1. Regression Analysis
Prepared By:
Batool Alsayyed

2. Machine Learning
Is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

3. Supervised Machine Learning Algorithms
can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.

4. Unsupervised Machine Learning Algorithms
Are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.

5. Regression Analysis

6. Why do we use Regression Analysis?
Regression is a supervised learning technique that helps in finding the correlation between variables and enables us to predict the continuous output variable based on one or more predictor variables. It is mainly used for prediction, forecasting, time series modeling, and determining the causal-effect relationship between variables.
Regression estimates the relationship between the target and the independent variable.
It is used to find the trends in data.
It helps to predict real/continuous values.
By performing the regression, we can confidently determine the most important factor, the least important factor, and how each factor is affecting the other factors.

7. Types of Regression
Various types of regressions are used in data science and machine learning. Each type has its own importance on different scenarios, but all the regression methods analyze the effect of the independent variable on dependent variables at the core.
Linear Regression
Logistic Regression
Polynomial Regression
Support Vector Regression
Decision Tree Regression
Random Forest Regression
Ridge Regression
Lasso Regression

8. Linear Regression
The Pearson correlation measures the degree to which a set of data points form a straight line relationship.
Regression is a statistical procedure that determines the equation for the straight line that best fits a specific set of data.
Any straight line can be represented by an equation of the form Y = bX + a, where b and a are constants.
The value of b is called the slope constant and determines the direction and degree to which the line is tilted.
The value of a is called the Y-intercept and determines the point where the line crosses the Y-axis.

9. Linear Regression
Below is the mathematical equation for Linear regression:
Y= aX+b  

10. Example Of Regression Analysis
Your business wants to forecast your sales for the upcoming summer program in order to plan for your budget and figure out if you need to conduct a second round of hiring for temporary sales reps. In this scenario, the sales team is the dependent variable and your goal is to understand what influences it. So, you compare the sales to an independent variable, like the number of sales calls. Then you collect data for both the total seasonal sales and the total seasonal sales calls for the last five years. Once you set everything up and have the data, you can get even more granular with that information and review the number of sales calls as it impacts the number of sales each year, and then again for each month during the sales season so that you can determine not only how many new sales reps to hire the following year, but for precisely what months you need to ramp up seasonal sales reps. Then, you filter them out as the sales calls, and subsequently, the sales themselves, start to thin out.

11. Example Of Regression Analysis
The goal here, again, is to compare what influences the number of calls had on the number of sales.

12. Example Of Regression Analysis
Example Conclusion:
The regression model equation might be as simple as Y = a + bX in which case the Y is your Sales, the ‘a’ is the intercept and the ‘b’ is the slope. You would need regression software to run an effective analysis. You are trying to find the best fit in order to uncover the relationship between these variables.