Product Demand Prediction Using Data Science
The value of a product in the market changes with respect to it’s demand, so let’s assume a situation that a firm’s product demand is changing and what is supposed to be it’s price in the market, and the firm wants to set prices in a way that can even best the competitors in the market.
The process will be carried out using certain python libraries and an extracted dataset from GitHub. The libraries used here are pandas, numpy, plotly, seaborn, matplotlib, scikit-learn. The libraries help in reading the data, visualizing the data and training it to create machine learning models for predictive analytics.
Going on to further processes, we can start by importing the necessary libraries and the required dataset from GitHub.
The dataset contains columns like ID, Store ID, Total Price, Base Price and the units sold, so we do have a basic idea of the problem that has been framed in front of us and what are the necessary methods we can use to retrieve necessary information from the data which can be used for prediction modeling.
The most important question that stands here is whether there is any null value present or not, so to identify that we use the following code to retrieve the necessary information. Similarly if there is a missing value then in that case it is better to remove the row.
Once the data has been cleaned and improved, now the next step would be to use scatterplot to analyze the demand for the product with the varying change in price.
We can see the scatterplot and can understand that that the sales of the product is increasing and the price is decreasing on the other hand with some exceptions. The second better way to analyze it further is to understand the correlation between the features of the datasets.
Once the correlation has been established, we can train the dataset for presenting the predicted machine learning model. The columns used here are Base Price and the Total price.
The data will now be trained into training and test sets using the decision tree algorithm, after which the data can be inserted to check the demand prediction.
So that’s how you can use Python to train a machine learning model for product demand prediction. One of the most important variables influencing product demand is price. Even if the price of a product rises, just a few individuals will buy it if it is not a necessity. I hope you enjoyed my post on using Python to estimate product demand using machine learning.
Project GitHub Link — https://github.com/advait27/product-demand-prediction.git