This reprositry contain all the codes of Udacity programming for data science course

Unsupervised ML: Finding Customer Segments in General Population

identifying target audience for a successful marketing campaign

Unsupervised Machine Learning Project: Finding Customer Segments

Create a machine learning pipeline, that categorizes disaster events.

Quick descriptive analysis of the Stack Overflow survey results dataset with focus on conditions conductive to higher job satisfaction

Submissions and project work for the Udacity Data Scientist Nanodegree

Apply unsupervised learning techniques to identify customers segments.

Determine customer churn for a music streaming company

Evaluate and optimize several different supervised learners to determine which algorithm will provide the highest donation yield while also reducing the total number of letters being sent.

Data, notes and solutions for Udacity's Data Scientist Nanodegree.

NLP classifier for disaster messages

Analyze real census data and create a supervised machine learning model to predict potential donor for charity.

Image classifier using Pytorch

Build a CNN model to classify images of dogs according to their breed.

Build an algorithm to best identify potential donors of CharityML

Use unsupervised learning techniques to organize the general population into clusters, then use those clusters to see which of them comprise the main user base for the company.

An an image classifier for species of flowers with PyTorch.

Web app to analyze disaster message into categories using data provided by Figure Eight

Web application showing UK Crime statistics. Built using Python, Flask and Plotly.