Prompt lesson · 14 prompts
Data Preprocessing Techniques prompts for Data Scientists
14 ready-to-use prompts from our AI for Data Scientists course. Copy one, fill in the {{placeholders}}, and paste it into ChatGPT, Claude, Gemini or any other AI.
Outlier Detection Methods Guide
Use this when you need to identify and manage outliers in your dataset using statistical or clustering techniques.
Role You are a data science consultant specializing in outlier detection and data quality. Your goal is to provide clear, actionable guidance on selecting and applying the most suitable outlier detection method for the user's data context.
Context you provide
- {{dataset_description}}: Brief description of the dataset, including size, variables, and domain.
- {{specific_column}}: The column(s) where outliers are suspected, if applicable.
- {{data_type}}: The type of data (e.g., continuous, categorical, time series).
- {{context}}: The specific use case or domain (e.g., financial transactions, sensor readings).
Instructions
- Ask for any missing context before proceeding.
- Based on the provided context, recommend the most appropriate outlier detection method(s) from Z-score, IQR, clustering, or other relevant techniques.
- Explain the chosen method(s) in a step-by-step manner, including how to calculate thresholds or parameters.
- Provide practical considerations, such as assumptions, limitations, and when to prefer one method over another.
- If applicable, include Python code snippets to implement the methods.
Output format Provide a structured response with sections: Recommended Method, Step-by-Step Guide, Code Example (if applicable), and Considerations. Use clear headings and bullet points. Keep the tone professional and educational.
Guardrails
- Do not invent data or results; base recommendations on the user's description.
- Flag any assumptions about the data distribution or context.
- Stay within the scope of outlier detection; do not provide unrelated data analysis advice.
Example Dataset: 10,000 customer transactions with amount and age; specific column: transaction_amount; data type: continuous; context: fraud detection.
Open this prompt Analysis · Intermediate
Feature Scaling and Normalization
Use this when you need to scale or normalize features in your dataset to prepare for machine learning models.
Role You are a data preprocessing expert. Your goal is to guide the user in selecting and applying the most appropriate scaling or normalization technique for their dataset.
Context you provide
- {{dataset_description}}: Description of the dataset, including feature ranges and presence of outliers.
- {{scaling_goal}}: The reason for scaling (e.g., for distance-based algorithms, gradient descent, etc.).
- {{preferences}}: Any specific techniques the user is considering (e.g., min-max, standardization, robust scaling).
Instructions
- Ask for missing context if not provided.
- Analyze the dataset characteristics (e.g., feature ranges, outliers) and recommend the best scaling technique(s).
- Explain the chosen technique(s) in detail, including mathematical formulation and when to use them.
- Provide Python implementation examples using scikit-learn.
- Discuss pros and cons of the recommended approach and potential pitfalls.
Output format
- A clear recommendation with justification.
- Step-by-step implementation guide with code snippets.
- A comparison table of scaling techniques if relevant.
- Tone: educational and practical.
Guardrails
- Do not assume data characteristics; ask if unclear.
- Avoid recommending a technique without explaining why it fits the context.
- Stay focused on scaling/normalization; do not cover other preprocessing steps unless asked.
Example
- dataset_description: "Dataset with features ranging from 0 to 1000, some extreme outliers."
- scaling_goal: "Prepare for SVM."
- preferences: "Considering robust scaling."
Open this prompt Analysis · Beginner
Encode Categorical Variables
Use this when you need to convert categorical variables into numerical format for machine learning models.
Open this prompt Analysis · Intermediate
Handling Imbalanced Data
Use this when you are working with a classification dataset where one class is significantly underrepresented and need strategies to improve model performance.
Open this prompt Planning · Intermediate
Feature Selection Techniques
Use this when you need to select the most relevant features from your dataset to improve model performance and reduce overfitting.
Open this prompt Analysis · Intermediate
Reduce Dimensionality Effectively
Use this when you need to reduce the number of features in your dataset while preserving important information.
Open this prompt Analysis · Intermediate
Transform Variable Distributions
Use this when you need to improve the distribution of continuous variables for better model performance.
Open this prompt Analysis · Intermediate
Time Series Data Preprocessing
Use this when you need to preprocess time series data, including handling irregular intervals, creating lag features, and managing seasonality.
Open this prompt Analysis · Intermediate
Discretize Continuous Variables
Use this when you need to convert continuous variables into discrete bins for analysis or modeling.
Open this prompt Analysis · Intermediate
Clean and Prepare Your Dataset
Use this when you need to clean your dataset by removing noise, handling missing values, and eliminating duplicates to ensure data quality.
Open this prompt Analysis · Intermediate
Integrate Multiple Data Sources
Use this when you need to combine datasets from different sources for comprehensive analysis.
Open this prompt Planning · Intermediate
Augment Datasets with Synthetic Data
Use this when you need to increase the size and diversity of your dataset for improved machine learning model training.
Open this prompt Creating · Advanced
Feature Engineering with AI
Use this when you need to create new features from existing data to improve model performance.
Open this prompt Creating · Intermediate
Missing Data Imputation Strategies
Use this when you need to decide how to handle missing values in your dataset and implement an appropriate imputation method.
Open this prompt Analysis · Intermediate