ChatGPT Code Interpreter: No-Code Data Analysis with Real-World Examples

OpenAI’s Code Interpreter, now available to all Plus users, a major upgrade I was eager for. It excels in data analysis, especially when dealing with Python code. For tasks from basic Excel number-crunching to high-level analysis, engage with ChatGPT and experience a new era of data analytics.

To illustrate Code Interpreter’s capabilities, I picked a dataset from Kaggle. The tasks varied from simple data analysis and graph creation to intricate machine learning models, showcasing its power.

This case study serves as a guide, suitable for both Excel enthusiasts and professional data analysts, showing how to use Code Interpreter effectively.

The dataset includes information about over 8000 Netflix movies and TV shows, such as directors, actors, ratings, release times, durations, genres, descriptions, and more.

First, I’ll show how to set up Code Interpreter, then walk you through its usage in various data analysis tasks, from beginner to advanced. Finally, I’ll discuss the impact and limitations of Code Interpreter on data analysis.

Let’s dive right into it!

Setting Up Code Interpreter

To use Code Interpreter with ChatGPT, you need a ChatGPT Plus account. After logging in, click the three-dot menu next to your username, at the bottom left corner. This action brings up the settings window.

Inside this window, select “Beta features” on the left side, and toggle on Code Interpreter at the bottom right.

Next, hover over the “GPT-4” button at the top of the main ChatGPT screen and select “Code Interpreter” from the drop-down menu that appears.

Now, let’s move on to data cleaning and pre-processing.

Data Cleaning and Pre-processing

Typically you can’t directly upload files using ChatGPT; it requires a web connection or third-party plugin. However, Code Interpreter allows direct file uploads.

Code Interpreter supports file uploads up to 512M in size and can unzip files before upload, allowing it to manage significant data analysis tasks. You can upload a CSV file containing millions of lines.

Once Code Interpreter is active, a plus icon appears to the left of the chat input box. Clicking this opens your local machine’s file browser.

I uploaded the unzipped package I downloaded from Kaggle, saving an additional unzipping step.

After the upload, I gave tasks straight to ChatGPT:

As a professional data analyst, please follow the standard data analysis procedure. Let’s go step by step.

And it returned the following after an initial data analysis:

Great, let’s start the data analysis process. Here are the steps we usually follow:

1. Data Collection: In this case, you’ve provided a dataset.
2. Data Processing: This includes data cleaning, handling missing values, and other necessary transformations.
3. Exploratory Data Analysis (EDA): This involves understanding the data’s characteristics through statistics and visualization.
4. Modeling (if necessary): Depending on the goals of the analysis, we might build statistical or machine learning models.
5. Interpretation: Finally, we interpret the results and make conclusions or recommendations.

Let’s start with the first step. You’ve provided a file named archive.zip, so let’s extract it and inspect its content to understand the dataset better.

The archive.zip file contained one CSV file named netflix_titles.csv. This seems to be a dataset about Netflix titles.

Let’s proceed with the next step of the data analysis process which is data processing. We will read the CSV file and take a look at the first few rows to understand its structure and the type of data it contains.

The dataset contains the following columns:

1. show_id: Unique identifier for each show.
2. type: The format of the show (Movie or TV Show).
3. title: The title of the show.
4. director: The director of the show.
5. cast: The cast involved in the show.
6. country: The country where the show was produced.
7. date_added: The date the show was added to Netflix.
8. release_year: The year the show was originally released.
9. rating: The rating of the show.
10. duration: The duration of the show.
11. listed_in: The genre(s) of the show.
12. description: A short description of the show.

Before we proceed further with the analysis, it is beneficial to check the data for any missing values or inconsistencies that need to be handled. Let’s check for missing values in our dataset.

Here’s the count of missing values in each column:

• director: 2634 missing values
• cast: 825 missing values
• country: 831 missing values
• date_added: 10 missing values
• rating: 4 missing values
• duration: 3 missing values

The columns director, cast, and country have a significant number of missing values. Depending on our analysis goals, we might fill these with an appropriate value (like ‘Unknown’), or we might decide to ignore these columns if they aren’t relevant to our analysis.

The date_added, rating, and duration columns have only a few missing values. We could consider removing the rows with missing values in these columns, or filling them in if we can determine an appropriate value.

The next step is exploratory data analysis. However, we need to decide how to handle these missing values first. How would you like to proceed?

Hitting the “Show Work” button in ChatGPT’s response reveals the underlying code:

import zipfile
import os

# Path to the zip file
zip_path = "/mnt/data/archive.zip"
# Directory to extract to
extract_dir = "/mnt/data/archive/"

# Create directory to extract to if it doesn't exist
os.makedirs(extract_dir, exist_ok=True)

# Open the zip file
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
    # Extract all the contents of zip file in the directory
    zip_ref.extractall(extract_dir)

# List the contents of the directory after extraction
contents = os.listdir(extract_dir)

contents

Code Interpreter doesn’t just analyze data and show the process, it also generates code. This allows Python coders to grasp the logic behind the data analysis. Even non-coders can benefit from Code Interpreter as it annotates each code line for better understanding.

The above code and comments show that ChatGPT uses Python’s os and zipfile libraries to unzip files. This isn’t a manual instruction; it happens automatically. It then reads and understands each column name. One of ChatGPT’s benefits is its ability to comprehend data context and provide accurate interpretations.

Then, it counts missing data in each column and suggests remedies based on the quantity of missing values and the analysis goal. Data cleaning is a key step in data analysis as empty data can trigger errors, and incorrect data can distort the results.

ChatGPT’s data cleaning suggestions might be inconsistent due to inherent randomness. You can directly ask for cleaning advice or skip this part. If any problems crop up, it will clean the data itself. However, to reduce our carbon footprint, let’s stick to its advice and clean the data. So, I gave it this prompt:

Fill the missing values of director, cast, and country with ‘Unknown’ and remove the rows with missing values of date_added, rating, and duration.

ChatGPT responded as follows:

All missing values have been successfully handled. The dataset no longer contains any missing values.

Now we can move on to the next step: Exploratory Data Analysis (EDA). In this step, we’ll dive deeper into the dataset to understand the distribution of data, relationships between different variables, and identify any interesting patterns or insights.

What specific aspects would you like to explore in this dataset? For example, we could look at the distribution of movies vs. TV shows, analyze the content added over the years, or see which countries produce the most content, among other things.

Exploratory Data Analysis (EDA)

Think of data as a puzzle; EDA is your first step. It reveals the puzzle’s general shape, identifies important pieces, and begins making connections.

EDA is vital in data analysis because it familiarizes us with the dataset and highlights key features for further examination.

As Code Interpreter has recommended EDA next, let’s proceed:

As a professional data analyst, please make the decision yourself.

Here’s its response:

Alright, let’s start by exploring some general aspects of the data. Here are a few steps we can take:

1. Distribution of Type (Movie vs TV Show): This can give us an idea about the kind of content that is most prevalent on Netflix.
2. Content Added Over the Years: This analysis can show us how the addition of content on Netflix has trended over time.
3. Content Production by Country: This can show us which countries are the major producers of content that ends up on Netflix.
4. Rating Distribution: This can give us an idea about the kind of content (in terms of maturity rating) that is most prevalent on Netflix.

Let’s start with the distribution of type (Movie vs TV Show).

The plot shows that there are more movies than TV shows in the dataset, indicating that Netflix has a larger collection of movies compared to TV shows.

Next, let’s analyze how the addition of content on Netflix has trended over the years. To do this, we first need to extract the year from the date_added column. Note that we currently have it as a string, so we’ll convert it to a datetime format and then extract the year.

The plot shows the number of movies and TV shows added to Netflix each year. We can see a clear trend of increasing content addition over the years, with a notable increase in the number of movies added around 2016 and a steady increase in the number of TV shows added each year. There’s a particularly significant rise in content added from 2018 to 2020.

Next, let’s look at which countries produce the most content that ends up on Netflix. Since some shows have multiple countries listed, we’ll need to split these entries to get an accurate count. For the purpose of this analysis, we’ll consider the top 10 countries.

The bar plot shows the top 10 countries that produce content available on Netflix. The United States is the largest producer by far, followed by India, United Kingdom, Canada, and France.

Finally, let’s look at the distribution of content ratings on Netflix. This can provide an idea about the target audience in terms of age group and content maturity.

The plot shows the distribution of content ratings on Netflix. The most common rating is “TV-MA”, which is intended for mature audiences, followed by “TV-14”, suitable for children aged 14 and above, and “TV-PG”, which suggests parental guidance.

This concludes our exploratory data analysis. These insights give us a good understanding of the dataset and the type of content available on Netflix.

Would you like to proceed with modeling, or is there anything else you’d like to explore in the dataset? Modeling is typically done for prediction or classification tasks, and might not be necessary if our goal was to simply understand the dataset.

Code Interpreter carried out an exploratory analysis from 4 different angles. These angles served as excellent starting points for gaining a quick overview of the dataset and laying the foundation for in-depth analysis.

During the analysis, Code Interpreter automatically converted the data types to match the analysis goals. For instance, it converted string date formats to Pandas datetime objects for processing using Python’s Pandas library.

Additionally, Code Interpreter generated charts based on the analysis objectives and presented the analysis conclusions, even without explicit requests. By default, it generated bar charts, but you could ask it to change the chart type if desired.

Lastly, it outlined the next steps, whether to continue data exploration or begin modeling.

Code Interpreter’s EDA was impressive. It handles much of the heavy lifting, particularly beneficial for individuals who lack analytical skills from the beginning.

Let’s assign it more analytical tasks to further assess its performance.

Here’s my prompt:

Analyze duration of movies.

ChatGPT’s response:

The duration column contains the length of movies in minutes and the number of seasons for TV shows. To analyze movie durations, we first need to separate the movies from the TV shows. Then we can look at the distribution of movie durations and find out things like the shortest, longest, and average movie length.

Let’s start by separating the movies and extracting the duration in minutes.

Here are some statistics about the duration of movies on Netflix:

• The shortest movie is 3 minutes long.
• The longest movie is 312 minutes long.
• The average movie length is approximately 100 minutes.
• The 25th percentile (i.e., 25% of movies are shorter than this length) is 87 minutes.
• The 50th percentile, or median (i.e., half of the movies are shorter than this length) is 98 minutes.
• The 75th percentile (i.e., 75% of movies are shorter than this length) is 114 minutes.

These statistics provide a general idea about movie lengths. However, visualizing the distribution can provide a more intuitive understanding. Let’s plot a histogram of movie durations.

The histogram shows the distribution of movie durations on Netflix. Most movies are between 80 and 120 minutes long, with a peak around 90-100 minutes. There are fewer very short or very long movies, as evidenced by the drop-off on both ends of the distribution.

This concludes the analysis of movie durations. Is there anything else you’d like to explore in this dataset?

My prompt:

Show me the relationship between genres and their count

ChatGPT:

To analyze the relationship between genres and their count, we first need to split the listed_in column, as each entry can contain multiple genres separated by commas.

Let’s count the occurrences of each genre and visualize the result. For this analysis, we’ll consider all genres, not just those for movies or TV shows.

The bar plot shows the count of Netflix content by genre. The genres “International Movies”, “Dramas”, and “Comedies” have the highest counts, meaning they are the most common genres for content available on Netflix.

This concludes our analysis of genres and their counts. Is there anything else you’d like to explore in this dataset?

Natural Language Processing

Let’s now test Code Interpreter’s abilities with Natural Language Processing (NLP) tasks. NLP aims to enable computers to comprehend and interact with human language. ChatGPT does employ NLP techniques to model language, understand syntactic and semantic relationships, and perform sentiment analysis.

Code Interpreter uses NLTK (Natural Language Toolkit), a popular Python library for NLP tasks. It helps those lacking deep data analysis knowledge to efficiently process and analyze text data.

For example, I tasked it to:

Analyzing the most frequent words in titles.

Word frequency calculation can provide insights into the main content of the text.

Check out ChatGPT’s output:

Analyzing the most frequent words in titles can provide insights into popular themes or trends. For instance, words like “love”, “war”, “world” might indicate common topics covered in the content.

We’ll begin by extracting all the words from the title column, then count the frequency of each word. To get meaningful results, we’ll exclude common English words (like “the”, “a”, “and”) that don’t provide much information. These are known as “stop words”. Let’s find the top 20 most frequent words.

I apologize for the inconvenience, it appears that I’m unable to download the set of English stop words due to the limited permissions in the current environment.

A workaround would be to manually define a basic set of English stop words. This set won’t be as comprehensive as the one provided by NLTK, but it should work for our purposes. Let’s proceed with this approach.

The bar plot shows the 20 most frequent words in Netflix titles after excluding a basic set of English stop words.

Words like “love”, “man”, “world”, “life”, and “story” are among the most common, suggesting these themes are prevalent in Netflix content.

Keep in mind that this analysis might be affected by other common words that were not included in our basic set of stop words.

Is there anything else you’d like to explore in this dataset?

Code Interpreter lacks a comprehensive stop word list, so it didn’t exclude stop words like ‘&’, ‘-‘, ‘with’, ‘i’, which are irrelevant for word frequency analysis.

Therefore, let’s provide it with unnecessary stop words to ignore. Also, since word frequencies are often better visualized through word clouds, I submitted the following prompt:

Pleas also remove the stop words ‘&’, ‘my’, ‘2’, ‘with’, ‘i’, ‘on’, ‘you’, ‘man’, ‘little’, ‘one’, ‘-‘, ‘from’. Use word cloud for presentation.

Here’s what ChatGPT produced:

The word cloud provides a visual representation of the most frequent words in Netflix titles, with the size of each word corresponding to its frequency. Common words like “love”, “world”, “life”, “story”, “girl”, “baby”, and “christmas” are particularly prominent, suggesting these themes are prevalent in Netflix content.

Keep in mind that the exact placement and color of words in a word cloud are randomly generated and do not carry any meaning. The important aspect is the relative size of the words.

Is there anything else you’d like to explore in this dataset?

Time Series Analysis

Time series analysis can uncover data trends and patterns over time, aiding in informed decisions and future predictions. For instance, in finance, it could predict stock prices or market trends, and in weather forecasting, it could estimate temperature shifts or rainfall trends.

Let’s employ Code Interpreter to trace the evolution of TV/Movie genres over time.

My query:

Observe how the genres of movies and TV shows have changed over time.

ChatGPT’s response:

Analyzing how the genres of movies and TV shows have changed over time can provide insight into trends and shifts in viewer preferences or content production strategies.

To do this, we’ll need to:

1. Separate movies and TV shows.
2. Split the ‘listed_in’ column as each entry can contain multiple genres.
3. Group by ‘release_year’ and count the frequency of each genre per year.

Let’s start with the movies. We’ll plot the top 5 genres over time for clarity.

The line plot shows the number of movies in the top 5 genres over time. The genres include “International Movies”, “Dramas”, “Comedies”, “Documentaries”, and “Action & Adventure”.

We can observe that the number of “International Movies”, “Dramas”, and “Comedies” has significantly increased over the years, particularly after 2000. “Documentaries” also saw an increase, but not as substantial. The “Action & Adventure” genre has remained relatively steady over the years.

Next, let’s perform the same analysis for TV shows.

The line plot shows the number of TV shows in the top 5 genres over time. The genres include “International TV Shows”, “TV Dramas”, “TV Comedies”, “Crime TV Shows”, and “Kids’ TV”.

We can see that the number of “International TV Shows”, “TV Dramas”, and “TV Comedies” has significantly increased over the years, particularly after 2010. “Crime TV Shows” and “Kids’ TV” also saw an increase, but not as substantial.

These trends indicate a shift in the types of content being produced and added to Netflix, with a clear increase in international content and dramas for both movies and TV shows.

Is there anything else you’d like to explore in this dataset?

Statistical Inference

Statistical inference draws conclusions and makes predictions about a population based on a data sample. Techniques like hypothesis testing and confidence intervals quantify our estimates’ uncertainty.

Let’s explore a possible correlation between the ‘release_year’ and ‘date_added’ columns. I queried ChatGPT:

Performing a correlation test to see if there’s a significant linear relationship between ‘release_year’ and ‘date_added’.

ChatGPT’s response:

To perform a correlation test, we need to ensure that both variables are numerical. The ‘release_year’ is already numerical, but ‘date_added’ is a datetime object. We’ll need to extract the year from ‘date_added’ and use that for our correlation test.

The Pearson correlation coefficient can be used to measure the strength and direction of the linear relationship between two variables. The value ranges from -1 to 1. A value close to 1 indicates a strong positive relationship, a value close to -1 indicates a strong negative relationship, and a value close to 0 indicates no linear relationship.

Let’s calculate the Pearson correlation coefficient between ‘release_year’ and the year the show was added to Netflix.

The Pearson correlation coefficient between ‘release_year’ and ‘year_added’ is approximately 0.11. This indicates a weak positive linear relationship between these two variables.

In other words, there is a slight tendency for shows to be added to Netflix closer to their release year, but the relationship is not very strong. It’s likely that other factors, such as licensing agreements and content strategy, play a significant role in determining when a show is added to Netflix.

Is there anything else you’d like to explore in this dataset?

Data Mining

Data mining digs out valuable information from large, complex datasets, revealing hidden relationships or trends that guide decision-making. For example, in retail, data mining can discover customer buying patterns, supporting personalized marketing strategies and boosting sales.

Let’s examine any hidden relationship between ‘Director’ and ‘Genre’.

Discover associations between directors and genres.

ChatGPT’s response:

Analyzing the associations between directors and genres can provide insights into the specific types of content certain directors are known for.

To do this, we’ll need to:

1. Split the ‘director’ column as each entry can contain multiple directors separated by commas.
2. Split the ‘listed_in’ column as each entry can contain multiple genres.
3. Group by ‘director’ and count the frequency of each genre.

However, please note that this could potentially lead to a large amount of data as there are many unique directors and genres. For the sake of this analysis, let’s consider the top 20 directors who have directed the most shows.

Let’s proceed with this approach.

The table above represents the associations between the top 20 directors (who have directed the most shows) and the genres of their shows. Each cell represents the number of shows a director has in a particular genre.

For instance, Cathy Garcia-Molina has directed 7 shows in the “Comedies” genre, while Suhas Kadav has directed 16 shows in the “Children & Family Movies” genre.

To visualize this data better, we can generate a heatmap. Heatmaps can help us understand where the highest concentrations of shows lie in terms of directors and genres. Let’s proceed with that.

The heatmap provides a visual representation of the associations between the top 20 directors (who have directed the most shows) and the genres of their shows. The color intensity in each cell represents the number of shows a director has in a particular genre. Darker cells indicate a higher number of shows.

For example, the heatmap reveals that director Suhas Kadav is particularly associated with the “Children & Family Movies” genre, and Rajiv Chilaka with “Kids’ TV”. Director Cathy Garcia-Molina is associated with “Comedies”, and Anurag Kashyap with “Thrillers”.

Keep in mind that a director can be associated with multiple genres, and that this analysis only includes the top 20 directors based on the number of shows they’ve directed.

Is there anything else you’d like to explore in this dataset?

Machine Learning Modeling

Machine learning modeling educates computer algorithms to recognize patterns and make predictions or decisions based on input data. These models can generalize and accurately predict unseen data.

Code Interpreter leverages Python’s extensive machine learning libraries like scikit-learn. Here’s a question I posed to ChatGPT:

What machine learning models can you build for this dataset?

ChatGPT’s response:

The choice of machine learning model depends on the specific question or task you’re interested in. Here are some potential tasks and the corresponding models that could be used:

1. Content Recommendation: If you’re interested in recommending similar content to users based on their viewing history, you could use collaborative filtering models (like matrix factorization) or content-based filtering models (that recommend similar items based on item attributes).
2. Genre Prediction: If you want to predict the genre of a movie or TV show based on other features (like director, cast, description), you could use supervised learning models. These could include decision trees, random forest, support vector machines, or neural networks.
3. Sentiment Analysis: If you want to predict the sentiment of user reviews, you could use natural language processing models like Naive Bayes, logistic regression, or even more complex models like recurrent neural networks (RNNs) or transformers.
4. Trend Forecasting: If you’re interested in predicting future trends (like the number of movies or TV shows released in a particular genre), you could use time series forecasting models like ARIMA or exponential smoothing.

Please note that the effectiveness of these models depends heavily on the quality and quantity of the data, as well as the feature engineering and model tuning steps. Also, the model interpretation and evaluation should be done carefully to avoid overfitting and to ensure that the model generalizes well to unseen data.

Please let me know if there’s a specific task or question you’re interested in, and I can provide more detailed suggestions.

Even with our relatively small dataset of over 8,000 lines, we can still task Code Interpreter with building models. I submitted the following prompt:

Utilize the text data (like title and description) and categorical data (like director, cast, country, and rating) to build a content-based recommendation system.

ChatGPT’s response:

Sure, we can build a content-based recommendation system. Content-based recommendation systems work by using item features to give recommendations. In this case, the item features are the various characteristics of the movies or TV shows, such as title, description, director, cast, country, and rating.

Here is a general outline of how we can create such a recommendation system:

1. Preprocessing: This step involves cleaning and preparing the data for use in the model. This includes filling in missing values and converting text data into a numerical format that can be used by the model.
2. Feature Extraction: In this step, we’ll transform the categorical data into numerical data using techniques like one-hot encoding, and we’ll transform the text data using techniques like TF-IDF (Term Frequency-Inverse Document Frequency) or CountVectorizer.
3. Similarity Score Calculation: Next, we’ll calculate the similarity scores between different items (in this case, movies or TV shows). There are various ways to calculate similarity scores, such as cosine similarity, Jaccard similarity, or Euclidean distance.
4. Recommendation: Once we have the similarity scores, we can recommend items that are most similar to a given item.

Let’s start with the preprocessing step. We already handled missing values earlier, so we can move on to converting the text and categorical data into a format that can be used by our model. For simplicity, we’ll use the CountVectorizer technique, which converts a collection of text documents into a matrix of token counts. We’ll apply this to the ‘title’, ‘description’, ‘director’, ‘cast’, ‘country’, and ‘rating’ columns.

To do this, we’ll first combine these columns into a single text document for each movie or TV show, and then we’ll apply CountVectorizer.

The CountVectorizer has transformed our combined text data into a matrix of token counts. The resulting matrix has 8790 rows (corresponding to our 8790 movies and TV shows) and 53198 columns (corresponding to the different tokens in the text data).

The next step is to calculate the similarity scores between different movies and TV shows. There are various ways to calculate these scores, such as cosine similarity, Jaccard similarity, or Euclidean distance. Here, we’ll use cosine similarity, which is a common choice for text data.

Cosine similarity calculates the cosine of the angle between two vectors. If the vectors are identical, the cosine is 1 (corresponding to an angle of 0 degrees). If the vectors are orthogonal (i.e., not similar at all), the cosine is 0 (corresponding to an angle of 90 degrees). Thus, the closer the cosine similarity is to 1, the more similar the items are.

Let’s calculate the cosine similarity scores for our count matrix.

The cosine similarity matrix has been created successfully. The matrix is of size 8790×8790, which means each row (and each column) corresponds to a movie or TV show, and each cell in the matrix contains the cosine similarity score between two movies or TV shows.

Next, let’s define a function that takes in a movie or TV show title as input and outputs a list of the most similar movies or TV shows. This function will perform the following steps:

1. Get the index of the movie or TV show that matches the title.
2. Get the list of cosine similarity scores for that movie or TV show with all other movies or TV shows. Convert this list into a list of tuples where the first element is the position and the second is the similarity score.
3. Sort the list of tuples based on the similarity scores, in descending order.
4. Get the top elements of the sorted list.
5. Return the titles corresponding to the top elements.

The recommendation system seems to be working as expected. For the movie ‘Inception’, it recommended the following 10 movies:

1. Final Account
2. The World Is Not Enough
3. The Last Shaman
4. 9
5. Mars Attacks!
6. Dead in a Week (Or Your Money Back)
7. High Risk
8. Catch Me If You Can
9. Snowden
10. A Bridge Too Far

These recommendations are based on the similarity in their content features such as title, description, director, cast, country, and rating.

Do you want to test the recommendation system with another movie or TV show title?

Overall, the approach it takes to build a content-based recommender system is sound. Using CountVectorizer to convert text data to a numeric form is a good choice, as it simplifies the process of counting.

But CountVectorizer merely counts word occurrences, ignoring context, which might result in information loss. More advanced techniques like TF-IDF, Word2Vec, or Doc2Vec capture more semantic details. Code Interpreter didn’t employ these more complex methods, possibly due to the sandbox environment’s limitations in loading larger Python libraries or computational resources.

Let’s prompt it to build another model:

Predict the rating of a movie based on features like director, cast, country, release year, and genre.

ChatGPT’s reply:

Predicting movie ratings is a common problem in machine learning, often framed as a regression problem (if predicting the exact rating) or a classification problem (if predicting rating categories). In our case, the rating is a categorical variable with multiple classes, so we can use multi-class classification algorithms.

Some algorithms that could be suitable for this task include Decision Trees, Random Forests, Naive Bayes, Support Vector Machines, or even Neural Networks. For simplicity, we can start with a Decision Tree classifier.

However, before we can train a classifier, we need to preprocess the data. The features you mentioned (director, cast, country, release_year, genre) are mostly categorical and may have many unique values. We’ll need to convert them into a format that can be used with a classifier, which often involves encoding categorical variables as numerical ones.

Let’s start by examining these features and the target variable (rating) in more detail.

Here’s what we have:

• There are 18 unique ratings. However, it seems there are some errors since “74 min”, “84 min”, and “66 min” are not valid ratings. We’ll need to clean this up before we can use the rating variable as a target for classification.
• There are 4,529 unique directors. This is a high-cardinality categorical variable, and encoding it for use with a classifier could result in a very large number of features. We might need to consider other ways of using this information, such as grouping less frequent categories together.
• There are approximately 36,440 unique cast members. This is a large number and dealing with this feature could be tricky. One option might be to only consider the main cast members (for example, the first few listed), or to create a feature that represents the number of cast members.
• There are 749 unique countries. This is another high-cardinality categorical variable. As with the director feature, we might need to group less frequent categories together.
• There are 74 unique release years. This is a numerical feature and can be used directly with most classifiers.
• There are 42 unique genres. We could encode this variable using one-hot encoding, which would add 42 binary features to the dataset.

Before we proceed with further preprocessing and model training, we need to clean up the rating variable and handle the missing and incorrect values. Let’s do that next.

I allowed it to continue:

The incorrect ratings have been replaced with ‘Unknown’. The cleaned rating variable now has the following unique values:

• ‘PG-13’
• ‘TV-MA’
• ‘PG’
• ‘TV-14’
• ‘TV-PG’
• ‘TV-Y’
• ‘TV-Y7’
• ‘R’
• ‘TV-G’
• ‘G’
• ‘NC-17’
• ‘Unknown’
• ‘NR’
• ‘TV-Y7-FV’
• ‘UR’

These represent different content rating systems used to categorize the titles. There are movie ratings like ‘PG-13’, ‘PG’, ‘R’, ‘G’, ‘NC-17’, ‘NR’, and ‘UR’, as well as TV ratings like ‘TV-MA’, ‘TV-14’, ‘TV-PG’, ‘TV-Y’, ‘TV-Y7’, ‘TV-G’, and ‘TV-Y7-FV’.

Next, let’s address the high-cardinality categorical features. One common strategy is to group the less frequent categories into an ‘Other’ category. For this dataset, we can apply this strategy to the director, cast, and country variables. We’ll set a threshold for each of these features, and categories that appear less frequently than this threshold will be grouped into the ‘Other’ category.

After this, we’ll be ready to encode these categorical features and prepare the data for training the classification model.

Let’s proceed with this plan.

I let ChatGPT go ahead:

After grouping less frequent categories into an ‘Other’ category, we now have:

• 13 unique directors
• Approximately 459 unique cast members
• 60 unique countries
• 42 unique genres

This is still a large number of categories, especially for the cast feature. For cast and genre, we might need to consider other approaches, such as only considering the first few cast members or main genres.

The next step is to encode these categorical features. For this, we can use one-hot encoding or target encoding. One-hot encoding would increase the dimensionality of our data significantly, especially for high-cardinality features. Target encoding could be a better choice as it transforms the categorical variable into a single new column and captures valuable information on the target variable.

However, before we proceed with encoding, it would be beneficial to split our data into a training set and a test set. This way, we can ensure that our encoding methods do not leak information from the test set into the training set.

Let’s proceed by splitting the data. For simplicity, let’s consider only director, country, and release_year as features to predict rating. We’ll handle the cast and genre features in a more advanced analysis if required.After grouping less frequent categories into an ‘Other’ category, we now have:

• 13 unique directors
• Approximately 459 unique cast members
• 60 unique countries
• 42 unique genres

This is still a large number of categories, especially for the cast feature. For cast and genre, we might need to consider other approaches, such as only considering the first few cast members or main genres.

The next step is to encode these categorical features. For this, we can use one-hot encoding or target encoding. One-hot encoding would increase the dimensionality of our data significantly, especially for high-cardinality features. Target encoding could be a better choice as it transforms the categorical variable into a single new column and captures valuable information on the target variable.

However, before we proceed with encoding, it would be beneficial to split our data into a training set and a test set. This way, we can ensure that our encoding methods do not leak information from the test set into the training set.

Let’s proceed by splitting the data. For simplicity, let’s consider only director, country, and release_year as features to predict rating. We’ll handle the cast and genre features in a more advanced analysis if required.

I let ChatGPT proceed:

We have successfully split the data into a training set with 7,045 instances and a test set with 1,762 instances. Each instance has 3 features: director, country, and release_year.

The next step is to encode the categorical features. As mentioned earlier, we’ll use target encoding for this. Target encoding replaces each category of a categorical feature with the mean value of the target variable for that category. For example, if the average rating for movies directed by “Director A” is 7.5, then “Director A” would be replaced by 7.5 in the director feature.

Let’s go ahead and apply target encoding to the director and country features. We’ll fit the encoder on the training data and then transform both the training and test data.

I apologize for the inconvenience. The category_encoders module is not available in this environment. As an alternative, we can perform a simple type of encoding called frequency encoding, where each category is replaced by its frequency in the training data. This type of encoding can still capture some information about the target variable, since categories that occur more frequently might have a different average rating than less frequent categories.

Let’s proceed with frequency encoding for the director and country features.

We have successfully encoded the director and country features using frequency encoding. The director and country columns in the DataFrame now represent the frequency of each director and country in the training data, respectively.

Here are the first few rows of the encoded training and test sets:

Training Set:

	director	country	release_year
5471	0.683605	0.318524	2017
530	0.298935	0.318524	2015
5594	0.683605	0.318524	2017
996	0.683605	0.026544	2021
6951	0.298935	0.020014	2018

Test Set:

	director	country	release_year
4970	0.683605	0.318524	2018
3362	0.683605	0.318524	2019
5494	0.298935	0.009510	2017
1688	0.298935	0.318524	2020
1349	0.298935	0.003549	2012

Next, we’ll need to encode the target variable rating. Since this is a multi-class classification problem, we can simply convert each unique rating to a unique integer using label encoding. After that, we can train our classification model.

I asked ChatGPT to continue:

We have successfully encoded the target variable rating using label encoding. Each unique rating has been assigned a unique integer, as shown below:

• ‘G’: 0
• ‘NC-17’: 1
• ‘NR’: 2
• ‘PG’: 3
• ‘PG-13’: 4
• ‘R’: 5
• ‘TV-14’: 6
• ‘TV-G’: 7
• ‘TV-MA’: 8
• ‘TV-PG’: 9
• ‘TV-Y’: 10
• ‘TV-Y7’: 11
• ‘TV-Y7-FV’: 12
• ‘UR’: 13
• ‘Unknown’: 14

Now that our data is properly preprocessed, we can proceed to train a classification model. As mentioned earlier, we will start with a Decision Tree classifier due to its simplicity and interpretability. Let’s proceed with this.

I let ChatGPT go ahead:

I apologize for the confusion. It seems there was a discrepancy between the number of unique classes in the test set and the number of unique classes in the entire dataset, leading to a mismatch in the classification report.

This can happen when the training and test sets do not contain all the same classes, which can occur due to the random splitting of the data. In this case, it appears that there are classes in the target variable that are not represented in the test set.

To resolve this, we can specify the labels parameter in the classification_report function to ensure that it considers all possible classes. Let’s correct this and generate the classification report again.

I apologize for the oversight. It appears I forgot to import the numpy library which is used in this code. Let me correct this and generate the classification report again.

Here’s the classification report for the Decision Tree classifier:

           precision    recall  f1-score   support

        G       0.00      0.00      0.00        12
    NC-17       0.00      0.00      0.00         0
       NR       0.14      0.06      0.09        16
       PG       0.28      0.11      0.16        62
    PG-13       0.25      0.29      0.27        87
        R       0.22      0.19      0.20       163
    TV-14       0.45      0.46      0.46       414
     TV-G       0.00      0.00      0.00        43
    TV-MA       0.48      0.70      0.57       662
    TV-PG       0.28      0.08      0.13       185
     TV-Y       0.21      0.06      0.09        52
    TV-Y7       0.50      0.11      0.18        65
 TV-Y7-FV       0.00      0.00      0.00         0
       UR       0.00      0.00      0.00         0
  Unknown       0.00      0.00      0.00         1

micro avg       0.42      0.42      0.42      1762
macro avg       0.19      0.14      0.14      1762
weighted avg       0.38      0.42      0.38      1762

The model seems to have difficulty predicting certain classes, such as ‘G’, ‘NC-17’, ‘TV-G’, ‘TV-Y7-FV’, ‘UR’, and ‘Unknown’. This is likely due to these classes being underrepresented in the data.

The weighted average precision, recall, and F1-score are around 0.38 to 0.42, indicating that there is room for improvement.

This is a simple model trained on a subset of the available features, and it did not handle high-cardinality categorical features (such as cast and genre) or text features (such as description). A more complex model that can handle these features might be able to achieve better performance. Additionally, techniques like class balancing or model tuning could also help improve the results.

I asked ChatGPT to continue:

To improve the performance of our model, we can attempt a more sophisticated approach. We could use a Random Forest Classifier, which tends to perform better than a single decision tree by combining the predictions of multiple decision trees. Also, Random Forests are less prone to overfitting.

However, we should note that handling the high-cardinality categorical features like cast and genre is not straightforward. As mentioned before, one approach could be to only consider the main cast members or the main genres. For the sake of this demonstration, we will exclude these features and focus on director, country, and release_year.

Let’s proceed with training a Random Forest Classifier on our data. We’ll also use cross-validation to get a better estimate of the model’s performance.

The cross-validation scores for the Random Forest Classifier are approximately:

• 0.41
• 0.42
• 0.42
• 0.42
• 0.40

These scores represent the accuracy of the classifier on different partitions of the data. As we can see, the scores are slightly lower than those of the Decision Tree classifier. This could be due to the randomness inherent in the Random Forest algorithm, or it could be that the Decision Tree overfit the training data.

It’s also important to note that accuracy might not be the best metric for this problem, especially if the classes in the target variable are imbalanced. Other metrics like precision, recall, or the F1 score might be more informative.

Finally, as mentioned before, a more advanced model that could handle the high-cardinality categorical features (cast and genre) and the text feature (description) might be able to achieve better performance. Such a model could involve natural language processing (NLP) techniques for the description feature, and more sophisticated encoding methods for the cast and genre features. However, developing such a model would be a more complex task and is beyond the scope of this interaction.

Let me know if you want to proceed further or need help with anything else.

Let’s summarize the above processes: I tasked ChatGPT with creating two machine learning models. The first, a content recommendation system, was a breeze for it. However, the second model, predicting movie ratings, was more challenging. Code Interpreter encountered limitations due to the sandbox environment, sometimes forgetting to import crucial libraries, though it would self-correct.

In the movie rating prediction task, it initially tried a simple decision tree classifier but switched to a random forest classifier when the weighted average precision, recall, and F1 scores didn’t measure up. The results were still unsatisfactory, and creating more advanced models required complex coding techniques, which are beyond Code Interpreter’s current abilities.

Impact of Code Interpreter on Data Analysis

The saying “everyone is a data analyst” is becoming more tangible with Code Interpreter. Prior to ChatGPT and Code Interpreter, this goal seemed far off. But now, it’s within reach.

After the tests detailed above, we can see what Code Interpreter is capable of. I tested it using professional data analysis, but for most people who use Excel for data analysis, Code Interpreter is ideal. It can handle many data analysis tasks commonly encountered in daily office work. You can directly upload Excel files and let ChatGPT assist with data analysis, even creating visually appealing charts through conversation.

For professional data analysts or data scientists, Code Interpreter still offers considerable help. To utilize its full potential, you need solid statistics knowledge and familiarity with different data analysis methods. This way, you can effectively guide Code Interpreter in performing various tasks. For complex data analysis involving machine learning modeling, Code Interpreter faces bigger challenges. It can provide basic modeling and ideas, but its current sandbox environment limits it.

In general, we’re seeing a move towards low-code in data analysis. We’re gradually writing less code and focusing more on managing the data analysis process, letting AI tools like ChatGPT handle the data analysis under our guidance. This approach saves time, letting us better understand the business context, strengthen our statistical knowledge, and improve our proficiency in various aspects of machine learning modeling.

Limitations of Code Interpreter in Data Analysis

While Code Interpreter has revolutionized data analysis, it also has limitations:

• Plus member requirement: Code Interpreter, being an OpenAI-developed feature, is currently available only to Plus members. This may hinder its widespread adoption. Even with a Plus account, the limitation of 50 sessions per three hours can be inconvenient for larger-scale projects.
• Lack of internet connection: Code Interpreter operates within a sandbox environment, meaning libraries not built-in can’t be downloaded from the internet. Plus, if your analysis topic is quite new, ChatGPT might lack real-time knowledge, affecting decision-making.
• Unstable behavior: Code Interpreter has a time limit on how long it can retain data. If this limit is passed, re-uploading the data might be needed. Also, its inherent randomness might produce different analyses each time.
• Limited machine learning capabilities: While Code Interpreter handles regular data analysis well, complex machine learning modeling is a bigger challenge. It provides basic modeling and ideas but lacks many crucial libraries and computing resources.

Despite these constraints, Code Interpreter still offers valuable support and simplifies the data analysis process for many users.

Conclusion

In conclusion, Code Interpreter is a game-changer, bringing data analysis within everyone’s reach. It simplifies tasks, from basic Excel work to more complex Python code analysis.

While it has limitations—like a Plus account requirement and limited machine learning capabilities—it’s undeniably a powerful tool. As it evolves, we anticipate seeing these limitations addressed.

Ultimately, we’re stepping into an era where we focus more on guiding the process, and less on the coding itself, letting AI handle the heavy lifting. Code Interpreter brings us one step closer to a future where everyone can be a data analyst.