ArunRamachandran25
Hello! My name is Arun Ramachandran and I had worked at Wipro Technologies as a Java Developer and at World Wide Fund for Nature in India as a Research Analyst.
i2e Consulting | Wipro Technologies / Wipro Digital | World Wide Fund for Nature in India | Chegg - IndiaWest Delhi, New Delhi
Pinned Repositories
Analyzing-US-Economic-Data-and-Building-a-Dashboard
Extracting essential data from a datasets and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some essential economic indicators from some data, you will then display these economic indicators in a Dashboard. Gross domestic product (GDP) is a measure of the market value of all the final goods and services produced in a period. GDP is an indicator of how well the economy is doing. A drop in GDP indicates the economy is producing less; similarly an increase in GDP suggests the economy is performing better. In this, you will examine how changes in GDP impact the unemployment rate.
Applied-Data-Science-Capstone-Foursquare-API
In this lab, you will learn in details how to make calls to the Foursquare API for different purposes. You will learn how to construct a URL to send a request to the API to search for a specific type of venues, to explore a particular venue, to explore a Foursquare user, to explore a geographical location, and to get trending venues around a location. Also, you will learn how to use the visualization library, Folium, to visualize the results.
Applied-Data-Science-Capstone-Segmenting-and-Clustering-Neighborhoods-in-New-York-City
In this lab, you will learn how to convert addresses into their equivalent latitude and longitude values. Also, you will use the Foursquare API to explore neighbourhoods in New York City. You will use the explore function to get the most common venue categories in each neighbourhood, and then use this feature to group the neighbourhoods into clusters. You will use the k-means clustering algorithm to complete this task. Finally, you will use the Folium library to visualise the neighbourhoods in New York City and their emerging clusters.
awesome-internships
A curated list of tech internships resources.
Boston-Housing-Data-Statistical-Analysis-using-Python
The code sample is from the Boston Housing Data Analysis which was performed using Python. The code basically involved various data visualizations on the columns and thereby extracting meaningful information from the graphs like Scatter Plots, Boxplots. Then we used those graphs for analysis via hypothesis testing like code sample included t-test, ANOVA, Correlation and other metrics to extract information which supports the visualizations we prepared. It also contains Linear Regression to create a machine learning model to support our analysis. The code sample included Pearson test for continuous variables and Chi-Square Test for the categorical variables.
Coursera_Capstone
Peer-graded Assignment - Capstone Project Notebook This capstone project course will give you a taste of what data scientists go through in real life when working with data.
Data-Analysis-with-Python-House-Sales-in-King-County-USA
In this assignment, you are a Data Analyst working at a Real Estate Investment Trust. The Trust will like to start investing in Residential real estate. You are tasked with determining the market price of a house given a set of features. You will analyze and predict housing prices using attributes or features such as square footage, number of bedrooms, number of floors, and so on. This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015.
Machine-Learning-with-Python-Multiple-Linear-Regression
In this lab, we learn how to use scikit-learn library to implement Multiple linear regression. We again use the Carbon dioxide emission dataset to build a model, Evaluate the model, and finally use model to predict unknown value.
Machine-Learning-with-Python-Simple-Linear-Regression-
In this lab, we learn how to use scikit-learn library to implement Simple linear regression. We download a dataset that is related to fuel consumption and Carbon dioxide emission of cars. Then, we split our data into training and test sets, create a model using training set, evaluate your model using test set, and finally use model to predict unknown value.
open-source-communication-channel
Guides, best practices, templates, and discussions for the WHO open source community
ArunRamachandran25's Repositories
ArunRamachandran25/Applied-Data-Science-Capstone-Foursquare-API
In this lab, you will learn in details how to make calls to the Foursquare API for different purposes. You will learn how to construct a URL to send a request to the API to search for a specific type of venues, to explore a particular venue, to explore a Foursquare user, to explore a geographical location, and to get trending venues around a location. Also, you will learn how to use the visualization library, Folium, to visualize the results.
ArunRamachandran25/Applied-Data-Science-Capstone-Segmenting-and-Clustering-Neighborhoods-in-New-York-City
In this lab, you will learn how to convert addresses into their equivalent latitude and longitude values. Also, you will use the Foursquare API to explore neighbourhoods in New York City. You will use the explore function to get the most common venue categories in each neighbourhood, and then use this feature to group the neighbourhoods into clusters. You will use the k-means clustering algorithm to complete this task. Finally, you will use the Folium library to visualise the neighbourhoods in New York City and their emerging clusters.
ArunRamachandran25/Coursera_Capstone
Peer-graded Assignment - Capstone Project Notebook This capstone project course will give you a taste of what data scientists go through in real life when working with data.
ArunRamachandran25/Extracting-Stock-Data-Using-a-Python-Library
In this lab, you will use a Python library to obtain financial data. You will extract historical stock data using yfinance.
ArunRamachandran25/Machine-Learning-with-Python-Agglomerative-clustering
In this lab, we will be looking at Agglomerative clustering, which is more popular than Divisive clustering. We will also be using Complete Linkage as the Linkage Criteria.
ArunRamachandran25/Machine-Learning-with-Python-Collaborative-Filtering-on-Movies
Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous can be commonly seen in online stores, movies databases and job finders. In this notebook, we will explore recommendation systems based on Collaborative Filtering and implement simple version of one using Python and the Pandas library.
ArunRamachandran25/Machine-Learning-with-Python-DBSCAN-Clustering
Density-based Clustering locates regions of high density that are separated from one another by regions of low density. Density, in this context, is defined as the number of points within a specified radius. In this section, the main focus will be manipulating the data and properties of DBSCAN and observing the resulting clustering.
ArunRamachandran25/Machine-Learning-with-Python-Decision-Trees
In this lab exercise, you will learn a popular machine learning algorithm, Decision Tree. You will use this classification algorithm to build a model from historical data of patients, and their response to different medications. Then you use the trained decision tree to predict the class of a unknown patient, or to find a proper drug for a new patient.
ArunRamachandran25/Machine-Learning-with-Python-k-Means
Despite its simplicity, the K-means is vastly used for clustering in many data science applications, especially useful if you need to quickly discover insights from unlabelled data. In this notebook, you learn how to use k-Means for customer segmentation.
ArunRamachandran25/Machine-Learning-with-Python-KNN
In this Lab you will load a customer datasets related to a telecommunication company, clean it, use KNN (K-Nearest Neighbours to predict the category of customers, and evaluate the accuracy of your model. Let's learn about KNN and see how we can apply it real world problems.
ArunRamachandran25/Machine-Learning-with-Python-Logistic-Regression
In this notebook, you will learn Logistic Regression, and then, you'll create a model with telecommunications data to predict when its customers will leave for a competitor, so that you can take some action to retain the customer.
ArunRamachandran25/Machine-Learning-with-Python-Peer-graded-Assignment-The-best-classifier
In this project, you will complete a notebook where you will build a classifier to predict whether a loan case will be paid off or not. You load a historical dataset from previous loan applications, clean the data, and apply different classification algorithm on the data. You are expected to use the following algorithms to build your models: k-Nearest Neighbour, Decision Tree, Support Vector Machine, Logistic Regression. The results is reported as the accuracy of each classifier, using the following metrics when these are applicable: Jaccard index, F1-score, Log Loss.
ArunRamachandran25/Machine-Learning-with-Python-SVM-Support-Vector-Machines
In this notebook, you will use SVM (Support Vector Machines) to build and train a model using human cell records, and classify cells to whether the samples are benign or malignant.
ArunRamachandran25/Python-Project-for-Data-Science-Analyzing-Historical-Stock-Revenue-Data-and-Building-a-Dashboard
As a data scientist working for an investment firm, you will extract the revenue data for Tesla and GameStop and build a dashboard to compare the price of the stock vs the revenue.
ArunRamachandran25/qiskit-aer
Aer is a high performance simulator for quantum circuits that includes noise models
ArunRamachandran25/Applied-Data-Science-Capstone-k-means-Clustering
There are many models for clustering out there. In this lab, we will be presenting the model that is considered the one of the simplest model among them. Despite its simplicity, *k*-means is vastly used for clustering in many data science applications, especially useful if you need to quickly discover insights from unlabelled data.
ArunRamachandran25/ArunRamachandran25
My Personal Repository
ArunRamachandran25/datahub
A Generalized Metadata Search & Discovery Tool
ArunRamachandran25/feasts
Feature Extraction And Statistics for Time Series
ArunRamachandran25/github1s
One second to read GitHub code with VS Code.
ArunRamachandran25/Machine-Learning-with-Python-Content-based-Recommendation-Systems
Lab: Content-based Recommendation Systems - Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous can be commonly seen in online stores, movies databases and job finders. In this notebook, we will explore Content-based recommendation systems and implement a simple version of one using Python and the Pandas library.
ArunRamachandran25/Peer-graded-Assignment-Segmenting-and-Clustering-Neighbourhoods-in-Toronto
Peer-graded Assignment: Segmenting and Clustering Neighbourhoods in Toronto - In this assignment, you will be required to explore, segment, and cluster the neighbourhoods in the city of Toronto based on the postal-code and borough information.. However, unlike New York, the neighbourhood data is not readily available on the internet. What is interesting about the field of data science is that each project can be challenging in its unique way, so you need to learn to be agile and refine the skill to learn new libraries and tools quickly depending on the project. For the Toronto neighbourhood data, a Wikipedia page exists that has all the information we need to explore and cluster the neighbourhoods in Toronto. You will be required to scrape the Wikipedia page and wrangle the data, clean it, and then read it into a pandas data-frame so that it is in a structured format like the New York datasets. Once the data is in a structured format, you can replicate the analysis that we did to the New York City datasets to explore and cluster the neighbourhoods in the city of Toronto.
ArunRamachandran25/Python
All Algorithms implemented in Python
ArunRamachandran25/Python-Project-for-Data-Science-Extracting-Stock-Data-Using-Web-Scraping
In this lab you will use web scraping to obtain financial data. You will extract historical stock data from a web-page using beautiful soup.
ArunRamachandran25/Python-Project-for-Data-Science-Intro-to-Web-Scraping-Using-BeautifulSoup
In this lab you will learn how to use BeautifulSoup and specifically how to extract data in HTML tables to a DataFrame.
ArunRamachandran25/qiskit
Qiskit is an open-source SDK for working with quantum computers at the level of circuits, algorithms, and application modules.
ArunRamachandran25/qiskit-aqua
Quantum Algorithms & Applications in Python
ArunRamachandran25/qiskit-ibmq-provider
Qiskit Provider for accessing the quantum devices and simulators at IBMQ
ArunRamachandran25/qiskit-ignis
Ignis provides tools for quantum hardware verification, noise characterization, and error correction.
ArunRamachandran25/qiskit-terra
Terra provides the foundations for Qiskit. It allows the user to write quantum circuits easily, and takes care of the constraints of real hardware.