Open Prompts
Submit a prompt
Analysis

Investigate And Handle Data Outliers

Give the numeric columns to check and describe what the dataset represents, and this prompt detects outliers with IQR, Z-score, and isolation forest in parallel, then classifies each one as a data-entry error, a genuine extreme value, or part of a systemic cluster pointing to an upstream process bug. It shows each outlier alongside its row context, quantifies how much removing them shifts the mean and median, and generates Python code that flags the outliers and outputs a cleaned dataset with the recommended handling applied.

Prompt
Investigate the outliers in my dataset:

Columns to check for outliers: {numeric_columns}
Dataset context: {dataset_context}

For each column:
1. Detect outliers using more than one method (IQR, Z-score, isolation forest)
2. Classify each outlier as:
   - A data entry error (clearly wrong values)
   - A true extreme value (real but unusual)
   - A systemic issue (a cluster of related outliers pointing to a process problem)
3. Show the outlier values alongside their row context (other columns for those rows)
4. Suggest handling: remove, cap/floor, keep as-is, or investigate further
5. Quantify how much the outliers shift summary statistics (mean/median with vs. without them)

Write Python code that flags the outliers and produces a cleaned dataset with your recommended handling applied.
Download .md

Variables

Related prompts

Analysis

Handle Missing Values By Type

List the columns with missing data — name, type, approximate percent missing, and likely cause — and this prompt classifies each one as MCAR, MAR, or MNAR before recommending a fix. It weighs whether the missingness itself is informative, considers adding a was_missing flag, justifies the chosen imputation method against alternatives for every column, and outputs the Python code implementing the strategy column by column.

Analysis

Analyze A/B Test Results Rigorously

A statistics-first review of an A/B test that goes past a single p-value before you ship the result. - Inputs: control and treatment group results, primary metric, test duration, and optional segments - Checks: post-hoc power, statistical significance with confidence interval, absolute and relative effect size - Flags: novelty-effect risk, sample ratio mismatch, peeking problems, and multiple-comparison issues - Output: a ship/kill/keep-testing call with the reasoning behind it

Analysis

Analyze Dataset Correlations And Risks

Goes past a raw correlation matrix to flag the relationships in a dataset that actually matter before modeling starts. - Inputs: key variables of interest plus dataset columns, types, and row count - Analysis: correlation matrix, non-obvious pairings, multicollinearity risk, categorical-to-numeric associations, and time-lagged correlations - Warns: spurious correlations that look causal but aren't - Output: plain-language findings plus runnable Python code