Kaushik Manikonda's repositories
Credit-card-approval-prediction-classification
Credit risk analysis for credit card applicants
Credit_Risk_Classification
Credit risk poses a classification problem that’s inherently imbalanced. Using a dataset of historical lending activity from a peer-to-peer lending services company, build a model that can identify the creditworthiness of borrowers.
Loan-amount-prediction-regression
Predicting how much loan will be approved
Credit_Risk_Analysis
In 2019, more than 19 million Americans had at least one unsecured personal loan. Personal lending is growing at an extremely fast rate, and FinTech firms need to go through an organize large amounts of data in order to optimize lending. Python will be used to evaluate several machine learning models to predict credit risk. Algorithms such as Rando
Data-Analysis-Using-Python-OCNG_669_Geosciences
Python for Geosciences: Basic python, Numpy, Matplotlib, Pandas, Data analysis, Data Visualization
18S191
Course 18.S191 at MIT, Spring 2021 - Introduction to computational thinking with Julia:
Advanced-Quality-Control_ISEN_614
Advanced Quality Control.
Applied-Analytics-in-R_STAT-656
Applied Analytics using R. Machine Learning, Multivariate Analysis, Deep Learning.
astsa
R package to accompany Time Series Analysis and Its Applications: With R Examples -and- Time Series: A Data Analysis Approach Using R
Databases-and-Big-Data-Tools_STAT_624
Python, Parallel R, SQL, SQL Databases, No SQL Databases, Linux and Shell Scripting, Introduction to OpenMP, Compiling, Optimization, Vectorization, Data Management and GIT, Introduction MPI, Introduction to Big Data and Hadoop,
Design-and-Analysis-of-Industrial-Experiments_ISEN_616
Designing Industrial Experiments, one-way, and two-way ANOVA analysis, Experimental design principles (Replication, Randomization, and Blocking), Parameter Estimation, Sample Variance
Industrial-Data-Analytics-and-Machine-Learning_ISEN_613
Data Analytics and Machine Learning in R. Linear-regression, Logistic-regression, Hierarchical-clustering, Boosting, Bagging, Random-forests, K-means-clustering, K-nearest-neighbors (K-N-N), Tree-pruning, Subset-selection, LDA, QDA, Support Vector Machines (SVM)
greykite
A flexible, intuitive and fast forecasting library
hummingbird
Hummingbird compiles trained ML models into tensor computation for faster inference.
inferr
Parametric and non-parametric statistical tests
kaushikmanikonda.github.io
Personal Website Hosting on Github
Machine-Learning_Deep-Learning-in-Python_PETE_689
Machine Learning and Deep Learning in Python.
matplotlib
matplotlib: plotting with Python
NeuralPDE.jl
Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
olsrr
Tools for developing linear regression models
pdfplumber
Plumb a PDF for detailed information about each char, rectangle, line, et cetera — and easily extract text and tables.
Program-Design-and-Concepts-CSCE
C++ program design and concepts; Data Structures.
pyfolio-reloaded
Portfolio and risk analytics in Python
quant-research
A collection of projects published by Bloomberg's Quantitative Finance Research team.
Statistical-Computations-in-R-SAS_STAT_604
Data analysis with R and SAS. Basic R, Basic SAS, Basic Data analysis. Analysis of Texas Covid-19 data. STAT_604, Statistical Computations, Texas A&M University.
Time-Series-Analysis_STAT_626
Time Series Analysis in R.
Twitter-Recommendation-Engine
Source code for Twitter's Recommendation Algorithm
webex-teams-jwt-samples
Python and JavaScript sample code showing how to build and authenticate JSON Web Tokens for Cisco Webex Teams