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