| Files | Description |
|---|---|
| Employee salary analysis of San Francisco.ipynb | This is our all EDA as well as insights |
1.Load the data: Download the San Francisco data from Kaggle and create a DataFrame in Jupyter Notebook using the Pandas library. Use methods such as head(), info(), and describe() to explore the data's shape, data types, and summary statistics.
2.Clean and preprocess the data: Handle missing or noisy values, change the data types of columns, and drop irrelevant or redundant columns in order to prepare the data for analysis. Use Pandas methods such as dropna(), fillna(), astype(), rename(), and drop() to manipulate the data.
3.Analyze the data: Perform exploratory data analysis using statistical methods to identify patterns or relationships in the data. Use libraries such as Matplotlib, Seaborn, and Plotly for data visualization, and NumPy and SciPy for statistical analysis. Calculate summary statistics, create visualizations, and identify trends or patterns in the data.
4.Extract insights: Draw conclusions and insights from the data based on the results of your analysis.
The top 10 average base pay for different job titles was determined and analyzed. The results show the job titles with the highest average base pay, which can provide valuable insights into the compensation trends for various positions.
