application_vnd.jupyter.stderr_output_1.png image_output_1.png
import pandas as pd

import seaborn as sns

df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')

df = df.drop('url', axis=1)

df.isnull().sum()

df = df.dropna()

corr = df.corr()

furniture_type = df['type'].value_counts()

plt.figure(figsize=(10,15))
ax = sns.barplot(y = furniture_type.index[:10], x = furniture_type.values[:10])
for i in ax.containers:
    ax.bar_label(i,)
plt.title("The most popular furniture types", size=15)
plt.show()

sns.scatterplot(x='price', y='delivery', data=df)
plt.xlabel('Price')
plt.ylabel('Delivery Price')
plt.title('Relationship between Furniture Price and Delivery Price')
plt.show()