Predicting Bank Customer Churn with Machine Learning in Python | Streamlit App + Power BI Dashboard скачать в хорошем качестве

Predicting Bank Customer Churn with Machine Learning in Python | Streamlit App + Power BI Dashboard

In this video, we take you step-by-step through the process of using Python and Machine Learning to predict bank customer churn. 🚀💳 Here’s what you’ll learn: 1️⃣ Churn Prediction Model: • Built a machine learning model to predict whether a customer will churn and their probability of churning. • Identified the most important factors driving churn using feature importance analysis. 2️⃣ Streamlit App Deployment: • Created an interactive Streamlit app where users can input customer data to see: 🔹 If the customer is likely to churn. 🔹 The probability of churn. 3️⃣ Power BI Dashboard: • Designed a detailed Power BI dashboard showcasing: 🔹 Insights from the model and analysis. 🔹 Predictions for existing customers. 🔹 Key trends and metrics for better decision-making. Steps We Followed: • Loaded raw data from Kaggle and formulated key questions. • Preprocessed the data (cleaning, deduplication, scaling, and transformation). • Visualized distributions and outliers to understand the data. • Ran feature importance using Decision Trees and XGBoost Trees to identify the key drivers of churn. • Developed and evaluated the model using an XGBoost classifier and a confusion matrix. • Tuned the model for optimal performance with hyperparameter optimization. • Saved the model and transformer using pickle and exported results to Excel for further analysis. • Deployed the model in Streamlit and built a Power BI dashboard to present findings. This project combines data science, machine learning, app development, and dashboard creation for a complete end-to-end solution to tackle customer churn. 👉 Watch now to see how we built it step by step and learn how to create similar solutions for your own projects! #CustomerChurn #MachineLearning #Python #Streamlit #PowerBI #XGBoost #datascience 🔗 Chapters: 00:00 – Intro 02:11 – ML Process 03:26 – Raw Data Load 04:02 – Data Preprocessing 05:32 – Data Visualizations 11:08 – Running XGBoost/ML 14:04 – Oversampling Minority Class 16:10 – HyperParameter Tuning 18:48 – Storing the model & Results Python Part 1 video:    • Predicting Bank Customer Churn with Machin...   Streamlit Part 2:    • Streamlit App for Predicting Customer Chur...   Power BI Part 3:    • Power BI Dashboard for Customer Churn Anal...   Github Link: https://github.com/Pitsillides91/pyth... Connect with me on LinkedIn:   / yiannis-pitsillides-8b103271   Follow me on X: https://x.com/pitsillides91