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aravindh ram

Housing pricing prediction using linear regression
by aravindh ram

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# The data contains the following columns:
# * 'Avg. Area Income': Avg. Income of residents of the city house is located in.
# * 'Avg. Area House Age': Avg Age of Houses in same city
# * 'Avg. Area Number of Rooms': Avg Number of Rooms for Houses in same city
# * 'Avg. Area Number of Bedrooms': Avg Number of Bedrooms for Houses in same city
# * 'Area Population': Population of city house is located in
# * 'Price': Price that the house sold at
# * 'Address': Address for the house

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
get_ipython().run_line_magic('matplotlib', 'inline')

USAhousing = pd.read_csv('USA_Housing.csv')







X = USAhousing['Avg. Area Number of Bedrooms'].values.reshape(-1,1)
pd.options.display.float_format = '{:,.2f}'.format
y = USAhousing['Price'].values

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=101)

from sklearn.linear_model import LinearRegression

lm = LinearRegression(),y_train)


plt.scatter(y_train_pred, y_train_pred - y_train, c='blue', marker='o', label='Training data')
plt.scatter(y_test_pred, y_test_pred - y_test, c='orange', marker='*', label='Test data')
plt.xlabel('Predicted values')
plt.legend(loc='upper left')
plt.xlim([1.593866e+04, 6.593866e+04])
plt.ylim([-5, 35])


coeff_df = pd.DataFrame(lm.coef_,X.columns,columns=['Coefficient'])



from sklearn import metrics

print('MAE:', metrics.mean_absolute_error(y_test, predictions))
print('MSE:', metrics.mean_squared_error(y_test, predictions))
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))

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