Predictive Modeling

Predictive Modeling is a statistical technique using machine learning and data mining to forecast future outcomes based on historical data. Coursera's Predictive Modeling catalogue helps you understand, design, and build predictive models to solve real-world business problems. You'll learn a variety of machine learning algorithms, techniques for handling missing data, steps to evaluate and validate models, and how to leverage these models in decision making. Gain proficiency in tools and libraries such as Python's Scikit-Learn and Pandas, R's Caret, and various data visualization tools, equipping you with skills widely applicable in fields like finance, healthcare, marketing, and more.

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Results for "Predictive Modeling"

  • Skills you'll gain: Regression Analysis, Statistical Hypothesis Testing, Logistic Regression, Statistical Analysis, Data Analysis, Correlation Analysis, Advanced Analytics, Predictive Modeling, Statistical Modeling, Machine Learning, Model Evaluation, Variance Analysis, Python Programming

  • From the course: The Nuts and Bolts of Machine Learning·Lesson: Boosting

  • From the course: The Nuts and Bolts of Machine Learning·Lesson: Review: Tree-based modeling

  • From the course: The Nuts and Bolts of Machine Learning·Lesson: End-of-course portfolio project wrap-up

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    From the course: The Nuts and Bolts of Machine Learning

  • From the course: The Nuts and Bolts of Machine Learning·Lesson: Boosting

  • From the course: The Nuts and Bolts of Machine Learning·Lesson: Additional supervised learning techniques

  • From the course: Data Analysis with R Programming·Lesson: Take a closer look at the data

  • From the course: Regression Analysis: Simplify Complex Data Relationships·Lesson: Understand multiple linear regression

  • Skills you'll gain: Scientific Visualization, Data Preprocessing, Regression Analysis, Scikit Learn (Machine Learning Library), Feature Engineering, Data Cleansing, Predictive Modeling, Data Analysis, Statistical Modeling, Model Training, Statistical Methods, Supervised Learning, Model Evaluation, Machine Learning, Python Programming