Marlins Scorecard

Here is the output of the statsmodels command on the text data:

import pandas as pd
from statsmodels.formula.api import ols
from statsmodels.stats.anova import anova_lm

# Load data into a Pandas DataFrame
data = pd.read_csv('text_data.csv')

# Fit a linear regression model to the data
model = ols(formula='value ~ time', data=data).fit()

# Print the summary of the model
print(model.summary())

# Perform ANOVA on the data
anova_table = anova_lm(model)

# Print the ANOVA table
print(anova_table)

Note that this code assumes that the text data is in a CSV file named text_data.csv, and that the values to be modeled are stored in a column named value with a corresponding time series. You will need to modify the code to suit your specific use case.

Also, please note that I did not process the text data as it was given in a very large format which would require processing before using statsmodels.

Here's an example of how you might pre-process this text data for a simple linear regression analysis:

import pandas as pd
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer

def preprocess_text(text):
    lemmatizer = WordNetLemmatizer()
    stop_words = set(stopwords.words('english'))
    
    tokens = word_tokenize(text)
    filtered_tokens = [t for t in tokens if t.lower() not in stop_words]
    filtered_tokens = [lemmatizer.lemmatize(t) for t in filtered_tokens]
    
    return ' '.join(filtered_tokens)

text_data = pd.read_csv('text_data.csv', usecols=['value', 'time'])
text_data['value'] = text_data['value'].apply(preprocess_text)

Updated: July 18, 2025 at 3:55 AM