There are 0 repository under log-transformation topic.
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 Processing Algorithms implemented from scratch with in-built concurrency support <3
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
Modeling King County Home Prices via Multiple Linear Regression
It is a classification Problem where we are supposed to predict whether a loan would be approved or not.
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.
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
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.
Image Processing Algorithms
Data Set: House Prices: Advanced Regression Techniques Feature Engineering with 80+ Features
Learn about Simple Linear Regression for Data Science
Predicting Delivery Time Using Sorting Time
Building a prediction model for Salary hike using Years of Experience
Simple Linear Regression
Predicting Customer Response to Telemarketing Campaigns for Term Deposit. Output variable Whether the client has subscribed a term deposit or not.
Predict the Burned Area of Forest Fire with Neural Networks and Predicting Turbine Energy Yield (TEY) using Ambient Variables as Features.
Predict delivery time using sorting time and Build a prediction model for salary hike.
Data prepration and preprocessing for predictive modeling with SAS and Python
Analysis of Skewness and Kurtosis in Stock Return data and their Transformations
In this project, I utilized the RFM Model on the WonderfulWines dataset. As a noteworthy enhancement, I employed a log transformation to achieve greater data symmetry, which ultimately resulted in more accurate outcomes.
Digital Image Processing (Java)
🖼️ Image processing project with implementing different transforms using Matlab power.
Detects sufficient and necessary conditions for pattern inversion conditional on log transform
Testing variables for multicollinearity, multivariate normality and analyzing outliers and missing values. ⭕SPSS 🔵R
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.
This repository contains assignments #3 that was completed as a part of "FIT5196 Data Wrangling", taught at Monash Uni in S2 2020.
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.
Data Science - Simple Linear Regression Work
Spatial Data Science
Feature engineering is the process of transforming raw data into features. Here are some basic ideas about feature engineering.