Learn to start ChatGPT conversations for data science with a census dataset to predict income, list features, handle missing values, and generate Python scripts for preprocessing and classification.

Generate Python code in Colab to load census.csv, display basic statistics and info for numerical columns, and show unique value counts for categorical features and the income target.

Generate and visualize statistics for numeric columns with a Python script, then use ChatGPT to interpret results, identify insights like class imbalance and correlations, and guide data preprocessing.

Explore identifying and replacing interrogation marks as missing values in a dataset using Python and pandas in Google Colab, focusing on categorical columns like work class, occupation, and native country.

Explore correlation analysis between education and income, age and hours per week, and capital gain and capital loss, and visualize these relationships with Python using scatter, box, and bar plots.

Visualize categorical columns with bar, pie, treemap, and heatmap charts using Python scripts, while handling errors when income is categorical and generating per-attribute charts of unique values.

Generate and compare ten chart types for numerical attributes with ChatGPT, from histograms and box plots to heatmaps and pair plots, then select and refine visuals in Colab.

Learn to generate dynamic charts in Python with Plotly, including dynamic correlation matrices and dynamic 3D scatterplots, and iteratively refine code to display values.

explores class imbalance in the income attribute using oversampling, undersampling, and smote to balance data, visualize distributions, and apply preprocessing for more reliable models.

One hot encoding turns categorical features into binary columns, preserving information and avoiding numerical assumptions. It shows when to apply it to linear, logistic, and kNN models in census study.

Apply feature scaling to align numeric attributes on a common scale. Compare normalization and standardization, note outlier sensitivity, and demonstrate standardization with a sklearn script on the encoded data.

Learn to split data into training and testing sets, using train_test_split and cross-validation, with 70–80% training and 30% testing, plus one-hot encoded features and smote resampling.

Explore classification algorithms in sklearn and evaluate ten models using train and test data, reporting accuracy, precision, recall, F1, and confusion matrices to compare performance.

Explore cross-validation to evaluate multiple algorithms—train and test across k folds, report mean accuracy and standard deviation for classifiers like decision tree, random forest, AdaBoost, and QDA.

Interpret cross-validation results across classifiers using accuracy, precision, recall, and F1 score; random forest achieves about 88% accuracy with a stable standard deviation, outperforming the other models.

Tune hyperparameters for a random forest to improve results using grid search cross-validation on a trimmed parameter grid including number of estimators, max depth, and min samples split.

Save and load a trained random forest with the best hyperparameters using joblib, run predictions on sample data, and test results for app development.

Learn feature selection with x_resampled and y_resampled, identify the top five attributes by feature importance, and compare full versus reduced data random forest models with re-applied preprocessing.

Predict house prices using a regression dataset by analyzing features like bedrooms, bathrooms, sqft, and living area, and learn to read the house prices csv and compute basic statistics.

Generate and explore charts to visualize correlations between price, sqft living, grades, condition, and renovation years, using scatter plots, histograms, box plots, and a location map.

Standardize the dataset (excluding price), split 70/30 into training and testing sets, and evaluate multiple regression algorithms, with random forest and gradient boosting performing best and SVR the worst.

Use a Twitter dataset to classify tweets as positive or negative by analyzing text attributes, balancing a 10,000-tweet sample, and generating insights with word clouds and preprocessing in Python.

Continue exploring the Twitter data set by regenerating statistics for the balanced set sample, analyzing sentiment distribution, tweets by day, and word clouds with stop-word removal.

Implement text preprocessing to prepare data for machine learning by lowercasing, removing HTML, URLs, usernames, numbers, punctuation, and stopwords; then tokenize to remove stopwords in the balanced sample.

Recap exploratory data analysis and machine learning case studies: income classification, house price regression, and tweet sentiment classification; and highlight ChatGPT as a copilot for faster analysis and code generation.