ChatGPT for Data Science and Data Analysis in Python

Learn to accelerate data preparation with ChatGPT in Python. Import data in Colab, inspect structure, remove unused columns, handle missing values and duplicates, and export clean results.

Leverage ChatGPT for data science to master categorical data encoding in Python, including identifying categorical variables, applying ordinal and nominal encodings, and building JSON-based pipelines with Python scripts.

Use a Python script to perform descriptive data analysis on a dataset, extract statistics such as count, mean, and standard deviation, and derive actionable business insights with ChatGPT.

Engineer three new encoded features—skills enc, impact enc, and experience enc—by aggregating and normalizing related skill, impact, and experience values, with iterative prompts and ChatGPT-assisted box plots for verification.

Learn how to use ChatGPT as a co-pilot for exploratory data analysis in Python, crafting prompts, generating diverse visualizations, and interpreting charts with accessible explanations.

Learn to analyze qualitative, freeform survey data with ChatGPT as a copilot, transforming text into categorized insights and concise tables through text categorization without writing code.

Learn to use ChatGPT for text classification and sentiment analysis on a Reddit vaccine comments dataset, producing a table that labels comments as negative, neutral, or positive in Google Colab.

Learn to build data analysis reports with charts and insights, generate a clean pdf, and craft concise emails with an executive action plan for stakeholders using ChatGPT in Google Colab.

Set up and use the OpenAI ChatGPT API for data analysis in Python, compare GPT-4 and GPT-3.5 turbo pricing, and manage tokens, API keys, and usage limits.

Test prompts with the ChatGPT API in Google Colab to achieve stable, predictable results. Use few-shot prompts, temperature and max tokens to tighten responses for data analysis workflows.

Use function calling in GPT-4 to convert natural language queries into JSON function calls. Implement a get_sales function in Colab to fetch sales data.

Explore a step-by-step data science workflow to predict e-commerce churn, including feature engineering with tree of thoughts, exploratory data analysis, handling class imbalance, and model training with ChatGPT.

Learn to identify churned e-commerce users with ChatGPT by using context-rich, iterative prompts, a 30-day churn threshold, and loading data from Google Drive for Python analytics.

Learn to apply the tree of thoughts prompting technique for feature engineering to predict churn, evaluating features like recency, frequency, monetary value, and other metrics with probability and confidence assessments.

Streamline churn feature engineering in Python by separating context from tasks, verify ChatGPT outputs for accuracy, and build features in one dataframe using Google Drive data.

Explore exploratory data analysis with chatgpt to validate feature engineering results, using visual inspections, summary statistics, distribution plots, and spot checking to ensure dataset correctness.

Learn data preprocessing with ChatGPT in data analysis projects, address class imbalance, apply normalization, encode categorical variables, and split data to prepare a balanced dataset for model building.

ChatGPT assists with predictive modeling by choosing metrics and establishing a simple baseline for ecommerce churn, using logistic regression and interpreting recall and F1 to guide model evaluation.

Explore model selection with ChatGPT by evaluating, filtering redundant steps, and comparing random forest, gradient boosting, and SVM for binary classification, with practical tuning guidance.

Explore cross-validation, model evaluation, and final model selection for e-commerce churn prediction, guided by ChatGPT in Python. Compare Random Forest and XGBoost with cross_val_score on k-folds, balancing recall and precision.

Learn to plot learning curves with ChatGPT to diagnose overfitting or underfitting, interpret training and cross-validation scores, and streamline the data science process for reliable model performance.

Harness ChatGPT to complete final model training and interpretation for e-commerce churn, compare trade-offs between using all data versus preserving a test set, and save the robust model.

Explore how LangChain unifies diverse data tools for data analysis in Python, using prompts and chain-of-thought enhancements with GPT and OpenAI API keys to extract practical insights from Titanic data.