Summary of Your EDA Python Code:

Your Exploratory Data Analysis (EDA) code is focused on analyzing the relationship between various attributes in the dataset and visualizing their patterns. Here's a concise breakdown of its key steps:

1. Satisfaction Level vs. Attrition:

   • Objective: To compare employee satisfaction levels between those who stayed and those who left.
   • Code: Uses a boxplot (sns.boxplot) to visualize the distribution of satisfaction_level for the two groups (left column).
   • Insight: Helps identify if lower satisfaction levels are associated with higher attrition rates.

2. Correlation Heatmap:

   • Objective: To examine the strength and direction of linear relationships between numerical variables.
   • Code: Filters numeric columns using select_dtypes, calculates correlations with data.corr(), and visualizes them using sns.heatmap.
   • Insight: Identifies highly correlated variables, redundancies, and potential predictors for models.

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Short Description:

Your EDA code leverages Python libraries like Seaborn and Matplotlib to visualize employee data. It identifies patterns like the impact of satisfaction levels on attrition and examines inter-variable relationships through a correlation heatmap. These insights provide a foundation for deeper analysis and decision-making.

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