Top 5th percentile solution to the Kaggle knowledge problem - Bike Sharing Demand

Image processing codes written in python

It is From Analytics Vidhya Hackathons, Sponsored by Club Mahindra. It is based on Regression Problem, Where Accuracy matters the most, It is measured by RMSE Score. Different Techniques such as Stacking, Ensembling, Boosting and Scientific Operations such box-cox Operations to reduce skewness of the data.

Various things, operation related to digital Image Processing

Image Enhancement( Unsharp masking, Histogram Equalisation)

This repository introduces reader to basic concepts of simple linear regression and its application.

This repo includes; Image Negative, Logarithmic Transformation, Power-Law (Gamma) Transformation, Averaging Filter, Median Filter, Laplacian Filter, Sobel Gradiant, Histogram Equalization, DFT, Marr and Hildreth, Otsu Thresholding, Global thresholding

Image Processing Algorithms implemented from scratch with in-built concurrency support <3

It is a classification Problem where we are supposed to predict whether a loan would be approved or not.

Jupyter notebook and "Streamlit" python scripts for identifying features that can predict employee turn over rates at 250 senior care centers across the US. Combines multiple repetition of Lasso regression and linear regression. Integrates U.S. census data, employee salary, and employee tenure with data on employee satisfaction and engagement to improve the prediction accuracy and stability of the model.

Predicting Delivery Time Using Sorting Time

Simple Linear Regression

Data Set: House Prices: Advanced Regression Techniques Feature Engineering with 80+ Features

All Program of 6th Sem Digital Image Processing Lab with their Image Outputs. This Lab provides practical exposure to fundamental image processing concepts through hands-on experiments and simulations. It is designed to complement theoretical learning by applying core techniques using tools like MATLAB or similar environments.

Building a prediction model for Salary hike using Years of Experience

This project focusing on statistical analysis to understand and prepare data for potential machine learning applications. The dataset house_price.csv includes property prices in Bangalore. The analysis aims to perform exploratory data analysis (EDA), detect and handle outliers, check data distribution and normality, and analyze correlations.

Predict the Burned Area of Forest Fire with Neural Networks and Predicting Turbine Energy Yield (TEY) using Ambient Variables as Features.

Image Processing Algorithms

implement the concepts of Fourier Transformation technique such One-Dimensional Fourier Transform, Two-Dimensional Fourier Transform and Image Enhancement technique such as Image Inverse, Power Law Transformation and Log Transformation.

Udacity Data Scientist Nanodegree Project - Employ supervised algorithms to accurately model individuals income

Machine Learning Nano-degree Project : To identify customer segments hidden in product spending data collected for customers of a wholesale distributor

Learn about Simple Linear Regression for Data Science

In this project, we explore the properties of Quantile Regression and compare its results with Ordinary Least Squares regression, using Monte Carlo simulations. The paper highlights Quantile Regression's advantages in handling heteroscedastic data and outliers, and strategies to mitigate quantile crossing.

an R project of manipulating and fittingdata into regression with 95.5% R-Square, involving Automated Selection, detecting outliers, influential observations and multicollinearity

Introdução a técnicas de modelagem de dados para modelos de Regressão Linear utilizando StatsModels e Scikit Learn.

This repository contains MATLAB scripts for performing image transformation operations, including log transformation, power-law transformation, and contrast stretching on grayscale images. These techniques enhance image features and improve visual perception for various applications.

Data Science - Simple Linear Regression Work

Data prepration and preprocessing for predictive modeling with SAS and Python

Predicting Customer Response to Telemarketing Campaigns for Term Deposit. Output variable Whether the client has subscribed a term deposit or not.

This lab focuses on image transformation techniques in OpenCV with Python. Tasks include creating mirror images using both Affine and Projective transformations, applying a Log Transformer for contrast adjustment, and implementing a Power-Law Transformer for gamma correction.