Mastering Data Analysis with Decision Trees: Theory and Practice

Synopsis

Description

Introduction: The course begins with an overview of the project's objectives and scope, setting the stage for participants to understand the importance of data analysis with decision trees in predictive modeling.

Project Steps and Files: Participants will be guided through the essential steps of the project, from importing files to data preprocessing and exploratory data analysis (EDA). They will learn how to handle data preprocessing tasks effectively and gain insights from EDA to inform subsequent analysis.

Data Preprocessing EDA: This section delves into the critical steps of data preprocessing and exploratory data analysis (EDA). Through a series of comprehensive tutorials, participants will learn techniques for cleaning, transforming, and visualizing data to uncover patterns and insights.

Hyper Parameter Tuning: Participants will explore hyperparameter tuning techniques to optimize the performance of decision tree models. They will learn how to fine-tune model parameters effectively to improve model accuracy and generalization.

Decision Tree: In this section, participants will gain a deep understanding of decision tree theory and implementation steps. They will learn how to install necessary libraries such as Graphviz and Pydotplus and interpret decision tree models for insightful analysis.

Throughout the course, participants will gain practical skills in data analysis with decision trees, from data preprocessing and exploratory analysis to hyperparameter tuning and decision tree implementation. By the end of the course, participants will be equipped with the knowledge and tools to apply decision tree analysis confidently to real-world datasets for predictive modeling and decision-making.

There are different types of statistical, data mining and machine learning algorithms in Predictive Modeling. Each algorithm is used to address the specific needs of the business concern. So choosing the right algorithm for your business is a great task. Regression algorithm is one among them. Regression algorithm is used to forecast continuous data like credit scoring or predicting the next outcome of a time based event. For example regression algorithm can be used to predict the trend of a stock movement with its past prices.

Regression is a statistical method which helps to determine the relationship between one dependent variable and other independent variables. It explains how the dependent variable changes when one of the independent variable varies. It is also used to know which independent variable is related to the dependent variable and what is their relationship. Regression analysis is widely used in the field of prediction and forecasting. Regression analysis is an important component for modelling and analyzing data.

In the recent years many techniques have been developed to perform regression analysis. They are Linear regression, Logistic regression, Polynomial regression, Stepwise regression, Ridge regression, Lasso Regression and Elastic net regression.

Logistic regression is also known as logit regression or logit model. This is used to find the probability of event success and event failure. Logistic regression determines the relationship between categorical dependent variable and one or more independent variables using a logistic function.

Logistic regression is used for predicting the probability of occurrence of an event by fitting the data to a logistic curve. Ordinary Least Squares on the other hand is an important computational problem that is used in applications when there is a need to use a linear mathematical model to measurements which are derived from the experiments. OLS takes various forms like Correlation, multiple regression, ANOVA and others. Logistic regression is most widely used in the field of medical science whereas OLS is mostly used in social sciences.