Скачать или смотреть Data Science Portfolio Project - Churn prediction model

In this video tutorial we will do a machine learning project. We will build churn prediction model with Python using bank data. We will apply several algorithms (Random Forest, Logistic Regression, Support Vector Machines, KNN, Gradient Boosting Classifier) and compare their results.

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Data used in the video - https://www.kaggle.com/datasets/shrut...
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Timestamps:
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00:00 Intro
00:20 What is churn model, applications
03:22 import libraries
04:45 About data
06:35 .info
07:03 null and duplicates
08:12 Label encoding, one-hot encoding
09:25 Feature selection
11:02 Split data into testing and training
12:00 Feature scaling
14:42 RandomForest
16:48 Making predictions
17:01 Confusion matrix, accuracy, classification report
17:33 What is confusion matrix
18:37 How to read classification report
20:00 Accuracy score
20:22 Feature importances
22:58 Logistic regression
24:35 Support Vector Machines
25:46 KNN
27:01 Gradient Boosting Classifier
27:52 How to select an algorithm
29:30 Feature engineering

🎥 Other videos you might be interested in
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   • Data Science Portfolio Project - K-means  
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About me
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Hi, my name is Karina and I'm a finance person turned data person.

My mission is to transform intimidating tech into accessible tools. I aim to empower 1 million people to harness the power of AI, Python, SQL, and Excel to work smarter, not harder.

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