Logistic Regression was used in the biological sciences in early twentieth century. It was then used in many social science applications. Logistic Regression is used when the dependent variable(target) is categorical. For example, To predict whether an email is spam (1) or (0) Whether the tumor is malignant (1) or…

Vectorized Implementation Gradient descent algorithm is one of the most popular optimization algorithms for finding optimal parameters for the model. Goal is to find the parameter which minimize the cost function. (Full Source Code: https://github.com/SSaishruthi/Gradient_Descent_Algorithm_Using_Numpy) Process Local gradient of the error function with respect to the parameter is measured and…

R-square value gives the measure of how much variance is explained by model. For a given set of points, the default regression line with minimum sum of square is the horizontal line that passes through the mean. This horizontal regression line denotes that there is no information that can be…

Linear regression is used for finding linear relationship between target and one or more predictors. There are two types of linear regression- Simple and Multiple. Simple Linear Regression Simple linear regression is useful for finding relationship between two continuous variables. One is predictor or independent variable and other is response or dependent variable…

Full code in https://github.com/SSaishruthi/SVM-using-Numpy Support Vector Machine is used for finding an optimal hyperplane that maximizes margin between classes. SVM’s are most commonly used for classification problem. They can also be used for regression, outlier detection and clustering. SVM works great for a small data sets. There are two classes…

Full code in https://github.com/SSaishruthi/Math_behind_Gradient_Descent There are many ways to frame the learning process. Easiest way is to use labelled data. For a given input, it is necessary to learn how these are mapped to the output. To measure the performance, an error function is defined. Given an input, prediction is…

There are two types of matrices that are common — Dense and Sparse Sparse matrix is the one which has most of the elements as zeros as opposed to dense which has most of the elements as non-zeros. Provided with large matrix, it is common that most of the elements…

Data preprocessing is crucial in any data mining process as they directly impact success rate of the project. This reduces complexity of the data under analysis as data in real world is unclean. Data is said to be unclean if it is missing attribute, attribute values, contain noise or outliers…

Data mining is the process of handling large data sets and identify interesting as well as meaningful patterns and relationships between them to solve real world problems. This helps business organizations to make accurate and on-time business decisions leading to tremendous increase in profit. This has become an integral part…