Ramkumar Jothis's repositories
project-one
Analysis of US Residential Real Estate data using Jupyter Notebook, Python, Pandas, Matplotlib
OpenSourceChessProject
Open Source Chess Project
project-four
Machine Learning Model to predict US economic recession based on GDP Data and Long-Term & Short-Term Treasury Interest Rates.
project-three
SEC Finance Data Engineering - ETL process for SEC Finance data of S&P 500 companies. Jupyter Notebooks to run ETL work flows. The final dataset is hosted in MongoDB Atlas(cloud). The API is written using Python with PyMongo and Flask libraries. The dashboards with charts are hosted in MongoDB Atlas.
Algorithms
A collection of algorithms and data structures
credit-risk-classification
Machine Learning Model for Credit Risk Classification using Scikit Learn Logistic Regression
java-websockets
A sample application to demo the usage of web sockets
reactive-todo-service
A sample application to demo the usage of Reactive Programming using Spring framework and Project Reactor with Apache Cassandra database as the backend.
sqlalchemy-challenge
Climate Analysis using Python, SQLite database, SQLAlchemy ORM, Pandas, Matplotlib. Climate Analysis API was developed using SQLAlchemy ORM & Flask API
belly-button-challenge
Interactive Visualization Dashboard built using HTML5, Javascript & D3 Visualization library
city-bike-project
City Bike Project
Crowdfunding_ETL
This project takes the crowd funding data provided in excel files through Extract Transform and Load (ETL) process and makes it available in a relational database for further usage.
CryptoClustering
Create an unsupervised machine learning model that will predict cryptocurrencies that are affected by 24-hour or 7-day price changes.
data-collection-challenge
Mars News and Mars Weather - Data Collection and Analysis. Data scraped from websites using Python packages Splinter and Beautiful Soup.
deep-learning-challenge
Neural Network Model to select the applicants for funding with the best chance of success in their ventures.
Home_Sales
Home sales data is analyzed using SparkSQL. Spark is also used to create temporary views, partition the data, cache and uncache a temporary table, and verify that the table has been uncached.
matplotlib-challenge
Matplotlib challenge
nosql-challenge
UK Food Ratings are imported into a MongoDB Database collection. PyMongo package is used to connect to MongoDB database from Python code and query the MongoDB collection to update and analyze data.
pandas-challenge
Pandas Challenge
Prompt-Engineering-Guide
🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
public-apis
A collective list of free APIs
python-api-challenge
Python API Challenge
python-challenge
Python Challenge
si
The System Initiative software
sql-challenge
Data Modelling, Data Engineering and Data Analysis
styleguide
Style guides for Google-originated open-source projects
VBA-challenge
VBA challenge