Data Science

Supervised Learnings - Classifications

Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It works for both categorical and continuous input and output variables.

Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.

Logistic Regression is a classification algorithm. It is used to predict binary outcome (1/0, Yes/No, True/False) given a set of independent variables. To represent binary/categorical outcome, we use dummy variables.

https://github.com/epilicer/MachineLearning/blob/master/ClassificationsExamples.ipynb

Predictive Analysis

This folder contains example Jupyter Notebooks for predictive models:

• Linear regression

• Polynominal regression

• Multivariate regression

These models uses statistics to predict outcomes from a dataset

https://github.com/epilicer/PredictiveAnalysis

Support Vector Machine

It is an advanced way of clustering or classifying higher dimensional data. When there are multiple features to predict from, SVM, which is a supervised learning technique, can be used.

SVM finds higher dimensional support vectors across which to divide, and those of support vectors are mathematically defined as hyperplanes.

https://github.com/epilicer/MachineLearning/blob/master/SupportVectorMachine.ipynb

Python Survey Analysis

Context In the fall of 2018, the Python Software Foundation together with JetBrains conducted the official annual Python Developers Survey for the second time. Over twenty thousand developers from more than 150 different countries participated.

https://github.com/epilicer/SurveyAnalysis/blob/master/PythonSurveyAnalysis.ipynb