Data Science Projects with Python
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-
Free ChapterData Exploration and Cleaning
- Course Overview
- Installation and Setup
- Lesson Overview
- Python and the Anaconda Package Management System
- Different Types of Data Science Problems
- Loading the Case Study Data with Jupyter and pandas
- Getting Familiar with Data and Performing Data Cleaning
- Boolean Masks
- Data Quality Assurance and Exploration
- Deep Dive: Categorical Features
- Exploring the Financial History Features in the Dataset
- Lesson Summary
-
Introduction to Scikit-Learn and Model Evaluation
- Lesson Overview
- Exploring the Response Variable and Concluding the Initial Exploration
- Introduction to Scikit-Learn
- Model Performance Metrics for Binary Classification
- True Positive Rate, False Positive Rate, and Confusion Matrix
- Obtaining Predicted Probabilities from a Trained Logistic Regression Model
- Lesson Summary
-
Details of Logistic Regression and Feature Exploration
-
The Bias-Variance Trade-off
- Lesson Overview
- Estimating the Coefficients and Intercepts of Logistic Regression
- Assumptions of Logistic Regression
- How Many Features Should You Include?
- Lasso (L1) and Ridge (L2) Regularization
- Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters
- Reducing Overfitting on the Synthetic Data Classification Problem
- Options for Logistic Regression in Scikit-Learn
- Lesson Summary
-
Decision Trees and Random Forests
-
Imputation of Missing Data, Financial Analysis, and Delivery to Client
- Lesson Overview
- Review of Modeling Results
- Dealing with Missing Data: Imputation Strategies
- Cleaning the Dataset
- Mode and Random Imputation of PAY_1
- A Predictive Model for PAY_1
- Using the Imputation Model and Comparing it to Other Methods
- Financial Analysis
- Final Thoughts on Delivering the Predictive Model to the Client
- Lesson Summary
About this
video
Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You’ll discover how to tune the algorithms to provide the best predictions on new and, unseen data. As you delve into later chapters, you’ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions.
The codes for this course can be downloaded from https://github.com/TrainingByPackt/Data-Science-Projects-with-Python-eLearning.
- Publication date:
- June 2019
- Publisher
- Packt
- Duration
- 6 hours 7 minutes
- ISBN
- 9781838986063
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