Скачать или смотреть End To End Data Science Project with Machine Learning Pipelines Implementation & Complete Roadmap

Github: https://github.com/everyday-data-scie...

Uber Data Analytics Project:    • Uber Data Science Take Home Assignment | C...  

Chapters:

0:00 - Trailer
1:51 - Introduction
2:25 - Contents
5:35 - Libraries Required
6:11 - Load the dataset
7:28 - What is Churn
8:11 - Column Definitions
8:49 - Initial Inspection
17:35 - Summary of Initial Inspections
19:04 - Data Preprocessing
21:38 - EDA
37:56 - Feature Engineering
59:30 - Feature Selection
1:04:33 - Balance Class
1:09:40 - Precision, Recall, F1 score, ROC AUC
1:15:44 - Model Training & Evaluation
1:22:59 - Hyperparameter Tuning
1:29:37 - ML Pipeline Development
1:37:01 - Make Predictions on New Data

In this comprehensive tutorial, I’ll walk you through an entire data science project from start to finish, showing you how to build and deploy a machine learning model capable of making predictions on new data. We’ll start by defining the problem and gathering data, then move step-by-step through the data preparation and feature engineering process. You’ll see how to train different machine learning models, evaluate their performance, and fine-tune them for better accuracy. Finally, I’ll show you how to automate the entire process by building a robust data pipeline and demonstrate how to make predictions on new data.

This video is perfect for anyone looking to understand the full data science workflow, whether you're just getting started or you're looking to sharpen your skills by seeing how an end-to-end project comes together. If you're interested in improving your machine learning, data analysis, and model deployment skills, this video is for you!

Here’s a detailed breakdown of the steps covered in this project:

1. Problem Definition
We start by understanding the problem we're trying to solve and how machine learning can help provide a solution.

2. Data Collection
Next, we gather the relevant dataset that will form the foundation for our model. This could be from external sources, company databases, or web scraping.

3. Data Cleaning and Preprocessing
Before diving into model building, we clean the data—handling missing values, correcting data types, and dealing with any inconsistencies.

4. Exploratory Data Analysis (EDA)
Here, we explore the data using visualizations and statistical methods to uncover important patterns, relationships, and trends.

5. Feature Engineering
We transform raw data into features that better represent the underlying problem to the model. This step includes scaling, encoding categorical variables, and feature selection.

6. Model Selection
We choose a variety of machine learning models to try, such as Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, and more.

7. Model Training
We train the selected models on our training data and validate their performance using cross-validation techniques.

8. Model Evaluation
We evaluate the models using metrics like accuracy, precision, recall, F1-score, and AUC-ROC to determine which model performs best.

9. Hyperparameter Tuning
Once we have a baseline model, we optimize it by adjusting hyperparameters to improve its performance and avoid overfitting.

10. Pipeline Creation for Automation
We’ll create a machine learning pipeline to automate the data preprocessing and model training process for efficiency and scalability.

11. Model Deployment
In this step, we deploy the final model into production, making it available to generate real-time predictions.

12. Making Predictions on New Data
We test the deployed model by feeding it new, unseen data and using it to make predictions.

13. Monitoring and Maintenance
Finally, we’ll discuss how to monitor the performance of the deployed model over time, ensuring it continues to make accurate predictions as new data becomes available.

By the end of this video, you’ll have a clear understanding of how to manage a data science project from end to end and how to deploy a machine learning model to make real-time predictions.

Hashtags:
#EndtoEndDataScience #DataScienceTutorial #MachineLearningPipeline

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