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bank-indessa

Machine Learning Challenge on HackerEarth

basic-text-mining-process

This code gives basic understanding how to feed text data into model and how text different word embedding work.

basic_statistic-and-one-hot-encoding

This code will help to understand how we can perform basic statistic and one-hot encoding over categorical variable.

clustering-with-mean-shift

This is one of the clustering technique. This particular code help how cluster algo help to find pattern is data and how these pattern help us to understand data better. Over here i use TITANIC dataset to find find survival pattern in data in different way.

complete-python-3-bootcamp

Course Files for Complete Python 3 Bootcamp Course on Udemy

cs229_ml

🍟 Stanford CS229: Machine Learning

data-science-with-python

This repo has all the required materials of the Data science with python video series.

datasciencecoursera

Learning data science

datasharing

The Leek group guide to data sharing

educational

end-to-end-ml-model

This code is end to end model building process and tuning of models and finalizing the final model

exdata_plotting1

Plotting Assignment 1 for Exploratory Data Analysis

getting-and-cleaning-data-week-4

Getting and cleaning data- final project-week4

gradient-boosting-regression---sklearn

Boosting is a method of converting weak learners into strong learners. In boosting, each new tree is a fit on a modified version of the original data set.Gradient Boosting trains many models in a gradual, additive and sequential manner. Over here i am using for regression

handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

knn_classification_regression

A case is classified by a majority vote of its neighbors, with the case being assigned to the class most common among-st its K nearest neighbors measured by a distance function.

linear-regression-and-ensemble-tech-using-classification-using-hard-voting

Linear regression - Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. For example, a modeler might want to relate the weights of individuals to their heights using a linear regression model. Voting - You can train your model using diverse algorithms and then ensemble them to predict the final output. Say, you use a Random Forest Classifier, SVM Classifier, Linear Regression etc.; models are pitted against each other and selected upon best performance by voting using the VotingClassifier Class from sklearn.ensemble. Hard voting is where a model is selected from an ensemble to make the final prediction by a simple majority vote for accuracy.

loan-defaulter-prediction-machine-learning

Prediction of loan defaulter based on more than 5L records using Python, Numpy, Pandas and XGBoost

ml-youtube-courses

📺 A place to discover the latest machine learning courses on YouTube.

mslearn-aml-labs

Azure Machine Learning Lab Notebooks

pdftableextract

PDFTableExtract

principal-component-analysis-pca---wine-data

Principal Component Analysis (PCA) for wine data

principal-components-analysis---pca-analysis

Principal Component Analysis (PCA) is a statistical procedure that uses an orthogonal transformation which converts a set of correlated variables to a set of uncorrelated variables. PCA is a most widely used tool in exploratory data analysis and in machine learning for predictive models. Moreover, PCA is an unsupervised statistical technique used to examine the interrelations among a set of variables. It is also known as a general factor analysis where regression determines a line of best fit.a principal component can be defined as a linear combination of optimally-weighted observed variables. The output of PCA are these principal components, the number of which is less than or equal to the number of original variables. Less, in case when we wish to discard or reduce the dimensions in our dataset. The PCs possess some useful properties which are listed below: The PCs are essentially the linear combinations of the original variables, the weights vector in this combination is actually the eigenvector found which in turn satisfies the principle of least squares. The PCs are orthogonal, as already discussed. The variation present in the PCs decrease as we move from the 1st PC to the last one, hence the importance.

programmingassignment2

Repository for Programming Assignment 2 for R Programming on Coursera

regression-models-lasso-ridge-linear-regression---sklearn

This code will help to understand step wise understanding of linear regression model through python.

rossmann-sales-prediction

scikit-learn-mooc

scikit-learn-mooc

suport-vector-regression--scikit--learn

This code help to understand step wise SVM algo for regression model.

vscode

Visual Studio Code