HouseRent.py

import pandas as pd
from sklearn import preprocessing, linear_model
import numpy as np
import sklearn

### LOAD DATA ###
print('-'*30);print("IMPORTING DATA ");print('-'*30)
data = pd.read_csv('houses_to_rent.csv', sep=',')
data = data[['city','rooms','bathroom','parking spaces','fire insurance',
             'furniture', 'rent amount']]
print(data.head())

### PROCESS DATA ###
data['rent amount'] = data['rent amount'].map(lambda i: int(i[2:].replace(',', '')))
data['fire insurance'] = data['fire insurance'].map(lambda i: int(i[2:].replace(',', '')))
le = preprocessing.LabelEncoder()
data['furniture'] = le.fit_transform((data['furniture']))
print(data.head())

print('-'*30);print("CHECKING NULL DATA ");print('-'*30);
print(data.isnull().sum())
# data.dropna(inplace=True)
print('-'*30);print(" HEAD ");print('-'*30);
print(data.head())

#### SPLT DATA ####
print('-'*30);print(" SPLIT DATA ");print('-'*30);
x = np.array(data.drop(['rent amount'], axis=1))
y = np.array(data['rent amount'])
print('X', x.shape)
print('Y', y.shape)
xTrain, xTest, yTrain, yTest = sklearn.model_selection.train_test_split(x, y,
                                                                        test_size=0.2,
                                                                        random_state=10)
print('XTrain', xTrain.shape)
print('XTest', xTest.shape)

#### TRAINING ####
print('-'*30);print(" TRAINING ");print('-'*30);
model = linear_model.LinearRegression()
model.fit(xTrain, yTrain)
accuracy = model.score(xTest, yTest)
print('Coefficients: ', model.coef_)
print('Intercept: ', model.intercept_)
print('Accuracy: ', round(accuracy*100, 3), '%')

#### EVALUATION ####
print('-'*30);print(" MANUAL TESTING ");print('-'*30);
testVals = model.predict(xTest)
print(testVals.shape)
error = []
for i, testVal in enumerate(testVals):
    error.append(yTest[i]-testVal)
    print(f'Actual:{yTest[i]} Prediction:{int(testVal)} Error:{int(error[i])}')