6" Glow Sticks 2 Pack

import pandas as pd from sklearn.modelselection import traintestsplit from sklearn.linearmodel import LinearRegression from sklearn.metrics import meansquarederror, r2score import matplotlib.pyplot as plt import seaborn as sns Load the dataset df = pd.readcsv('advertising.csv') Display the first few rows and basic information print(df.head()) print(df.info()) print(df.describe()) Check for missing values print(df.isnull().sum()) Exploratory Data Analysis (EDA) Distribution of 'Sales' plt.figure(figsize=(8, 6)) sns.histplot(df['Sales'], kde=True) plt.title('Distribution of Sales') plt.xlabel('Sales') plt.ylabel('Frequency') plt.show() Relationship between TV, Radio, Newspaper advertising and Sales fig, axes = plt.subplots(1, 3, figsize=(18, 5)) sns.scatterplot(ax=axes[0], x='TV', y='Sales', data=df) axes[0].settitle('TV Advertising vs. Sales') sns.scatterplot(ax=axes[1], x='Radio', y='Sales', data=df) axes[1].settitle('Radio Advertising vs. Sales') sns.scatterplot(ax=axes[2], x='Newspaper', y='Sales', data=df) axes[2].settitle('Newspaper Advertising vs. Sales') plt.tightlayout() plt.show() Correlation Heatmap plt.figure(figsize=(10, 8)) sns.heatmap(df.corr(), annot=True, cmap='coolwarm', fmt=".2f") plt.title