Exploratory Data Analysis of a telecom dataset for customer churn

Objective:

1. Pie chart and
2. bar plot
3. Histogram
4. kde (Kernel Density Estimation)
5. ecdf (empirical cumulative distribution function)
6. scatter plot
7. Regression plot
8. Pair plot
9. Line plot
10. box plot
11. violin plot
12. Heatmap

Libraries: Numpy, Pandas, matplotlib, and seaborn

Lecture 01 objectives: Pie chart and bar plot

1. how to read a csv file

2. understand the telecom dataset

3. is it a good data or bad data?

4. how to look inside the data?

5. how to check for missing valaues or any discrepencies in the data?

6. data visualization: Pie chart and bar plot

7. How many customers have churned?

8. How many customers have international plan?

9. How many customers have voicemail plan?

Lecture 02 objectives: Histogram, distribution, ecdf using matplotlib and seaborn

10. How many customers have international plan as well as churned?
11. How many customers have voicemail plan as well as churned?
12. How to find the distribution of a feature?
13. How to change the color of a figure?
14. How to plot multiple figures in a single image?

Lecture 03 objectives: scatter plot, Regression plot, Pair plot, Line plot

15. Is there any correlation between the total number of calls of a customer and the total number of minutes?
16. Is there any correlation between the total number of calls of a customer and the total bill?
17. Is there any correlation between the total number of minutes of a customer has talked and the total bill?
18. Why customers are churning?

Lecture 04 objectives: box plot, violin plot, Heatmap

19. How to find the statistical measures from the dataset?
20. How to check the correlations between each feature within a single figure?