Upload _2646.py

_2646.py

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+# -*- coding: utf-8 -*-
+""".2646
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1rm4V5QMLQuWMZikisvPv9jIeKgMEAptr
+"""
+
+import pandas as pd
+
+df = pd.read_csv('//content/Advertising And Sales.csv')
+
+print(df.head())
+
+print(df.describe())
+
+print(df.info())
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+sns.set(style="whitegrid")
+
+plt.figure(figsize=(14, 6))
+
+plt.subplot(1, 3, 1)
+sns.scatterplot(x='TV', y='Sales', data=df)
+plt.title('TV Advertising vs Sales')
+
+plt.subplot(1, 3, 2)
+sns.scatterplot(x='Radio', y='Sales', data=df)
+plt.title('Radio Advertising vs Sales')
+
+plt.subplot(1, 3, 3)
+sns.scatterplot(x='Newspaper', y='Sales', data=df)
+plt.title('Newspaper Advertising vs Sales')
+
+plt.tight_layout()
+plt.show()
+
+corr_matrix = df.corr()
+
+plt.figure(figsize=(8, 6))
+sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f')
+plt.title('Correlation Matrix')
+plt.show()
+
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error, r2_score
+
+X = df[['TV', 'Radio', 'Newspaper']]
+y = df['Sales']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+y_pred = model.predict(X_test)
+
+print('Mean Squared Error:' , mean_squared_error(y_test, y_pred))
+print('R^2 Score:', r2_score(y_test, y_pred))
+
+coefficients = pd.DataFrame(model.coef_, X.columns, columns=['Coefficient'])
+print(coefficients)
+
+def calculate_roi(spend, sales):
+  return sales / spend if spend != 0 else 0
+
+df['TV_ROI'] = df.apply(lambda row: calculate_roi(row['TV'], row['Sales']), axis=1)
+df['Radio_ROI'] = df.apply(lambda row: calculate_roi(row['Radio'], row['Sales']), axis=1)
+df['Newspaper_ROI'] = df.apply(lambda row: calculate_roi(row['Newspaper'], row['Sales']), axis=1)
+
+print(df[['TV_ROI', 'Radio_ROI', 'Newspaper_ROI']].head())