Banking Customer Churn Prediction Project | Machine Learning with Python in Jupyter Notebook
Banking Customer Churn Prediction Project | Machine Learning with Python in Jupyter Notebook
Welcome to this hands-on machine learning project tutorial! In this video, we will build a Banking Customer Churn Prediction Model using Python in Jupyter Notebook. This is a complete end-to-end machine learning project, guiding you through real-world data analysis, preprocessing, model building, and evaluation.
📌 What You’ll Learn:
What is customer churn and why it matters in the banking sector
Data exploration and visualization techniques
Feature engineering and preprocessing best practices
How to apply Label Encoding & One-Hot Encoding
Splitting data into training and testing sets
Implementing ML models like Logistic Regression, Random Forest, and more
Evaluating models using accuracy, precision, recall, and F1-score
Making predictions and deriving actionable insights
📁 Tools & Libraries Used:
Python
Pandas
NumPy
Matplotlib & Seaborn
Scikit-learn (sklearn)
Jupyter Notebook
🎯 Whether you're preparing for interviews, building portfolio projects, or diving deeper into applied machine learning — this video will help you understand how to solve real-world problems using machine learning techniques.
👍 Don’t forget to Like, Share & Subscribe for more practical ML projects and tutorials!
#MachineLearning #CustomerChurnPrediction #MLProject #BankingAnalytics #PythonForDataScience #JupyterNotebook #DataScienceProjects #PythonMachineLearning #ChurnPrediction #RandomForest #LogisticRegression #RealWorldML #PythonProjects
Banking Customer Churn Prediction Project | Machine Learning with Python in Jupyter Notebook
Welcome to this hands-on machine learning project tutorial! In this video, we will build a Banking Customer Churn Prediction Model using Python in Jupyter Notebook. This is a complete end-to-end machine learning project, guiding you through real-world data analysis, preprocessing, model building, and evaluation.
📌 What You’ll Learn:
What is customer churn and why it matters in the banking sector
Data exploration and visualization techniques
Feature engineering and preprocessing best practices
How to apply Label Encoding & One-Hot Encoding
Splitting data into training and testing sets
Implementing ML models like Logistic Regression, Random Forest, and more
Evaluating models using accuracy, precision, recall, and F1-score
Making predictions and deriving actionable insights
📁 Tools & Libraries Used:
Python
Pandas
NumPy
Matplotlib & Seaborn
Scikit-learn (sklearn)
Jupyter Notebook
🎯 Whether you’re preparing for interviews, building portfolio projects, or diving deeper into applied machine learning — this video will help you understand how to solve real-world problems using machine learning techniques.
👍 Don’t forget to Like, Share & Subscribe for more practical ML projects and tutorials!
#MachineLearning #CustomerChurnPrediction #MLProject #BankingAnalytics #PythonForDataScience #JupyterNotebook #DataScienceProjects #PythonMachineLearning #ChurnPrediction #RandomForest #LogisticRegression #RealWorldML #PythonProjects
