Exploratory Data Analysis (EDA) using Jupyter
Budget: $30 – $250 USD
Perform an explanatory data analysis (EDA) on Customer Churn data within the Telecommunication
industry. Although there will be no need to build a model based on the data provided, you are asked to
look for issues in the data and find correlations among the various variables in order to improve/lower
customer churn predictions
Investigating the data should be done two-fold:
1. Manually by utilizing the classic (legacy) EDA libraries: NumPy, Pandas, graph libraries
(MatPlotlib, Seaborn, Plotly), and Python’s Statsmodel modules.
2. Generate ‘html’ reports by integrating Pandas Profiling and SweetViz Python libraries.
industry. Although there will be no need to build a model based on the data provided, you are asked to
look for issues in the data and find correlations among the various variables in order to improve/lower
customer churn predictions
Investigating the data should be done two-fold:
1. Manually by utilizing the classic (legacy) EDA libraries: NumPy, Pandas, graph libraries
(MatPlotlib, Seaborn, Plotly), and Python’s Statsmodel modules.
2. Generate ‘html’ reports by integrating Pandas Profiling and SweetViz Python libraries.