Through this Project, I have developed skills at :

• Data Understanding: Analyze the dataset structure and contents, identifying input (ad budgets) and output variables (sales).
• Data Cleaning: Address missing values, outliers, and inconsistencies in the data, ensuring data integrity.
• Data Exploration: Visualize relationships between ad budgets and sales using scatter plots, correlation matrices, etc., to uncover trends and patterns.
• Feature Engineering: Create additional features like interaction terms between marketing channels or total advertising budget across all channels.
• Model Selection: Choose regression models such as linear regression, multiple regression, ridge regression, or random forest regression for predicting sales.
• Model Training and Evaluation: Split the dataset into training and testing sets, train the selected model, and evaluate its performance using metrics like MSE, R-squared, etc.
  • Fine-tune model hyperparameters as necessary to optimize performance.
• Model Interpretation: Interpret model coefficients to understand the impact of each marketing channel on sales. Analyze model predictions and compare them to actual sales values for accuracy assessment.
• Model Deployment: Deploy the trained model to make predictions on new data. Monitor model performance over time and update it as needed to adapt to changing market conditions.