Linear Regression - Exploratory Data Analysis

df.describe()

# Combination of subplot and histogram 
plt.figure(figsize=(15,11))
for (i, col) in enumerate(sellers.describe().columns):#["wait_time", "delay_to_carrier", "avg_review_score", "n_orders", "quantity", "price"]):
    plt.subplot(3,4,i+1)
    sns.histplot(sellers[col], kde=False, stat='density', discrete=[True,None][col in ['share_of_one_stars','share_of_five_stars','sales']]);

#seaborn PairPlot
sns.pairplot(df)

#plotly Scatter Plot
import plotly.express as px
fig = px.scatter(data_frame = sellers[sellers['review_score'] < 4],
    x="wait_time",
    y="delay_to_carrier",
    size="sales",
    color="review_score",
    size_max = 60,
    opacity = 0.5
)
fig.show()