daksh-intwala Goto Github PK

big-data-learning

control_chart-quality_control

Introduction to control chart, how data can be evaluated to be statistically under control or not.

data_601

Work for DATA 601: Introduction to Data Science

decisiontreeclassifier---basic

ds601_spring22

ewr_airport_capacity_analysis

handgesture_recognition_modeling

house-price-prediction--regression-anaylsis

A simple regression analysis of house prices in USA with 11 features selected on MECE Framework

hypothesis-testing---student-s-t-test-anova-test

Hypothesis Testing is done on a payroll database using various testing methods.

internship-basics

Python ML lib Basics

k-means---high-dimensional-data

Applying Kmeans to high dimensional data with 63 column fields.

knn--fruits-classification

Using KNN to classify fruits based on 4 features (mass,width,heigth,color).

makeup_industry_problem_identification-solution

Identified a problem and set out a plan, flowchart to solve it including architecture, modeling and validation where all proposed solutions are backed by thorough research and analysis.

maryland-health-insurance-data-tableau-analysis

Analyzed and performed EDA on health insurance data of Maryland State using Tableau.

ml--implementation-of-lr-svm-lda-knn-cart-nb

Just a basic implementation of 6 popular models. A full complete guide for an ML program for beginners!

multiple-linear-regression_ols

Ordinary Least Squared method was implemented to analyze an MLR problem.

naive-bayes-classifier_with_normality_tests

Gaussian Navie Bayes Classifier was applied on IRIS dataset. Different types of normality tests were used to introduce the normality concepts.

nlp_ulmfit

node-converter

Convert SVC and XLSC file to PDF

programmingassignment2

Repository for Programming Assignment 2 for R Programming on Coursera

py

Repository to store sample python programs for python learning

s-p500-stock-analysis-and-lstm-model-prediction

Detailed EDA performed in DataBricks environment wherein ETL was done through PySpark. LSTM model was trained and effective predictions were made with minimal Mean Square Error. The aim was to guide beginners in investing stock market and how S&P500 stock can be a safe heaven for the passive investors and a real-time big investment opportunity for venture capitalists.

snap-n-eat

Food detection and recommendation with deep learning

thresholding-opencv-pdf

Trying Data mining through thresholding

tsalib

Tensor Shape Annotation Library (numpy, tensorflow, pytorch, ...)

uniswap2-queries-and-web3---ethereumpython

A project of data retrieval from Uniswap2 in a DataFrame coded in Python3.9.5 on Jupyter Notebook-VScode.