ChatGPT for Data Science and Machine Learning

Explore data preprocessing with ChatGPT using a 100-row patient data set to encode diagnosis, handle missing values, remove duplicates, and generate a mapped CSV while emphasizing data masking.

Apply machine learning with ChatGPT to medical data, building on preprocessing, compares decision trees and KNN, and emphasizes careful prompt design and human verification.

Describe your furniture data to ChatGPT and analyze a four-table database (products, customers, orders, ratings) in Python without uploading data. Assess revenue, sales, and top clients from the last month.

Describe your data to ChatGPT and analyze a furniture company dataset without uploading to the advanced analysis tool, examining top products by revenue, sales, and ratings, and identifying top clients.

Analyze and prepare data in a jupyter notebook to merge orders and customers, then compute revenue and units sold, identifying top products and leading clients from the past month.

Perform exploratory data analysis on a real estate data set with ChatGPT, using histograms, scatter plots, and correlation analysis to reveal size, price, and location patterns, including outlier detection.

Explore exploratory data analysis with ChatGPT by building histograms, charts, and a correlation matrix; assess the size and price relationship, verify results, and practice outlier detection with skepticism and validation.

Learn hypothesis testing with ChatGPT by performing a two-sample t-test on grades for remedial class attendance, using Levene's test for equal variances; p = 0.11, cannot reject the null.

Clean and analyze the Marvel Comics database with regular expressions to preprocess text, uncover patterns, and reveal trends in descriptions and writers, with ChatGPT aiding code.

Analyze Marvel comic data with Python regular expressions and ChatGPT to identify top superhero names and titles, using regex to extract capitalized words, count frequencies, and preprocess missing values.

Explore how ChatGPT reads CSV files, provides brief data column summaries, and suggests analyses and algorithms for a movie database, including content-based recommendation engines.

Learn content-based movie recommendations using vectorized features and cosine similarity, compare with collaborative filtering, and observe a working engine that ranks Toy Story while noting age ratings.

Examine ethical implications of data and AI use with ChatGPT, focusing on privacy, data sensitivity, and bias, with examples like the Boston Housing Dataset.

Use ChatGPT to surface privacy, bias, and profiling risks in a fictitious social media data set, and apply anonymization and data minimization to protect ethics.

Explore sentiment analysis by classifying user course reviews and predicting star ratings with Naive Bayes, after preprocessing with ChatGPT Advanced Analysis Bot and exploratory data analysis.

Use Naive Bayes to classify text with Bayes theorem and conditional probabilities, illustrated by spam vs ham and word features like win; explore Multinomial and Bernoulli variants in scikit-learn.

Explore tokenization and vectorization for text classification with Naive Bayes, within the standard supervised learning pipeline of data preprocessing, feature selection, and model evaluation.

Learn how imbalanced data in classification affects model performance, why accuracy can be misleading, and how to address imbalance using appropriate evaluation metrics.

Learn how to handle imbalanced data in classification using resampling (undersampling, oversampling, smote), weighting, and data augmentation, plus algorithm adjustments for reliable, fair models.

Explore key metrics for evaluating classifier performance, including confusion matrix, precision, recall, and f1 score, and learn how imbalanced data can mislead accuracy.

Load the dataset, focus on text reviews and five-point ratings, handle missing or irrelevant rows, and perform exploratory data analysis to guide pre-processing for holdout validation.

Preprocess and explore user reviews for a data science workflow by converting ratings to numeric, removing missing text reviews, and analyzing distributions, summary statistics, and text length correlations.

Explore how regex analyzes text review data with ChatGPT, identifying sentiment cues, emphasis patterns, slang, and all-caps signals to uncover patterns in user reviews.

Explore Multinomial and Bernoulli Naive Bayes for sentiment classification, from preprocessing and bag-of-words feature extraction to model training, evaluation, and deployment on unseen reviews.

Classify five-class reviews with a multinomial naive bayes pipeline using text preprocessing, tokenization, and vectorization, then assess with a classification report and confusion matrix, addressing imbalanced data via oversampling (smote).

Transform the multi-class reviews into a binary good vs bad problem, apply Naive Bayes, compare baseline and oversampled models, and evaluate with confusion matrices and accuracy.

Test the model on the validation set after preprocessing to two classes, reporting around 92.5% accuracy and F1, and examine the classification report for per-class performance.