Python AI Modle To Predict The Problem Statement Based On The Given Array !!!

Here is an example of a code to create an AI model using Python's scikit-learn library : —

import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB

# Define the input data, consisting of arrays and their corresponding problem statements
arrays = ["[1,2,3,4]", "[5,6,7,8,9]", "[10,20,30,40,50]"]
problems = ["Find the sum of all elements", "Find the largest element", "Find the average of all elements"]

# Convert the arrays and problems into a dataframe
data = {'Array': arrays, 'Problem': problems}
df = pd.DataFrame(data)

# Vectorize the arrays using the CountVectorizer method
vectorizer = CountVectorizer()
array_vectors = vectorizer.fit_transform(df['Array'])

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(array_vectors, df['Problem'], test_size=0.2)

# Train the AI model using Multinomial Naive Bayes
model = MultinomialNB()
model.fit(X_train, y_train)

# Test the model on the testing data
accuracy = model.score(X_test, y_test)
print("Accuracy: ", accuracy)

This code creates an AI model that uses Multinomial Naive Bayes to predict the problem statement based on the given array. The model is trained on a dataset of arrays and their corresponding problem statements, and its accuracy is evaluated on a separate testing dataset.