def normal_distribution():
residence_table = Residences()
# residences = residence_table.get_residences(5000)
residences = residence_table.get_all_residences()
residences_df = pd.DataFrame(residences)
prices = residences_df['price']
standard_deviation = prices.std()
mu = prices.mean()
variance = prices.var()
sigma = math.sqrt(variance)
x = np.linspace(mu - 3 * sigma, mu + 3 * sigma, 50)
plt.style.use('ggplot')
plt.plot(x, stats.norm.pdf(x, mu, sigma))
plt.title('Distribuția normală a prețurilor')
plt.xlabel('Prețul chiriei lunare')
plt.ylabel('Probabilitatea de densitate')
plt.suptitle('Valoarea medie: ' + str(round(mu, 2)) +
'\nDeviația standard: ' + str(round(standard_deviation, 2)),
x=0.75,
y=0.85,
fontsize=10)
plt.show()
def plot_surface_and_rental_price():
residence_table = Residences()
residences = residence_table.get_all_residences()
residences_df = pd.DataFrame(residences)
residences_df = residences_df[residences_df.livable_area.notnull()]
plt.figure()
plt.style.use('ggplot')
plt.scatter(residences_df['livable_area'],
residences_df['price'],
s=15,
edgecolor="black",
c="darkorange")
plt.xlabel("Suprafața locuibilă")
plt.ylabel("Prețului chiriei lunare")
plt.title("Suprafața locuibilă / Prețului chiriei lunare")
plt.legend()
plt.show()
def plot_rooms_vs_price():
residence_table = Residences()
residences = residence_table.get_all_residences()
residences_df = pd.DataFrame(residences)
residences_df = residences_df[residences_df.rooms.notnull()]
plt.figure()
plt.style.use('ggplot')
plt.scatter(residences_df['rooms'],
residences_df['price'],
s=15,
edgecolor="black",
c="darkorange")
plt.xlabel("Număr camere")
plt.ylabel("Prețului chiriei lunare")
plt.title("Număr camere / Prețului chiriei lunare")
plt.legend()
plt.show()
def plot_surface_vs_price_per_sq_meter():
residence_table = Residences()
residences = residence_table.get_all_residences()
residences_df = pd.DataFrame(residences)
residences_df = residences_df[residences_df.livable_area.notnull()]
residences_df = residences_df[residences_df.livable_area > 0]
livable_area = residences_df['livable_area']
price_per_sq_meter = residences_df['price'] / residences_df['livable_area']
plt.figure()
plt.style.use('ggplot')
plt.scatter(livable_area,
price_per_sq_meter,
s=15,
edgecolor="black",
c="darkorange")
plt.xlabel("Suprafața locuibilă")
plt.ylabel("Prețul pe metrul pătrat")
plt.title("Suprafața locuibilă / Suprafața locuibilă")
plt.legend()
plt.show()
import joblib
import numpy as np
import math
from src.models.residences import Residences
import pandas as pd
from src.visualization.feature_importance import print_feature_importance
residences_nr = 12000
save = False
list_features_to_drop = ['price', 'currency', 'price_interval']
residence_table = Residences()
# residences = residence_table.get_residences(residences_nr)
residences = residence_table.get_all_residences()
print('Number of residences: {}'.format(len(residences)))
residences = pd.DataFrame(residences)
residences = residences.fillna(-999)
residences = residences.sample(frac=1)
# target = np.array(residences['price'])
target = np.array(residences['price_interval'])
features = residences.drop(list_features_to_drop, axis=1)
features_columns = features.columns
feature_list = list(features.columns)
features = np.array(features)
data_train = features[:math.floor(len(features) * 0.9)]
target_train = target[:math.floor(len(target) * 0.9)]
print('Number of training samples: {}'.format(len(data_train)))