Comprehensive Data Analysis with GPT-4 Data Analysis Tool
OpenAI released a new tool called ‘Data Analysis’ on November 6, 2023. I uploaded the winequality-white.csv from the UCI public dataset to the Data Analysis tool. The tool displays a list of all the columns in the dataset as its first response. Also, you can view the Python code for each part of the analysis described by the tool by clicking on the [>-] icon.
The dataset has been successfully loaded. It contains various chemical properties of white wines, such as acidity, sugar, sulfur dioxide levels, and others, along with a quality rating. Here are the columns in the dataset:
Fixed Acidity
Volatile Acidity
Citric Acid
Residual Sugar
Chlorides
Free Sulfur Dioxide
Total Sulfur Dioxide
Density
pH
Sulphates
Alcohol
Quality (the target variable, representing the wine quality rating)
Next Steps in Analysis:
Descriptive Statistics: To understand the basic statistical details like mean, median, standard deviation, etc.
Data Visualization: Creating visual representations of the data to identify patterns, trends, and outliers.
Correlation Analysis: To check the relationships between different variables.
Statistical Testing: If you have specific hypotheses, we can conduct statistical tests.
Shall we proceed with these steps, or is there anything specific you would like to focus on?
It takes you through these steps, both generating the results and Python code used to generate this output.
Among different visualization methods, it produces correlation matrix:
I asked it to produce a regression analysis. Here is the output:
Data Analysis
To perform a regression analysis, we’ll consider the quality of the wine as the dependent variable and other features as independent variables. Given the nature of the dataset, a multiple linear regression would be suitable to understand how these various factors collectively influence wine quality.
Here are the steps for the regression analysis:
Prepare the Data: Split the data into independent variables (features) and the dependent variable (quality).
Split the Data: Divide the dataset into training and test sets to evaluate the model’s performance.
Build the Model: Use a linear regression model.
Train the Model: Fit the model on the training data.
Evaluate the Model: Assess the model’s performance using the test data.
Let’s begin by preparing the data for regression analysis.
The results of the regression analysis are as follows:
Mean Squared Error (MSE): 0.666
R-squared (R²): 0.314
Interpretation:
MSE: This is the average squared difference between the estimated values and the actual value. A lower MSE indicates a better fit. In this case, an MSE of 0.666 suggests some level of error in the predictions.
R²: This metric provides an indication of the goodness of fit of a set of predictions to the actual values. In this context, an R² value of 0.314 means that about 31.4% of the variance in wine quality is explained by the model.
Conclusion:
The R² value is not very high, suggesting that the model, while having some predictive power, does not fully capture the complexity or all the factors affecting wine quality. This outcome is not uncommon in real-world data, especially when dealing with factors influencing something as subjective and complex as wine quality.
Would you like to explore any other aspects of this dataset, perform additional analyses, or perhaps explore the coefficients of the model to understand the influence of each variable?
Here is a Python code it used:
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
# Prepare the data for regression analysis
X = wine_data.drop('quality', axis=1) # Independent variables
y = wine_data['quality'] # Dependent variable (Quality)
# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Initialize the Linear Regression model
model = LinearRegression()
# Train the model
model.fit(X_train, y_train)
# Predict on the test data
y_pred = model.predict(X_test)
# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
mse, r2