WebDec 28, 2024 · Produces this plot. The plot shows customer counts of over 5000 No-Churn and close to 2000 Yes-Churn. There are 18 categorical features in the dataset. So, we can make two sets of a 3×3 count plots for each categorical feature. Below is a code for a 3×3 count plot visualization for the first set of nine categorical features. WebMar 11, 2024 · This repository contains analysis of churn in telephone service company (using IV and WOE), comparison of effect size and information value and quick tutorial how to use information value module (created for this analysis). ... (ANN), with TensorFlow and Keras in Python. This is a customer churn analysis that contains training, testing, and ...
nsikan-udoma/customer_churn_analysis-SQL-Python-Tableau
WebCustomer Churn Analysis Python · Churn in Telecom's dataset. Customer Churn Analysis. Notebook. Input. Output. Logs. Comments (13) Run. 32.3s. history Version 1 … WebJan 27, 2024 · No 5174 Yes 1869 Name: Churn, dtype: int64. Inference: From the above analysis we can conclude that. In the above output, we can see that our dataset is not balanced at all i.e. Yes is 27 around and No is 73 around. So we analyze the data with other features while taking the target values separately to get some insights. how many house seats are up for grabs in 2022
customer-churn-prediction · GitHub Topics · GitHub
WebJan 14, 2024 · We’ve performed exploratory data analysis to understand which variables affect churn. We saw that churned customers are likely to be charged more and often have a month-to-month contract. We’ve gone from the raw data that had some wrongly encoded variables, some missing values, and a lot of categorical data, to a clean and correctly … WebCustomer Personality Analysis and Churn. This is a quickly whipped up, well structured project using a Customer Personality dataset.; I have conducted a quite in-depth feature extraction (as outlined in feature_extraction.ipynb).; Models were tinkered with in train.ipynb.; Execute main_train.py using python main_train.py.; Currently implemented … WebJan 10, 2024 · Data Predicting Customer Churn Using Python. The above Pie chart shows the distribution of the target variable (Exited); There are more retained customers than churn, 79.6% of customers stayed , while 20.4% churned. The bar chart shows customers by Geography; France has the most customers, followed by Spain with a small difference … how many house seats are in congress