Data Insights for Restaurant Growth

Introduction

Expanding a restaurant franchise to new cities is an exciting opportunity for growth, but it requires careful planning and analysis. In this project, we’ll explore how data analysis and machine learning techniques can help us identify cities with the potential for higher profits. By leveraging a dataset containing information on city populations and restaurant profits, we’ll develop a predictive model to guide our decision-making process.

Problem Statement

Imagine you’re the CEO of a restaurant franchise, and you’re considering expanding to new cities. Your goal is to identify cities that offer the best potential for profit. You have access to data on profits and populations from existing restaurant locations, as well as information on candidate cities for expansion. How can you use this data to make informed decisions about where to open new outlets?

Dataset Overview

The dataset we’ll be using contains two main variables: the population of cities (`x_train`) and the profit of restaurants in those cities (`y_train`). Both `x_train` and `y_train` are provided as NumPy arrays, making it easy for us to perform data analysis and model training.

Approach

1. Data Loading and Exploration: We’ll start by loading the dataset and exploring its characteristics. This involves examining the distribution of population and profit values, as well as identifying any potential outliers or anomalies in the data.
2. Model Development: Next, we’ll develop a linear regression model to predict restaurant profits based on city populations. This model will serve as our predictive tool for evaluating the profitability of potential new locations.
3. Model Evaluation: We’ll evaluate the performance of our linear regression model using various metrics such as mean squared error and R-squared value. This assessment will help us gauge the accuracy and reliability of our predictions.
4. Business Insights: Finally, we’ll derive actionable insights from our model results to inform decision-making regarding city expansion. By identifying cities with the highest predicted profits, we can prioritize these locations for future investment and growth opportunities.

You can see the entire implementation here:

GitHub - aanchalparegi/ML-Exercises

Conclusion

By embracing innovation and leveraging the power of data analytics, restaurant franchises can unlock their full potential and thrive in an ever-changing business landscape. With the right data-driven approach, unlocking new business opportunities becomes a tangible and achievable goal.

Photo by Yiorgos Ntrahas on Unsplash