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bestfootballgifs-w3

A website where you can upload your favorite football GIF's and upvote them. You can also tip the uploader if you like the GIF.

blood-bank-management-system-database-in-sql-server-2017

I created a simple database for a Blood Bank Management System in SQL Server 2017 and discuss how it can be used to its full potential. Please see report for more details.

clustering-in-r-and-visualizing-results-in-tableau-on-titanic-dataset.

The objective of this exercise is to get a hands on experience on Decision Tree and K-means clustering analysis using R and visualizing results through Tableau. Below are the objectives of the exercise 1)Data retrieval 2)Data pre-processing 3) Decision Tree using R 4) K-mean clustering using Tableau- R integration by invoking Rserve (). The Titanic dataset will be used for this purpose.

complete-python-3-bootcamp

Course Files for Complete Python 3 Bootcamp Course on Udemy

coorelation-matrix-and-heatmap-in-r

Using the Toyota Corolla dataset I observe which variables are related to each other by creating dummy variables and plotting them in a correlation matrix.

geo-heatmap

:world_map: Generate an interactive geo heatmap from your Google location data

getting-started-with-git-and-github

Explaining Git and GitHub.

linear-discrimant-analysis-in-r-

A team collected data on email messages to create a classifier that can separate spam from non-spam email messages. We use LDA to classify emails as spam and non-spam email and then evaluate the effectiveness of the model. The data-set used is from the UCI Machine Learning Library. Here is the link: https://archive.ics.uci.edu/ml/datasets/spambase

logistic-regression-and-confusion-matrix-in-r

Ledoitte, a management consulting firm, is studying the roles played by experience and training in a system administrator’s ability to complete a set of tasks in a specified amount of time. Ledoitte is interested in figuring out which administrators can complete given tasks within a specified time and those who are not. Data are collected on the performance of 75 randomly selected administrators. They are stored in the file SystemAdministrators.csv . The variable Experience measures months of full-time system administrator experience, while Training measures the number of relevant training credits. The outcome variable Completed is either Yes or No, according to whether or not the administrator completed the tasks. 1. Using ggplot2 package, create a scatter plot of Experience vs. Training using color or symbol to distinguish programmers who completed the task from those who did not complete it. Which predictor(s) appear(s) potentially useful for classifying task completion? 2. Run a logistic regression model with both predictors using the entire dataset as training data. Generate a confusion matrix and answer the following: among those who completed the task, what is the percentage of programmers incorrectly classified as failing to complete the task? 3. How much experience must be accumulated by a programmer with 6 years of training before his or her estimated probability of completing the task exceeds 0.6?

my-wave-portal-w-3

optimal-salary-forcasting

“Many receive advice, only the wise profit from it.” — Harper Lee

safaridocs

Repo for safari labs and related docs

svm-on-iris-dataset

Support vector machine algorithm implemented on IRIS data-set to classify the features achieving a accuracy of 97%

tic-tac-toe-game-in-python

visualizing-data-using-gephi

The objective of this exercise is to develop skills on how to visualize and analyze large networks using Gephi. This exercise focuses on the relationship between websites. Here we would be emphasizing on the association of Apple with other websites and with itself.

visualizing-data-using-power-bi

The data in the CSV file is not properly cleaned. So we first clean the data using Python and then visualize it using Power BI.

visualizing-data-using-qlikview

The objective of this exercise is to develop skills on how to visualize the data using QlikView tool. This exercise focuses on visualizing data using multi maps aka Trellis maps. We will create these maps to track youth employment, income and expenditure trends over the years globally. Youth employment, income, and expenditure are key factors in identifying new markets for business.

visualizing-risk-of-disasters-at-oil-refineries-using-tableau

The objective of this project is to use data visualization to find out top 4 natural disasters impacting a corporation in the energy industry. As a part of this exercise we will also create multi maps (also known as Trellis charts or Panel charts) using Tableau. The dataset that will be used for this project contains list of natural disasters (Flood, Hurricane etc.) that occurred in the USA between the years of 2004 to 2015(data source FEMA).