feature-scaling
There are 132 repositories under feature-scaling topic.
ashishpatel26/Amazing-Feature-Engineering
Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
danyalimran93/Music-Emotion-Recognition
A Machine Learning Approach of Emotional Model
dr-mushtaq/Machine-Learning
This repository is a related to all about Machine Learning - an A-Z guide to the world of Data Science. This supplement contains the implementation of algorithms, statistical methods and techniques (in Python), Feature Selection technique in python etc. Follow Coursesteach for more content
the-mrinal/ML-Notebook
Karma of Humans is AI
komal11lamba/50-days-of-Statistics-for-Data-Science
This repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository.
uzaymacar/exemplary-ml-pipeline
Exemplary, annotated machine learning pipeline for any tabular data problem.
chongjason914/scikit-learn-tutorial
Tutorial on how to perform feature encoding, feature scaling, and missing values imputation using the scikit-learn library
Chinmayrane16/Diamonds-In-Depth-Analysis
Given dataset of Diamonds with features such as Cut, Carat, Clarity etc. I have used libraries such as Pandas, Numpy, Matplotlib, Seaborn to Analyse and Estimate the Price of Diamonds based on the features. Using Scikit-Learn , implemented Algorithms to increase the effective R2 score.
rsc-dev/ml
Machine Learning Notebooks
pawangeek/Machine-Learning-In-Python
Machine learning algorithms repository
awesome-mlops/awesome-feature-store
A curated list of awesome open source and commercial feature store tools and platforms 🚀
gvndkrishna/Kaggle-House-Price-Prediction
My solution to House-Prices Advanced Regression Techniques, A beginner-friendly project on Kaggle.
prat0101/Data-Science-Portfolio
Data Science Portfolio created for academic and personal projects.
CYBERDEVILZ/Stock-Market-Prediction
An attempt to predict the Stock Market Price using Long Short Term memory and plot its chart. By tweaking different hyper parameters, we get different trained models. The aim of this project is to identify the relation hidden in these hyper parameters.
lmego/customer_segments
Creating Customer Segments - 4th project for Udacity's Machine Learning Nanodegree
esharma3/project_austin_air_quality_analysis_and_prediction
The purpose of this project is to analyze the impact of climate change on air quality for the city of Austin and create a machine learning model that can establish a correlation between the level of air pollutants like Ozone and NO2 and the climate parameters by using regression models and null hypothesis.
petermchale/predict_customer_response
Machine-learning models to predict whether customers respond to a marketing campaign
Chandradithya8/Feature_Engineering
Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
ds-brx/EDA-Plotly
EDA and Feature engineering with Plotly library!
iAmKankan/Data-Gathering-And-Preprocessing
Tutorial- data Pre-processing
ian-whitestone/enron-poi-classification
Capstone project for Udacity's Intro to Machine Learning Course
JuzerShakir/Linear_Regression
A Mathematical Intuition behind Linear Regression Algorithm
RimTouny/Credit-Card-Fraud-Detection
Focused on advancing credit card fraud detection, this project employs machine learning algorithms, including neural networks and decision trees, to enhance fraud prevention in the banking sector. It serves as the final project for a Data Science course at the University of Ottawa in 2023.
sabeelahmad/Gradient-Descent
Gradient Descent for N features using two datasets: Boston House data, Power Plant Data
SayamAlt/Stellar-Classification---Sloan-Digital-Sky-Survey-17
Successfully established a machine learning model which can predict an appropriate stellar class, on the basis of a distinct set of spectral characteristics, to a substantially high level of accuracy.
skynoid2612/Employee_Absenteeism
The task is to build a machine learning regression model will predict the number of absent hours. As Employee absenteeism is a major problem faced by every employer which eventually lead to the backlogs, piling of the work, delay in deploying the project and can have a major effect on company finances. The aim of this project is to find an issue which eventually leads toward the absence of an employee and provide a proper solution to reduce the absenteeism
bnriiitb/MLND
This repository contains all the Machine Learning and Deep Learning projects that I worked on, spans across the two sub domains of Artificial Intelligence i.e., Computer Vision and Text Processing as a part of Machine Learning Nano Degree program at Udacity.
esharma3/project_boston_houseprice_predictions
The purpose of this project is to predict house prices based of off the Boston house price dataset. The project implements univariate and multivariate linear and polynomial regression models.
sanjeevai/customer_segments_arvato
Applied unsupervised learning techniques on demographic and spending data for a sample of German households.
SayamAlt/Health-Insurance-Claim-Prediction
Successfully established a machine learning model which can estimate the net health insurance claim of an individual based on a set of characteristics of that individual to an appreciable level of accuracy.
shishir349/Prediction-of-Tariff-Rates
Tariff is a list of expenses that incur while transporting the goods from one distance to another distance. Tariff is also dependent on seasonal and non-seasonal factors also. This project is aimed at predicting the tariff ratesfor truck load by using the different machine learning algorithms like lasso regression, elastic net regression, ridge regression and linear regression. Tariffisa combination of lot ofthings and tariff rate is dependent on some ofthe factorslikeYear, Road, SeasonalImpact, Fuel Cost,Distance, Weight, Toll charge, Demand, labour cost, travel expenses etc. Using some ofthese factors and by employing the above-mentioned machine learning regression algorithms we will be trying to predict the tariff rates on the trucks. By doing this we can help the industriesto estimate the tariffratesso that they can take the necessary actions and they can make their business run inprofitable way. This model helps small- and large-scale firms to control and manage the cost on transport.
ZL63388/data-preparation-codes
This repository is a collection of basic code templates for Data Preparation. All codes I am sharing are from the practical exercises I did from the Data Science Infinity Program.
qtle3/logistic-regression
A Python implementation of Logistic Regression to classify social network ads based on age and estimated salary, featuring data visualization and performance metrics such as confusion matrix and accuracy score.