What you'll get
- 3+ Hours
- 1 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Key stages involved in preparing data and conducting effective exploratory analysis.
- Methods for importing, organizing, and managing files to ensure clean, analysis-ready datasets.
- Exploratory Data Analysis (EDA) approaches are used to identify trends, relationships, and meaningful patterns.
- Techniques for dividing datasets and assessing model accuracy with tools such as confusion matrices and ROC curves.
- Approaches to fine-tuning hyperparameters to enhance the performance of decision tree models.
- Foundational concepts and practical implementation steps behind decision tree algorithms for prediction tasks.
- Guidance on installing and working with essential libraries, including Graphviz and Pydotplus.
- Best practices for interpreting decision tree outputs to support data-driven decisions.
- Application of decision tree methods to real-world datasets for hands-on predictive modeling.
- Development of strong analytical skills to use decision trees for informed decision-making and extracting actionable insights.
- Ability to interpret logistic regression results generated in Python.
- Skills to evaluate model outcomes and communicate findings effectively to stakeholders.
- Understanding of the fundamental components that make up logistic regression.
- Clear explanation of logistic regression principles and their practical advantages.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Credit-Default using Logistic Regression | 3h 3m | ✔ | View Curriculum |
Description
The course begins with a clear overview of the project's purpose, explaining how decision trees contribute to effective predictive modeling and data-driven decision-making. Participants gain an understanding of where decision trees fit within the broader data science workflow and why mastering these techniques is essential for building reliable, interpretable models.
Project Steps and Files
Participants follow a clear workflow that includes importing datasets, preparing files, and performing initial exploratory analysis to support informed modeling.
Data Preprocessing EDA
This part focuses on the essential steps of cleaning, transforming, and visualizing data. Learners quickly identify trends and insights through practical EDA methods.
Hyper Parameter Tuning
Participants explore simple yet effective tuning techniques to improve decision tree accuracy, enabling models to perform better on new data.
Decision Tree
This section explains decision tree concepts and guides learners through implementation using libraries like Graphviz and Pydotplus. They also learn to interpret tree structures to generate useful insights.
Predictive Modeling Overview
Predictive modeling involves selecting the appropriate statistical or machine learning algorithm to meet business needs and ensure reliable, relevant outcomes.
Regression Basics
Regression methods help forecast continuous outcomes and reveal how independent variables influence a target result, making them essential for forecasting and trend analysis.
Types of Regression Methods
Learners gain a quick introduction to key regression approaches, including linear, logistic, ridge, lasso, and elastic net, each suited to different modeling objectives.
Logistic Regression
Logistic regression helps predict the likelihood of an event and is commonly used for classification by modeling outcomes using a logistic curve.
Logistic Regression vs OLS
Logistic regression supports probability-based classification, while OLS is used for linear prediction and is commonly applied in fields like social science research.
Requirements
- Basic knowledge of Python programming, including data types, loops, and functions.
- Understanding of fundamental statistical concepts, including descriptive statistics, probability, and hypothesis testing.
- Familiarity with datasets and simple calculations is a plus.
- Willingness to apply analytical concepts to real-world problems.
Target Audience
- Individuals seeking to build foundational knowledge in data analysis and analytics.
- Professionals aiming to enhance decision-making skills using data-driven insights.
- Students or early-career learners interested in exploring data science concepts.
- Anyone looking to apply analytical techniques to solve real-world problems.
