It is one of the most widely known modeling techniques. Simple linear regression is useful for finding relationships between two continuous variables. One is a predictor or independent variable and the other is a response or dependent variable. It looks for a statistical relationship but not a deterministic relationship.

Linear Regression establishes a relationship between the **dependent variable (Y) and one or more independent variables (X)** using a best fit straight line (also known as a regression line).

It is represented by an **equation Y=a+b*X + e**, where a is the intercept, b is the slope of the line and…

Regression analysis is a form of predictive modeling technique which investigates the relationship between a target and predictor. This technique is used for forecasting, time series modeling, and finding the causal effect relationship between the variables.

**Why do we use Regression Analysis?**

There are multiple benefits of using regression analysis. They are as follows:

*It indicates the significant relationships between a**dependent variable and the independent variable.**It indicates the strength of the impact of**multiple independent variables on a dependent variable.*

**How many types of regression techniques do we have?**

We will cover 7 different regression types in this…

Evaluating the Model is considered a core part of building an effective machine learning model. Evaluating the model allows us to ask important questions like

- How well is my model doing? Is it a useful model?
- Will training my model on more data improve its performance?
- Do I need to include more features?

Evaluation metrics explain the performance of a model. An important aspect of evaluation metrics is their capability to discriminate among model results. Evaluating the Model helps in improving the performance of the model by checking the accuracy of your model before computing the predicted value.

They are…

The most important part of a machine learning project is preparing the data. Data preparation is the process of cleaning and transforming raw data before processing and analysis. It is an important step before processing and often involves reformatting data, making corrections to data, and combining data sets to enrich data.

Data preparation is often a lengthy process for data professionals. Data professionals spend most of the time preparing the data by cleaning and transforming raw data before processing and analysis. …

Exploratory Data Analysis (EDA) is an approach for data analysis that employs a variety of techniques (mostly graphical) to

- maximize insight into a data set;
- uncover underlying structure;
- extract important variables;
- detect outliers and anomalies;
- test underlying assumptions;
- develop parsimonious models; and
- determine optimal factor settings.

As a Machine Learning engineer, one of the first steps implemented as part of a machine learning project is Exploratory Data Analysis.

Exploratory Data Analysis refers to the critical process of performing initial investigations on data so as to discover patterns, spot anomalies, test hypotheses, and check assumptions with the help of summary statistics…

Chronic diseases are a tremendous burden to both patients and the health care system. In 2014, 60% of adult Indians had at least one chronic disease or condition, and 42% had multiple illnesses. Chronic diseases, including heart disease, cancer, chronic lung disease, stroke, Alzheimer’s disease, diabetes, osteoarthritis, and chronic kidney disease, are the leading causes of poor health, long-term disability, and death in the United States. One-third of all deaths in this country are attributable to heart disease or stroke, and every year, more than 1.7 million people receive a diagnosis of cancer. During the past several decades, the prevalence…

Machine Learning Engineer