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Learning Predictive Analytics with Python
Rating: 4.3 out of 5(4 ratings)
23 students

Learning Predictive Analytics with Python

A perfect course for Python learners
Last updated 3/2023
English

What you'll learn

  • Predictive Analytics Theory and Practical Application
  • Python with Object Oriented Programming
  • Decision Tree and Random Forest Algorithm
  • Introduction to libraries like sklearn, pandas and numpy

Course content

3 sections31 lectures8h 46m total length
  • Introduction5:45

    Explore how analytics turns data into insights, from descriptive analytics describing past patterns to predictive analytics using models like decision trees and random forests, guiding prescriptive actions with Python.

  • Predictive Analytics in brief7:25

    Predictive analytics uses historical data, data mining, and statistics with algorithms like decision trees, random forest regression, and neural networks to train on labeled data, forecast sales, and optimize inventory.

  • Key terms of Predictive Analytics7:34
  • Decision Tree Algorithm With Example25:44

    Learn the decision tree algorithm for classification and regression, with a practical example. Build trees from root to leaf nodes and evaluate splits using entropy and information gain.

  • Random Forest Algorithm6:28

    Explore the random forest algorithm, an ensemble of diverse decision trees built via bootstrapping and random feature selection to improve classification and regression while reducing overfitting.

Requirements

  • Very minimal understanding of Python
  • Beginner level understanding of libraries like numpy and pandas

Description

Hi all,


Predictive analytics is a branch of advanced analytics that uses statistical techniques, machine learning algorithms, and data mining to analyze historical data and make predictions about future events or trends. It helps organizations to identify patterns, understand trends, and anticipate outcomes, which can lead to better decision-making and more efficient operations. Predictive analytics is used in a variety of industries, including finance, healthcare, marketing, and manufacturing. By using predictive analytics, organizations can gain insights into customer behaviour, optimize marketing campaigns, detect fraud, and improve supply chain management, among other benefits.


In this course, we will be covering all the important basics of Predictive analytics and then we will start with all the important concepts about Python and Object Oriented Programming.


Python is a general-purpose language used in major areas like data science, machine learning and web development.

This course covers almost all the essential concepts of Python. As a beginner, it is essential to have a basic clear and thorough understanding of the concepts. This course focuses on these points and we have tried our best to deliver it in a way which will help you to get the basics right as well as concepts clear.

The key concepts covered in this course are:

1. Built-in data types like list, dictionary, tuple, set, string with their functionalities.

2. List comprehension, lambda function and decorators in Python

3. Object-oriented programming in Python

4. Creating an application using all the learned concepts

5. Predictive Analytics

6. A predictive model using Random Forest Algorithm to predict a winner between two given pokemon.


Does this course cover all the concepts of Data Science and Web development?

I will be honest with you, the course does not cover all the concepts of Web Development (although we have different courses for that we will discuss it some other day), however, it does cover about basics of Data Science and Machine Learning.

Who this course is for:

  • Beginner Python developers curios about Machine Learning and Data Analytics
  • Those who want to have good basic understanding of how ML is done with Python.