'''python

CODSALES

Repository for CODSOFT Data Science Internship tasks import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error, r2_score

--- 1. Load the Dataset ---

try: data = pd.read_csv('Advertising.csv') except FileNotFoundError: print("Error: 'Advertising.csv' not found. Please make sure the file is in the correct directory.") exit()

--- 2. Data Cleaning ---

Check for and remove an unnecessary index column

if 'Unnamed: 0' in data.columns: data = data.drop(columns=['Unnamed: 0'], axis=1) print("Removed unnecessary index column.")

Check for missing values

print("\nMissing values:") print(data.isnull().sum())

Handle missing values if any (e.g., imputation):

data = data.fillna(data.mean())

Check for duplicate rows

print("\nNumber of duplicate rows:", data.duplicated().sum())

Remove duplicate rows if any:

data = data.drop_duplicates()

print("Duplicate rows removed.")

Check data types

print("\nData types:") print(data.dtypes)

--- 3. Exploratory Data Analysis (EDA) ---

print("\n--- Exploratory Data Analysis ---")

Visualize the relationships between advertising spend and sales

sns.pairplot(data, x_vars=['TV', 'Radio', 'Newspaper'], y_vars='Sales', height=5, aspect=0.8) plt.suptitle("Relationship between Advertising Spend and Sales", y=1.02) plt.show() plt.savefig('movie_ratings_distribution.png')

Create a heatmap to visualize the correlation matrix

correlation_matrix = data[['TV', 'Radio', 'Newspaper', 'Sales']].corr() sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm') plt.title("Correlation Matrix") plt.show()

--- 4. Feature Selection ---

print("\n--- Feature Selection ---") X = data[['TV', 'Radio', 'Newspaper']] # Features (independent variables) y = data['Sales'] # Target variable (dependent variable)

--- 5. Split the Data ---

print("\n--- Data Splitting ---") X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) print(f"Shape of X_train: {X_train.shape}") print(f"Shape of X_test: {X_test.shape}") print(f"Shape of y_train: {y_train.shape}") print(f"Shape of y_test: {y_test.shape}")

--- 6. Choose and Train a Model ---

print("\n--- Model Training ---")

Initialize the Linear Regression model

model = LinearRegression()

Train the model on the training data

model.fit(X_train, y_train)

--- 7. Make Predictions ---

print("\n--- Making Predictions ---")

Make predictions on the test data

y_pred = model.predict(X_test)

--- 8. Evaluate the Model ---

print("\n--- Model Evaluation ---")

Calculate evaluation metrics

mse = mean_squared_error(y_test, y_pred) r2 = r2_score(y_test, y_pred)

print(f"Mean Squared Error: {mse:.2f}") print(f"R-squared: {r2:.2f}")

Visualize the predictions against the actual values

plt.scatter(y_test, y_pred) plt.xlabel("Actual Sales") plt.ylabel("Predicted Sales") plt.title("Actual Sales vs. Predicted Sales") plt.show()

--- 9. Interpret the Results ---

print("\n--- Model Interpretation ---")

Get the coefficients of the model

print("Coefficients (TV, Radio, Newspaper):", model.coef_) print("Intercept:", model.intercept_)

The coefficients indicate the change in Sales for a one-unit increase

in the corresponding advertising medium, holding other mediums constant.

The intercept is the predicted Sales value when all advertising spends are zero.

--- 10. Making New Predictions ---

print("\n--- Making New Predictions ---")

Example of predicting sales for a new advertising budget

new_data = pd.DataFrame({'TV': [200], 'Radio': [30], 'Newspaper': [50]}) predicted_sales_new = model.predict(new_data) print(f"Predicted Sales for new budget (TV=200, Radio=30, Newspaper=50): {predicted_sales_new[0]:.2f}")

new_data_multiple = pd.DataFrame({'TV': [100, 150, 250], 'Radio': [20, 40, 10], 'Newspaper': [30, 0, 60]}) predicted_sales_multiple = model.predict(new_data_multiple) print("\nPredicted Sales for multiple new budgets:") print(predicted_sales_multiple)

Result:

Missing values: TV 0 Radio 0 Newspaper 0 Sales 0 dtype: int64

Number of duplicate rows: 0

Data types: TV float64 Radio float64 Newspaper float64 Sales float64 dtype: object

--- Exploratory Data Analysis ---

--- Feature Selection ---

--- Data Splitting --- Shape of X_train: (160, 3) Shape of X_test: (40, 3) Shape of y_train: (160,) Shape of y_test: (40,)

--- Model Training ---

--- Making Predictions ---

--- Model Evaluation --- Mean Squared Error: 2.91 R-squared: 0.91

--- Model Interpretation --- Coefficients (TV, Radio, Newspaper): [0.05450927 0.10094536 0.00433665] Intercept: 4.714126402214127

--- Making New Predictions --- Predicted Sales for new budget (TV=200, Radio=30, Newspaper=50): 18.86

Predicted Sales for multiple new budgets: [12.31406014 16.92833152 19.61109654]

'''