import matplotlib.pyplot as plt
import seaborn as sns
df_numerics = house_data.select_dtypes(include = ['float64', 'int64'])
df_objects = house_data.select_dtypes(include=['object'])
# Densitat per columna
figure=plt.figure(figsize = (30, 40))
columns = 3
rows = df_numerics.shape[1] // columns + 1
for i, column in enumerate(df_numerics.columns, 1):
axes = figure.add_subplot(rows,columns,i)
sns.kdeplot(x = df_numerics[column], hue = house_data['SalePriceQ'], fill = True, ax = axes)
figure.tight_layout()
plt.show()
# Histograma
figure=plt.figure(figsize = (30, 40))
columns = 4
rows = df_objects.shape[1] // columns + 1
for i, column in enumerate(df_objects.columns, 1):
axes = figure.add_subplot(rows,columns,i)
sns.histplot(x = df_objects[column], ax = axes, hue=house_data["SalePriceQ"], multiple='dodge')
axes.tick_params(axis='x', rotation=45)
for label in axes.get_xticklabels():
label.set_ha('right') # Align labels to the right
figure.tight_layout()
plt.show()
# Scatter-plot entre parell de columnes
columns = ['OverallQual', 'OverallCond', 'GarageArea', 'GrLivArea', 'YearBuilt']
_ = sns.pairplot(data=house_data, vars=columns,
hue="SalePriceQ", plot_kws={'alpha': 0.2},
height=3, diag_kind='kde')
plt.show()
# Single scatter-plot
ax = sns.scatterplot(
x="GrLivArea", y="YearBuilt", data=house_data,
hue="SalePriceQ", alpha=0.5,
)
plt.show()
# Correlation matrix
columns = ['OverallQual', 'OverallCond', 'GarageArea', 'GrLivArea', 'YearBuilt']
corr_df = house_data[columns].corr(method='pearson', numeric_only=True)
plt.figure(figsize=(8, 6))
sns.heatmap(corr_df, annot=True)
plt.show()
