.5 24.8 20.9 43.1 22.8 42.1 51.7
41.5 52.2 49.5 23.8 30.5 56.8 37.4 69.7 53.3 47.3 29.3 40.3
12.9 46.6 55.3 25.6 27.3 67.7 38.6 31.3 35.3 40.3 24.7 42.5
31.9 32.2 23. 37.3 35.5 27.7 28.5 39.7 41.2 37.2 40.5 22.3
28.1 15.4 50. 40.6 52.5 63.9]
# Dividing the data into the train and test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)
print(X_train[:, 0])
[18.2 13.6 3.8 4. 41.3 3.4 35.4 6.2 33.2 10.8 20.9 13.2 14.1 13.2
10.4 23. 28.6 13.8 12.8 13.2 35.3 27.5 17.7 16.4 12.5 37.3 9.1 3.6
17.2 3.7 1.1 13.3 13.3 13. 3.5 6.5 33.5 25.6 30. 31.7 4.1 3.8
8.9 4. 9.9 0. 13.2 4.3 0. 10. 33.3 31.3 9.9 17.9 17. 0.
34.4 32. 0. 16.1 32.4 4.1 6.6 3.1 35.8 34.5 8.5 28.2 16.4 11.5
6.2 2.1 13.1 36.1 3.5 4.1 31. 3.6 30.6 1.9 19.2 33.5 12.5 10.3
3.2 26.8 17.5 15.4 34.8 8.1 30.2 13.3 18.9 16.2 16.9 1.5 19.8 11.6
15. 2.7 12.2 18.8 12.4 31.4 1.1 8.4 18. 42.7 32.6 15.1 31.7 1.1
6.4 41.4 5.7 7.8 19.2 29.6 14.4 31.7 16.4 37.9 30.1 24.2 34.6 34.7
12.6 16.5 9. 21.2 13.9 15.6 2.6 16.1 41.3 21.7 39.2 1. 37.7 7.8
18. 8. 18.5 16.4 17.1 36.6 22.2 27.6 3.9 35.9 14.8 33. 29.3 29.1
10.5 19. 18.2 13.6 39.6 14.1 12.8 33.4 30.9 17.6 21.7 10.3 1.5 16.2
28. 33.6 15.9 16.2 38.3 11.6 28.4 30.6 0. 25.3 0. 24. 5.6 16.1
13.9 15.2 34. 13.5 33.6 0. 37.2 29.6 30.3 11. 40.9 9.7 16.2 16.4
38.3 12. 1.7 34.8 17.6 6.4 5.2 11. 3.5 38. 7.1 14.7 33.9 4.5
14.2 12.3 17.4 20.5 3.9 5.2 34.9 17.3 13.3 32.1 15.9 30.4 17.7 16.9
17. 4.9 14.7 16.5 35.3 6.8 32.8 12.7 16.9 18.1 13.6 17.4 17.8 16.6
11.4 0. 25.3 20.2 13.8 2.6 38.6 15.2 15.7 16.3 32.7 6.5 2. 16.5
29.3 22.8 17.5 35.7 34.9 4. 5.1 16.4 3.1 35.9 34.4 2.3 13.3 8.
29.4 12.7 25.9 13.6 20.6 0. 37.8 32.6 34.8 13.3 19.5 8.3 14.7 15.6
34.6 20.3 5.1 1.8 18.4 13.7 19.2 30.4 21.7 30.7 5.9 0. 1.1 19.1
13.1 4.7 32.8 13. 35.5 38.5 11.9 27.3 18. 15.6 16.9 31.5 32.5 37.1
12.9 12. ]
# Building the model
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
LinearRegression()
y_pred = regressor.predict(X_test)
print(f"Price = {round(regressor.intercept_, 2)} + {round(regressor.coef_[0], 2)}house age + {round(regressor.coef_[1], 4)}mri station + {round(regressor.coef_[2], 2)}convenience store + {round(regressor.coef_[3], 2)}latitude + {round(regressor.coef_[4], 2)}longitude" )
# The Multiple Linear Regression Model
Price = -433.79 + -0.24house age + -0.0047mri station + 1.09convenience store + 224.0latitude + -42.1longitude
# Code for visualizing the data - mostly the relationships between the independent variables and the dependent variable.
plt.suptitle("Model Showing the factors that affect Real Estate Price")
plt.subplot(3,2,1)
plt.scatter(X_train[:, 0], y_train, color="r", s=5, alpha=0.5)
plt.xlabel("House Age")
plt.ylabel("Price")
plt.subplot(3,2,2)
plt.scatter(X_train[:, 1], y_train, color="b", s=5, alpha=0.5)
plt.xlabel("Distance to MRI Station")
plt.ylabel("Price")
plt.subplot(3,2,3)
plt.scatter(X_train[:, 2], y_train, color="g", s=5, alpha=0.5)
plt.xlabel("Number of Convenience stores")
plt.ylabel("Price")
plt.subplot(3,2,4)
plt.scatter(X_train[:, 3], y_train, color="orange", s=5, alpha=0.5)
plt.xlabel("latitude")
plt.ylabel("Price")
plt.subplot(3,2,5)
plt.scatter(X_train[:, 4], y_train, color="purple", s=5, alpha=0.5)
plt.xlabel("longitude")
plt.ylabel("Price")
plt.subplots_adjust(left=0.1,
bottom=0.1,
right=1.2,
wspace=0.4,
hspace=0.6)
plt.show()
