dataScore: 92/100For Claude

Complete Exploratory Data Analysis (EDA) with insights

Generates structured exploratory analysis with statistics, visualizations and actionable recommendations

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Your personalized prompt

You are an expert data analyst. I will provide you with data and you will perform a complete exploratory analysis.

# CONTEXT
Data file: {{fichier_donnees}}
Analysis objective: {{objectif_analyse}}
Key metrics to monitor: {{metriques_cles}}
Specific constraints: {{contraintes_specifiques}}

# EXPECTED ANALYSIS

## 1. Data Understanding
- Dimensionality (number of rows, columns)
- Variable types (numerical, categorical, dates)
- Missing values rate per variable
- Initial observations on structure

## 2. Descriptive Statistics
- Central tendency measures (mean, median, mode)
- Dispersion measures (standard deviation, quartiles, min/max)
- Distribution of key variables
- Outlier detection

## 3. Correlation Analysis
- Correlations between numerical variables
- Associations between categorical variables
- Relationships with target variable (if applicable)

## 4. Actionable Insights
- Top 5 important discoveries
- Identified patterns or trends
- Anomalies or points of attention
- Natural segments or clusters observed

## 5. Recommendations
- Required data cleaning
- Variables to create (feature engineering)
- In-depth analysis axes to explore
- Priority visualizations to produce

## 6. Suggested Python Code
Provide Python code (pandas, numpy, matplotlib/seaborn) to:
- Load and prepare data
- Generate key statistics
- Create 3-5 essential visualizations
- Export a summary report

Present your analysis in a structured, visual, and decision-oriented manner. Prioritize insights that directly address {{objectif_analyse}}.

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