feature-scaling

There are 1 repository 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.
feature-engineering machine-learning deep-learning scikit-learn data-mining data-science data-scientists feature-scaling feature-extraction feature-selection features data-analysis data-visualization
Language:Jupyter Notebook 749
danyalimran93 / Music-Emotion-Recognition
A Machine Learning Approach of Emotional Model
machine-learning data-science feature-extraction feature-selection feature-scaling music-information-retrieval digital-signal-processing librosa
Language:Python 246
mohadeseh-ghafoori / Coursera-Machine-Learning-Specialization
gradient-descent linear-regression contour-plot matplotlib numpy mean-normalization learning-curve feature-scaling feature-engineering polynomial-regression sklearn standard-normal decision-boundary logistic-regression regularization feature-mapping regularization-to-avoid-overfitting cost-function
Language:Jupyter Notebook 33
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.
dimensionality-reduction eda feature-engineering feature-extraction feature-scaling feature-selection python statistics
Language:Jupyter Notebook 29
the-mrinal / ML-Notebook
Karma of Humans is AI
numpy-arrays matploblib linear-regression logistic-regression logisticregression confusion-matrix decision-tree-algorithm feature-scaling gradient-descent naive-bayes-classifier knn-classifier text-classification svm-classifier pca-analysis notebook used-cars gridsearchcv
Language:Jupyter Notebook 26
uzaymacar / exemplary-ml-pipeline
Exemplary, annotated machine learning pipeline for any tabular data problem.
data-cleaning data-imputation data-visualization feature-encoding feature-engineering feature-scaling feature-selection featuretools h2o machine-learning-pipelines modeling sklearn
Language:Jupyter Notebook 26
awesome-mlops / awesome-feature-store
A curated list of awesome open source and commercial feature store tools and platforms 🚀
dataops datascience feature-engineering feature-extraction feature-scaling feature-selection feature-store kubernetes machine-learning mlops model-serving awesome awesome-list
20
chongjason914 / scikit-learn-tutorial
Tutorial on how to perform feature encoding, feature scaling, and missing values imputation using the scikit-learn library
feature-encoding feature-scaling imputation-methods
Language:Jupyter Notebook 19
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.
data-visualization feature-engineering feature-scaling pandas numpy seaborn matplotlib machine-learning
Language:Jupyter Notebook 18
gvndkrishna / Kaggle-House-Price-Prediction
My solution to House-Prices Advanced Regression Techniques, A beginner-friendly project on Kaggle.
data-visualization hyperparameter-optimization xgboost python3 lasso-regression-model ridge-regression feature-engineering feature-scaling support-vector-regression lgbmregressor kaggle-competition
Language:Jupyter Notebook 10
Machine-Learning-In-Python
pawangeek / Machine-Learning-In-Python
Machine learning algorithms repository
clustering feature-scaling machine-learning machine-learning-algorithms outlier-detection
Language:Jupyter Notebook 9
rsc-dev / ml
Machine Learning Notebooks
feature-scaling jupyter-notebook linear-regression machine-learning ml normal-equation
Language:Jupyter Notebook 9
Stock-Market-Prediction
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.
deep-learning feature-scaling hacktoberfest jupyter-notebook long-short-term-memory lstm lstm-neural-networks machine-learning python3 recurrent-neural-networks research-project stock-price-prediction tensorflow2 tkinter-gui
Language:PureBasic 7
prat0101 / Data-Science-Portfolio
Data Science Portfolio created for academic and personal projects.
feature-engineering feature-scaling feature-selection gradient-boosting hyperparameter-tuning kmeans-clustering linear-regression pandas performance-metrics python sklearn xgboost
Language:Jupyter Notebook 7
lmego / customer_segments
Creating Customer Segments - 4th project for Udacity's Machine Learning Nanodegree
machine-learning udacity udacity-nanodegree unsupervised-learning clustering clustering-algorithm creating-customer-segments scatter-matrix feature-scaling outlier-detection feature-transformation pca dimensionality-reduction biplot gaussian-mixture-models
Language:HTML 6
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.
api feature-scaling lasso-regression linear-regression pandas plotly polynomial-regression python random-forest requests seaborn
Language:Jupyter Notebook 5
petermchale / predict_customer_response
Machine-learning models to predict whether customers respond to a marketing campaign
pandas data-cleaning one-hot-encode missing-values imputation feature-scaling receiver-operating-characteristic neural-network support-vector-machine decision-tree dummy-variable multicollinearity
Language:Jupyter Notebook 5
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.
exploratory-data-analysis feature-engineering feature-scaling machine-learning model-deployment regression-analysis regression-testing
Language:Jupyter Notebook 5
Pradnya1208 / Naive-Bayes
This project aims to understand and build Naive Bayes classifier to predict the salary of a person.
naive-bayes-classifier bayes-theorem cardinality feature-engineering feature-scaling cross-validation confusionmatrix accuracy-metrics
Language:Jupyter Notebook 4
sabeelahmad / Gradient-Descent
Gradient Descent for N features using two datasets: Boston House data, Power Plant Data
machine-learning linear-regression linear-regression-models gradient-descent gradient-descent-algorithm boston-housing-price-prediction sklearn numpy pandas power-plant-predictions feature-scaling
Language:Jupyter Notebook 4
sakshigupta08 / Feature-Scaling
feature-scaling machine-learning scikit-learn
Language:Jupyter Notebook 4
AkashSaxenaOfficial / 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
data-science machine-learning-algorithms machinelearning-r machine-learning-models machine-learning-regression exploratory-data-analysis missing-values feature-selection feature-scaling knn-regression knn-model decision-tree-regression random-forest-regression linear-regressor data-analysis data-visualization
Language:Jupyter Notebook 3
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.
sklearn encoding feature-scaling
Language:Jupyter Notebook 3
iAmKankan / Data-Gathering-And-Preprocessing
Tutorial- data Pre-processing
missing-values feature-scaling feature-selection feature-engineering dimentionality-reduction principal-component-analysis-pca
Language:Jupyter Notebook 3
ian-whitestone / enron-poi-classification
Capstone project for Udacity's Intro to Machine Learning Course
python feature-scaling machine-learning
Language:Jupyter Notebook 3
JuzerShakir / Linear_Regression
A Mathematical Intuition behind Linear Regression Algorithm
linear-regression machine-learning algorithm univariate-regressions multivariate-regression hypothesis house-price-prediction linear-equations cost-function gradient-descent mean-square-error mse partial-derivative linear-algebra vector matrices feature-scaling mean-normalization bias-variance regularized-linear-regression
3
praj2408 / Machine-Learning-For-Operations-Notes
This repo contains Comprehensive notes covering various machine learning concepts, algorithms, and applications, providing a structured resource for both beginners and experienced practitioners to deepen their understanding and proficiency in the field.
feature-engineering feature-scaling handwritten hyperparameter-tuning machine-learning-algorithms metrics pca
Language:Jupyter Notebook 3
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.
classification-algorithms correlation-analysis data-science detection-model dimensionality-reduction encoding feature-engineering feature-scaling fraud-detection fraudulent-transactions label-encoding outliers pca smote standardscaler tsne-visualization non-fraudulent-transactions kears max-pooling nerual-network
Language:Jupyter Notebook 3
SanghyunKim1 / MLB_Team_RunsAllowed_Prediction
MLB Team Runs Allowed Prediction Project (Linear Regression)
mlb sabermetrics sports-analytics baseball baseball-statistics machine-learning linear-regression feature-engineering feature-selection data-imputation k-fold-cross-validation data-cleaning eda feature-scaling
Language:Python 3
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.
imbalanced-learning model-deployment multiclass-classification supervised-machine-learning cross-validation data-preprocessing data-visualization exploratory-data-analysis feature-engineering feature-scaling model-evaluation model-training
Language:Jupyter Notebook 3
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.
tariff machine-learning data-science data-analysis data-visualization supervised-machine-learning exploratory-data-analysis exploratory-data-visualizations statistics statistical-analysis principal-component-analysis feature-extraction feature-engineering feature-scaling data-cleaning data-manipulation aggregations grouping-operations
Language:Jupyter Notebook 3
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.
python3 pandas seaborn matplotlib linear-regression lasso-regression polynomial-regression feature-scaling
Language:Jupyter Notebook 2
fatimagulomova / iu-lessons
IU Lessons
anova-test chi-square-test clustering-algorithm correlation-matrix feature-engineering feature-engineering-algorithm feature-scaling feature-selection feature-variance kmeans-clustering machine-learning mutual-information pandas pandas-python python random-forest sklearn supervised-learning unsupervised-machine-learning
Language:Jupyter Notebook 2
gopiashokan / Industrial-Copper-Modeling-using-Machine-Learning
We harness the power of machine learning and data analysis to real challenges in the copper industry. Our documentation covers data preprocessing, feature engineering, classification, regression, and model selection. Discover how we've optimized predictive capabilities for manufacturing solutions.
classification copper cross-validation data-preprocessing exploratory-data-analysis feature-engineering feature-scaling gridsearchcv hyperparameter-optimization hyperparameter-tuning industrial machine-learning machine-learning-algorithms metrics modeling outlier-treatment prediction python regression skewness
Language:Jupyter Notebook 2
luckywirasakti / machine-learning-toolbox
imbalance-dataset feature-selection data-transformation feature-scaling adasyn information-gain python scratch jupyter-notebook min-max-normalization
Language:Jupyter Notebook 2
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.
pandas simpleimputer knn-imputer onehot-encoding outlier-detection feature-scaling feature-selection
Language:Python 2

feature-scaling

ashishpatel26 / Amazing-Feature-Engineering

danyalimran93 / Music-Emotion-Recognition

mohadeseh-ghafoori / Coursera-Machine-Learning-Specialization

komal11lamba / 50-days-of-Statistics-for-Data-Science

the-mrinal / ML-Notebook

uzaymacar / exemplary-ml-pipeline

awesome-mlops / awesome-feature-store

chongjason914 / scikit-learn-tutorial

Chinmayrane16 / Diamonds-In-Depth-Analysis

gvndkrishna / Kaggle-House-Price-Prediction

pawangeek / Machine-Learning-In-Python

rsc-dev / ml

CYBERDEVILZ / Stock-Market-Prediction

prat0101 / Data-Science-Portfolio

lmego / customer_segments

esharma3 / project_austin_air_quality_analysis_and_prediction

petermchale / predict_customer_response

SayamAlt / Health-Insurance-Claim-Prediction

Pradnya1208 / Naive-Bayes

sabeelahmad / Gradient-Descent

sakshigupta08 / Feature-Scaling

AkashSaxenaOfficial / Employee_Absenteeism

Chandradithya8 / Feature_Engineering

iAmKankan / Data-Gathering-And-Preprocessing

ian-whitestone / enron-poi-classification

JuzerShakir / Linear_Regression

praj2408 / Machine-Learning-For-Operations-Notes

RimTouny / Credit-Card-Fraud-Detection

SanghyunKim1 / MLB_Team_RunsAllowed_Prediction

SayamAlt / Stellar-Classification---Sloan-Digital-Sky-Survey-17

shishir349 / Prediction-of-Tariff-Rates

esharma3 / project_boston_houseprice_predictions

fatimagulomova / iu-lessons

gopiashokan / Industrial-Copper-Modeling-using-Machine-Learning

luckywirasakti / machine-learning-toolbox

ZL63388 / data-preparation-codes