The main objective of this project is to predict which customers are likely to churn. By analyzing customer data, we aim to identify the attributes that contribute to customer churn.
Dataset of telecom customers for predicting customer churn. The dataset is available on Kaggle(https://www.kaggle.com/datasets/blastchar/telco-customer-churn) and comprises data on 5,986 customers.
Each row in the dataset represents a customer, and each column contains customer attributes described in the column metadata. The dataset provides information about:
Customers who left within the last month (Churn column) Services signed up by each customer, including phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies Customer account information, such as tenure, contract type, payment method, paperless billing, monthly charges, and total charges Demographic information about customers, including gender, age range, and whether they have partners and dependents Inspiration The dataset offers an opportunity to explore predictive modeling for customer churn. By analyzing this data, you can gain insights into building models that predict customer behavior and contribute to customer retention strategies.
- Auto ML - PyCaret
- Machine Learning (Supervised / Unsupervised Learning)
- Data Visualization
- Predictive Modeling
- Python
- Pandas, Numpy, Scipy
- Sklearn,