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S - Retail Price Optimization using Python
Retail Price Optimization using Python Retail Price Optimization: Case Study April 15, 2023 Download the dataset below to solve this Data Science case study on retail price optimization. (Data Source: Kaggle ) Download Data The retail industry is extremely competitive, and pricing is a critical factor in attracting customers and boosting profits. Pricing decisions are multifaceted and are affected by numerous factors, including cost of goods sold (COGS), market demand, competition, and targeted profit margins. Therefore, companies need to fine-tune their pricing strategies to achieve the right balance that maximizes sales and ensures profitability. In this case study, we’ll use a dataset on Retail Price Optimization submitted by Suddharshan S on Kaggle. The dataset includes the following features: product_id: A unique identifier for each product. product_category_name: The product category name. month_year: The month and year of the retail transaction or data recording. ...
S - Food Delivery Time Prediction using Python
Food Delivery Time Prediction using Python Resources : Food Delivery Time Prediction: Case Study Food Delivery Time Prediction Google Colab https://colab.research.google.com/drive/1nIHs-fhegn7uX-MUJOjCD2PBnyA5LBCk?authuser=1#scrollTo=p1m7D89YfoZz
S - Packaging Machine Learning Models with Python
Packaging Machine Learning Models with Python Model Packaging Packaging a model is a critical part of the machine learning deployment process. It involves preparing the trained model in a format that is easy to deploy and integrate into production systems. If you’re interested in learning how to package machine learning models, this article is for you. Here, I’ll guide you through packaging machine learning models with Python. Packaging Machine Learning Models with Python Packaging machine learning models requires saving the trained model and wrapping it with an API. The general steps include: Export/Save the Trained Model Create a Wrapper Function to Load the Model Set Up an API to Serve the Model Let’s explore these steps one by one. Step 1: Export/Save the Trained Model After training your model, the first step is to save it in a format that can be easily reloaded during deployment. The method for saving models varies between libraries. Below is an example of training a model ...
S - Build An End-to-End Machine Learning Model( Real Estate Prediction: Case Study)
Real Estate Prediction: Case Study Download the dataset below to solve this Data Science case study on real estate prediction. Download Data Real Estate Price Prediction Accurate real estate price prediction is vital for the real estate industry and the economy. It helps investors, developers, and homeowners make informed decisions, plan investments, and manage risks effectively. The dataset provided for this case study includes 414 entries detailing real estate transactions. It features key variables that influence property prices, such as: Transaction date: The date when the property was transacted. House age: The age of the property in years. Distance to the nearest MRT station: The distance in meters to the nearest Mass Rapid Transit station, highlighting convenience and accessibility. Number of convenience stores: The number of convenience stores nearby, indicating the availability of essential amenities. Latitude and Longitude: The geographical coordinates of the property, d...