At the end of this Course, A student will be able to use Predictive analytics ( Decision tree , neural network or Logistic regression) to predict future outcomes. Some areas of application are the following: Actuarial Science, marketing, financial services, insurance, mobility, pharmaceuticals, healthcare, just to name a few
1 section • 12 lectures • 1h 0m total length
Missing values detection and treatment
Training and Testing set
Min Max normalization
The only prerequisite for this class is the willingness to learn and some basic knowledge of R but not necessary
In this course, we cover two analytics techniques: Descriptive statistics and Predictive analytics. For the predictive analytic, our main focus is the implementation of a logistic regression model a Decision tree and neural network. We well also see how to interpret our result, compute the prediction accuracy rate, then construct a confusion matrix .
By the end of this course , you will be able to effectively summarize your data , visualize your data , detect and eliminate missing values, predict futures outcomes using analytical techniques described above , construct a confusion matrix, import and export a data.
Who this course is for:
Anyone seeking a career as data scientist, data analyst , finance analyst, statistician , actuary, just to name few
Phd Student in Computer Science, My education Include a BS in Mathematics and a MS in Mathematical Statistics. I invest a lot of time on learning and teaching. Covering a wide range of topics in Mathematics, Statistics and Computer Science , Some of my main interests include machine learning, data reduction techniques, Statistical Computing, regression analysis and a wide range of mathematical Statistics topics including parameter estimate.