Use ChatGPT Code Interpreter for Data Science Work: A Step-by-Step Guide

In this blog, we’ll explore how to use the OpenAI’s ChatGPT code interpreter. The ChatGPT code interpreter allows you to run code, interact with data, and even build machine learning models all within a conversational format.

Step 1: Uploading a Dataset

Our journey starts by uploading a dataset. In our case, we’ve used a dataset named “Facebook_Data.xlsx”. Remember, you can upload your data files directly into the chat for analysis.

Step 2: Reading the Dataset

We can use the popular data analysis library pandas to load and examine our dataset.

Step 3: Creating New Metrics

Based on our dataset, we’ve decided to create a new metric called “Cost per Result”. This metric is calculated by dividing the “Amount Spent” by the sum of various result metrics including “Website Registrations Completed”, “Leads”, “Website Leads”, “On-Facebook Leads”, and “New Messaging Connections”.

Step 4: Exploring Feature Importance

To understand the influence of various metrics on our newly created “Cost per Result”, we calculated the correlation of “Cost per Result” with all other numerical columns. We used Pearson’s correlation coefficient for this purpose.

Step 5: Forecasting Future Metrics

To forecast the “Cost per Result” for the next 5 months, we used four different predictive models:

1. ARIMA (AutoRegressive Integrated Moving Average): A classic model for time series forecasting.
2. Holt-Winters Method: An effective approach to handling both trend and seasonality in time series data.
3. Simple Exponential Smoothing (SES): Suitable for forecasting data with no clear trend or seasonality.
4. Linear Regression Model: A basic predictive analytics technique used for predicting a dependent variable based on one or more independent variables.

Step 6: Visualizing the Forecasts

Finally, we visualized the forecasts from each of the four models alongside the actual data. This allows us to see how each model expects the “Cost per Result” to change over the next 5 months.

We hope you found this step-by-step guide on using the ChatGPT code interpreter helpful. Remember, while the models provide forecasts based on the data provided, always use these forecasts as a guide and adjust your expectations as more data becomes available.

Happy data analyzing with ChatGPT!