By conducting a thorough analysis of the data, I gained valuable insights into the retail landscape, customer behavior, and market trends. This initial exploration set the foundation for informed decision-making.
Understanding the financial impact of different pricing strategies is crucial. I meticulously calculated the revenue and profit associated with various pricing scenarios to identify the most profitable options.
Determining the margin is vital for retailers to maintain a healthy bottom line. I carefully calculated the margin for each product, taking into account factors like the cost of goods sold (COGS) and additional expenses.
To evaluate the competitiveness of products in the market, I examined price ratios. By comparing prices across similar products, I identified opportunities to adjust prices strategically and gain a competitive advantage.
Analyzing price differences between products provided valuable insights into pricing gaps and opportunities for optimization. I leveraged this information to develop targeted pricing strategies that resonate with customers.
Understanding market demand is key to setting optimal prices. I examined market demand indicators such as historical sales data and customer preferences to gauge price sensitivity and adjust pricing accordingly.
Incorporating time-related features allowed me to account for seasonality, trends, and other temporal factors. By considering the time dimension, I identified patterns and made data-driven pricing decisions.
By analyzing lagged price data, I evaluated the impact of previous pricing strategies on current sales performance. This analysis provided valuable insights into price dynamics and informed pricing adjustments.
Categorical variables, such as product categories, play a significant role in pricing decisions. I employed advanced techniques to effectively handle categorical variables, ensuring their inclusion in the pricing models.
To ensure fair comparisons and accurate modeling, I appropriately scaled numeric features. This step allowed for a more comprehensive analysis and improved the accuracy of pricing predictions.
Linear Regression and Decision Tree Regression
- I have reduced the file size of the iPython Notebook because I load several images inside to add to this git repository. To view the output, re-run the cells one after the other.
- Requirements: Ensure you have these libraries installed in your system.
pip install seaborn
pip install plotly==5.15.0