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akashkadam75.github.io

algorithm

exercise for algorithm

algorithms

algorithms-1

A collection of algorithms and data structures

apache-spark-kafka-poc-s

Implemented Spark machine learning Pipeline on AWS EMR for Collaborative Filtering to recommend users which online educational course they should take based on their viewing history. Target audience found using K-Means clustering over 2 billion data rows. • Using Kafka & Spark Structured Streaming simulated the above models as real time events with a window size of 2 minutes. • House price prediction for California residents based on Kaggle’s 2014/15 dataset using Linear Regression. Narrowed down the customers who were likely to purchase using Logistic Regression & Decision Tree Classifier along with Random Forests to choose the best performing model.

awesome-for-beginners

A list of awesome beginners-friendly projects.

awesome-scalability

The Patterns of Scalable, Reliable, and Performant Large-Scale Systems

backtobackswe

Code Examples For Back To Back SWE Lessons

beyond-leetcode-sql

Analysis of SQL Leetcode and classic interview questions. Common pitfalls, anti-patterns and handy tricks are discussed. Sample databases are provided.

big-data-mapreduce-course

Big Data Modeling, MapReduce, Spark, PySpark @ Santa Clara University

california_housing_prices

End-to-End Machine Learning Project based on the Stalib repository. This dataset is based on data from the 1990 California census. The machine learning models built in this project helps in determining whether to invest or not in the given area. The final prediction is made for a district's median housing price. This data has metrics such as the population, median income, median housing price, and so on for each block group in California. Block groups are the smallest geographical unit for which the US Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people). </br> System uses multiple features to make a prediction (it will use the district’s population, the median income, etc.). This classifies for a **multivariate regression problem**. Hence the performance measure chosen for this is **_RMSE_** (Root Mean Square Error).

certification

Online Certification

checkers_project

Java implementation of Checkers

checkers_project_2

Java implementation of Checkers with MouseListener

coding-challenges-practice

Answers to various puzzles and coding interview questions solved in Java for practice

coen_280_hw4

XML schema and Instance

complete-guide-to-elasticsearch

Contains all of the queries used within the Complete Guide to Elasticsearch course.

cowin-vaccine-slot-checker

This is a Python script which will help you to know when a slot is available on Cowin website.

data-science--cheat-sheet

Cheat Sheets

data-structures-algorithms

repository to record exercises of data structures and algorithms

data_analysis_and_visualization_using_pandas

Using Pandas, NumPy, Matplotlib, Seaborn, Plotly and Cufflinks, the answers for financial crisis of 2007/08 & the most crime affected US regions are visualized with detailed description leading to those events.

deep-learning-dashboard

DeepDash, is a deep learning dashboard which builds predictive stats from image data using 2 models: K Nearest Neighbors and VGG19. These stats are then displayed on the front end for user to analyse and develop/choose a strategy to solve the business problem he/her is working on. This first release of DeepDash is intended for people who have access to image datasets (public or private).

deepdash

A Deep Learning Dashboard

dgim_project

Mini project to implement DGIM Algorithm for estimating number of ones in continuous bit stream. Objective of this project is to estimate number of ones in past K data with a tolerance of 33%. The major challenge with continuous stream of bit is that storing the stream in main memory is not possible because of the continuous flow of bit stream which will start accumulating and eventually exceeds the size of memory. Therefore, using DGIM Algorithm, n number of bits can be stored in log n memory space. Often, it is much more efficient to get an approximate answer to our problem than an exact solution.

dockercasts

Companion repo to a course on Udemy.com

e-commerce-application

Spring Demo for an E-Commerce Application

edit_distance

Java implementation to find all possible Edit Distances

examples

Apache Kafka and Confluent Platform examples and demos

github-matrix-screensaver

The GitHub Matrix Screensaver for Mac OSX

gitignore

A collection of useful .gitignore templates