Master Data Prep & Descriptive Stats: Outliers, Missing Values, + More! | PD Model Dev - 1
In this video, we dive deep into the crucial aspects of data preparation for predictive modeling. Learn how to master the art of handling descriptive statistics, detecting and treating outliers, dealing with missing values, and more!
📊 Key Topics Covered:
Understanding Descriptive Statistics
Identifying and Treating Outliers
Strategies for Handling Missing Values
Essential Data Manipulation Techniques
🌐 Series Overview:
This video is the first instalment in our comprehensive PD Model Development Series. Whether you're a beginner or an experienced data scientist, this series will equip you with the skills needed to build robust predictive models.
🎓 Who is This For?
Data Scientists and Analysts
Machine Learning Enthusiasts
Financial Analysts and Risk Managers
Anyone Interested in Predictive Modelling
👍 Engage With Us:
If you find this content valuable, don't forget to like, share, and subscribe for more in-depth tutorials on predictive modelling and data science. Share your thoughts and questions in the comments below – we love hearing from our community!
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Resources -
Developed script in the video - https://github.com/LEARNEREA/Data_Sci...
Script to understand the diff. in scores - https://github.com/LEARNEREA/Data_Sci...
Automated Pre-processing - https://github.com/LEARNEREA/Data_Sci...
Logistic saved model - https://github.com/LEARNEREA/Data_Sci...
Random Forest saved model - https://github.com/LEARNEREA/Data_Sci...
XGB saved model - https://github.com/LEARNEREA/Data_Sci...
Raw data utilised in the development - https://github.com/LEARNEREA/Data_Sci...
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Ready to elevate your data science skills? Watch now and embark on a journey to become a PD modelling expert!
#DataScience #PredictiveModeling #DataPreparation #PDModelDevelopment #DataAnalytics #MachineLearning
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