Data Science: Retail Price Optimization

Retail price optimization in data science refers to the process of using data and analytical techniques to determine the most effective pricing strategy for retail products or services. The goal of retail price optimization is to set prices that maximize profits, taking into account various factors such as customer behavior, market dynamics, competition, and cost structures.

About the data set, involves different columns which are:

1. product_id: A unique identifier for each product in the dataset.

2. product_category_name: The name of the product category to which the product belongs.

3. month_year: The month and year of the retail transaction or data recording.

4. qty: The quantity of the product sold or purchased in a given transaction.

5. total_price: The total price of the product, including any applicable taxes or discounts.

6. freight_price: The cost of shipping or freight associated with the product.

7. unit_price: The price of a single unit of the product.

8. product_name_length: The length of the product name in terms of the number of characters.

9. product_description_length: The length of the product description in terms of the number of characters.

10. product_photos_qty: The number of photos available for the product in the dataset.

11. product_weight_g: The weight of the product in grams.

12. product_score: A score or rating associated with the product’s quality, popularity, or other relevant factors.

13. customers: The number of customers who purchased the product in a given transaction.

14. weekday: The day of the week on which the transaction occurred.

15. weekend: A binary flag indicating whether the transaction occurred on a weekend (1) or not (0).

16. holiday: A binary flag indicating whether the transaction occurred on a holiday (1) or not (0).

17. month: The month in which the transaction occurred.

18. year: The year in which the transaction occurred.

19. s: the effect of seasonality

20. comp_1, comp_2, comp_3: Competitor information or variables related to competitors’ prices, promotions, or other relevant factors.

21. ps1, ps2, ps3: Product score or rating associated with competitors’ products.

22. fp1, fp2, fp3: Freight or shipping cost associated with competitors’ products.

Lets code step by step to explain it further:

First we need to import the libraries

we need to load the dataset which is there,

For taking top 5 in the dataset you need to get the data.Head().

For taking the bottom five the dataset in the head.tail().

For getting the more information about the data set.

describe will give you the dataset in which you will get the count, mean, standard deviation, mean, maximum.

shape will get you the rows and column of the dataset

Now, the important step of all if we want to get the null values you need isnull().sum()

For plotting the graph we need to import the libraries.

Now, plotting the histogram for the Distribution of the Total Price.

we need tp plot the box plot of the unit price.

for the scatter plot between the quality to the total price.

Now, we need to plot the bar plot between the Average total price by Product Cateogry.

We need to plot the box plot between the Total price by the weekday between the weekday and the total_price.

box plot between the x and y which is for Box plot of the Total price of Holiday.

Importing the other necessary libraries like train_test-split, the algorithm which we have to use is Decision Tree Regressor.

Taking the dataset of the X and y in which later it is dividing it into the X_train, x_test, y_train and y_test.

Finally, we are building the model using DecisionTreeRegressor to that we are fiting the model to the X_train and y_train.

Predicting the model using the X_test.

Thanks for reading,

Moahammed Muqafamiddin.