Predictive Modeling with Python
- Description
- Curriculum
- FAQ
- Reviews
Predictive Modeling is the use of data and statistics to predict the outcome of the data models. This prediction finds its utility in almost all areas from sports, to TV ratings, corporate earnings, and technological advances. Predictive modeling is also called predictive analytics. With the help of predictive analytics, we can connect data to effective action about the current conditions and future events. Also, we can enable the business to exploit patterns and which are found in historical data to identify potential risks and opportunities before they occur. Python is used for predictive modeling because Python-based frameworks give us results faster and also help in the planning of the next steps based on the results.
Our course ensures that you will be able to think with a predictive mindset and understand well the basics of the techniques used in prediction. Critical thinking is very important to validate models and interpret the results. Hence, our course material emphasizes on hardwiring this similar kind of thinking ability. You will have good knowledge about the predictive modeling in python, linear regression, logistic regression, the fitting model with a sci-kit learn library, the fitting model with stat model library, ROC curves, backward elimination approach, stats model package, etc.
In this course, you will get an introduction to Predictive Modelling with Python. You will be guided through the installation of the required software. Data Pre-processing, which includes Data frame, splitting dataset, feature scaling, etc. You will gain an edge on Linear Regression, Salary Prediction, Logistic Regression. You will get to work on various datasets dealing with Credit Risk and Diabetes.
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20
Introduction to Multiple Linear Regression -
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Creating Dummies
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22
Removing one Dummy and Splitting Dataset
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Training Set and Predictions
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Stats Models to Make Optimal Model
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Steps to Make Optimal Model
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26
Making Optimal Model by Backward Elimination
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Adjusted R Square
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Final Optimal Model Implementation
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29
Introduction to Jupyter Notebook
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30
Understanding Dataset and Problem Statement
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31
Working with Correlation Plots
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Working with Correlation Plots Continue
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33
Correlation Plot and Splitting Dataset
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34
MLR Model with Sklearn and Predictions
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35
MLR model with Statsmodels and Predictions
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36
Getting Optimal model with Backward Elimination Approach
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37
RMSE Calculation and Multicollinearity Theory
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38
VIF Calculation
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39
VIF and Correlation Plots
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40
Introduction to Logistic Regression
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41
Understanding Problem Statement and Splitting
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42
Scaling and Fitting Logistic Regression Model
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Prediction and Introduction to Confusion Matrix
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44
Confusion Matrix Explanation
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45
Checking Model Performance using Confusion Matrix
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46
Plots Understanding
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47
Plots Understanding Continue
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48
Introduction and data Preprocessing
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49
Fitting Model with Sklearn Library
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50
Fitting Model with Statmodel Library
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51
Using Statsmodel Package
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Backward Elimination Approach
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53
Backward Elimination Approach Continue
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54
More on Backward Elimination Approach
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55
Final Model
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56
ROC Curves
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57
Threshold Changing
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58
Final Predictions
