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.

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danyalimran93/Music-Emotion-Recognition

A Machine Learning Approach of Emotional Model

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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

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the-mrinal/ML-Notebook

Karma of Humans is AI

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uzaymacar/exemplary-ml-pipeline

Exemplary, annotated machine learning pipeline for any tabular data problem.

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chongjason914/scikit-learn-tutorial

Tutorial on how to perform feature encoding, feature scaling, and missing values imputation using the scikit-learn library

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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.

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rsc-dev/ml

Machine Learning Notebooks

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mohadeseh-ghafoori/Coursera-Machine-Learning-Specialization

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prat0101/Data-Science-Portfolio

Data Science Portfolio created for academic and personal projects.

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pawangeek/Machine-Learning-In-Python

Machine learning algorithms repository

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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.

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petermchale/predict_customer_response

Machine-learning models to predict whether customers respond to a marketing campaign

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lmego/customer_segments

Creating Customer Segments - 4th project for Udacity's Machine Learning Nanodegree

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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.

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gvndkrishna/Kaggle-House-Price-Prediction

My solution to House-Prices Advanced Regression Techniques, A beginner-friendly project on Kaggle.

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sakshigupta08/Feature-Scaling

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iAmKankan/Data-Gathering-And-Preprocessing

Tutorial- data Pre-processing

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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

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alexlourencomattos/machinelearning

Aplicação de Aprendizado de Máquina (Machine Learning) para o setor de energia. #Previsão #Carga #Geração #Afluentes

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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.

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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.

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JuzerShakir/Linear_Regression

A Mathematical Intuition behind Linear Regression Algorithm

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ds-brx/EDA-Plotly

EDA and Feature engineering with Plotly library!

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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.

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sabeelahmad/Gradient-Descent

Gradient Descent for N features using two datasets: Boston House data, Power Plant Data

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ian-whitestone/enron-poi-classification

Capstone project for Udacity's Intro to Machine Learning Course

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praj2408/Machine-Learning-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.

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prasanna-muppidwar/One-Shot-Data-PreProcessing-Basics

This GitHub repository contains a comprehensive guide to data preprocessing in Python. It covers a wide range of topics, from basic data cleaning techniques to more advanced techniques such as feature scaling and normalization. The repository includes detailed explanations of each step, along with examples and code snippets that demonstrate how to

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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.

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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.

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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.

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katlass/Machine-Learning

A Collection of Data Science and Machine Learning Projects Utilizing Scikit-Learn, TensorFlow, and R for Predictive Modeling, Time Series Analysis, and Statistical Methods.

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Himel-Sarder/ML-Feature-Scaling-Normalization

This repository contains implementations of various normalization techniques used in machine learning to preprocess data. Normalization ensures that the data scales properly, improving the performance of machine learning algorithms.

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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.

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qtle3/support-vector-regression

This project implements Support Vector Regression (SVR) to predict the salary of an employee based on their position level. The script uses a dataset that contains position levels and corresponding salaries, applying feature scaling to improve the performance of the SVR model. The results are visualized to show how well the model fits the data.

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addygeek/MACHINE-LEARNING-MODELS

This repository contains a collection of machine learning models and notebooks, primarily focused on the housing dataset. It includes implementations of linear regression, logistic regression, feature scaling techniques, and gradient descent using scikit-learn. Additionally, it features learnings from the University of Washington's ML Specializatio

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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.

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yashrajjain726/Weather-Visibility-Prediction

This is a Project which uses live weather data using API, and predicts visibility in the weather.

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Hands-On-Fraud-Analytics/Chapter-12-Data-Preparation-for-Fraud-Analytics

Chapter 12: Data Preparation for Fraud Analytics

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ejay34/06_recovery_of_gold

На основании сырых данных с параметрами добычи и очистки золотоносной руды построить прототип модели для предсказания коэффициента восстановления золота из золотоносной руды с лучшей метрикой sMAPE.

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PervezSH/ml-notebooks

A series of Jupyter notebooks, to know about Machine Learning, its implementation, and identifying its best practices.

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SanghyunKim1/MLB_Team_RunsAllowed_Prediction

MLB Team Runs Allowed Prediction Project (Linear Regression)

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dadavalangege/Feature_Engineering

Improving Machine Learning models performances through Feature Engineering and Feature Scaling techniques such as Principal Component Analysis (PCA), Dummy variables, Standard Scaling and Data Normalization

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risarora/ML_recipes.md

A collection of working snippets used for machine learning related tasks.

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HarshKothari21/Kaggle_Competitions

It's about my analysis on large and real life problem based data set available @kaggle and applied machine learning and Deep learning techniques to build necessary model. Follow me on @Kaggle : https://www.kaggle.com/harshkothari21

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sushant1827/Creating-Customer-Segments

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

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MaaniBeigy/rescale

:straight_ruler: Generic feature scaling methods

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AkashSaxenaOfficial/Bike_Renting

The objective of this project is to predication of bike rental count on daily based on the environmental and seasonal settings. As it gets easy for an organisation to arrange the resource if the demand spikes.

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sanjeevai/customer_segments_arvato

Applied unsupervised learning techniques on demographic and spending data for a sample of German households.

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drizzersilverberg/gradDescentR

R package implementing Gradient Descent and its variants for regression tasks

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Nouran246/House-Pricing-Prediction Fork of Yahia-Elshobokshy/Task1-Machine-Learning

Housing Prices Prediction using Machine Learning Developed a regression model to predict housing prices using data preprocessing, feature engineering, and various regression algorithms. Tuned hyperparameters and evaluated performance with key metrics (RMSE, MAE, R²).

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qweryrr/Intro_to_Machine_Learning_Project

Intro to Machine Learning Project from TripleTen

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LKEthridge/Supervised_Learning

Supervised Learning project from TripleTen

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MvMukesh/FeatureEngineering-Framework-ML

Industry specific framework of Feature Engineering in Machine Learning

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SayamAlt/Egg-Sales-Forecasting-using-LSTM

Successfully established an LSTM model using Pytorch to forecast egg sales at a local shop based on historical sales data of the last 30 years.

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SayamAlt/PDB-Electric-Power-Load-Forecasting-using-LSTM

Successfully developed an LSTM model to forecast electric power load using PyTorch based on historical PDB electric power load data.

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SayamAlt/Global-Equity-Forecasting-using-LSTM

Successfully established an LSTM model to effectively forecast global equity based on over 20+ years of historical data of global equity.

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Saket046/Red-wine-quality-predictor

Red wine quality prediction machine learning model.

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physicskitten/Diabetes-Study

Oversampling Class Imbalance, Feature Scaling, Parameter Tuning, Assessing Model Performance

Language: Python - Size: 29.3 KB - Last synced at: 7 months ago - Pushed at: 7 months ago - Stars: 0 - Forks: 0

priyadarshighosh/ANN_Everyday

Everything about Artificial Neural Network from Basic to Adavnced

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SowmyaKothari/Lead-Scoring-Case-Study

A lead scoring model for the company Education X to improve lead conversion rate

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aneeshmurali-n/Project-ML-Data-Preprocessing

The main objective of this project is to design and implement a robust data preprocessing system that addresses common challenges such as missing values, outliers, inconsistent formatting, and noise. By performing effective data preprocessing, the project aims to enhance the quality, reliability, and usefulness of the data for machine learning.

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Chaitanya1436/Student_Performance_Analysis

A project focused on analyzing college student performance using data on department, assessment scores, and performance labels. Implemented in Google Colab, the analysis includes data preprocessing, feature scaling, and exploratory data analysis to uncover insights and prepare the data for further analysis or modeling.

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TrilokiDA/Data-pre-processing

Data preprocessing is a data mining technique that is used to transform the raw data into a useful and efficient format.

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krisssix/BankruptcyPrediction

Predicting company bankruptcy using various machine learning models. The dataset is sourced from Kaggle: Company Bankruptcy Prediction.

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AmbreenMahhoor/Function-Transformer

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AmbreenMahhoor/Normalization

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AmbreenMahhoor/Standardization

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maheera421/Supervised-Machine-Learning

Implementation of necessary supervised machine learning algorithms for regression and classification.

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frank01101/gradient_descent

Gradient descent algorithm from scratch for linear and logistic regression with feature scaling and regularization.

Language: Python - Size: 16.6 KB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0

zohaibterminator/machine-learning-specialization

Github repo for ML Specialization course on Coursera. Contains notes and practice python notebooks.

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Gokulakkrizhna/singapore-resale-price

We use machine learning and data analysis to predict resale prices of Singapore flats. Our documentation covers data preprocessing, feature engineering, regression, and model selection. Discover how we improved predictions to optimize solutions.

Language: Python - Size: 29.3 MB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

Gokulakkrizhna/industrial-copper-modelling

We leverage machine learning and data analysis to address real-world challenges in the copper industry. Our documentation encompasses data preprocessing, feature engineering, classification, regression, and model selection. Explore how we've enhanced predictive capabilities to optimize manufacturing solutions.

Language: Python - Size: 15.7 MB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

fatimagulomova/iu-lessons

IU Lessons

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jmarihawkins/classification-challenge

The purpose of this project is to develop and compare two machine learning models to detect spam emails. Spam detection is a crucial task in email filtering systems to protect users from unwanted and potentially harmful emails. The project involves using a dataset containing various features extracted from email content.

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LukaMatcharashvili/ML-Lib

Machine Learning Library for C++

Language: C++ - Size: 40 KB - Last synced at: 12 months ago - Pushed at: 12 months ago - Stars: 0 - Forks: 0

Udacity-MachineLearning-Internship/Feature-Scaling

Applying feature scaling with linear regression in python

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Ninad077/Machine_Learning-Gradient_descent_with_Multivariate_Linear_Regression

Content: Multivariate regression, Feature scaling, Polynomial regression, gradient descent, regression using sklearn

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NotTheStallion/Data_preparation_4_ML_algorithm

This project will focus on data preparation and will follow the steps : data cleaning, handling text and categorical attributes, and feature scaling.

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Samir-Zade/Feature-Engineering-and-Exploratory-Data-Analysis

This repository contains resources and code examples related to Feature Engineering and Exploratory Data Analysis (EDA) techniques in the field of data science and machine learning.

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iamSobhan/calories_brunt_prediction

Calories_Brunt_Prediction

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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.

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m-enesyilmaz/Feature_Engineering_with_Python

📶In this repository, we will do feature engineering with Python.

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hialisabet/datamining-course-aut

My datamining course project (aut)

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NifulIslam/DTization

A new method of supervised feature scaling using decision tree

Language: Python - Size: 10.7 KB - Last synced at: about 1 year ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0

Faisal-AlDhuwayhi/Identify-Customer-Segments

Identifying Customer Segments using unsupervised learning techniques

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bagalshreerang/Real-Estate-Price-Prediction

About The Boston House Price Prediction project utilizes data science methodologies and machine learning algorithms to provide accurate predictions for housing prices in the Boston area.

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L98S/Miami-Housing-prices

This project focuses on predicting house prices in Miami using regression techniques. By exploring and analyzing the data, performing feature selection and scaling, trying out different models, and tuning hyperparameters, we aim to develop an accurate model for predicting house prices.

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badhonparvej481/Feature-Scaling_ML

Language: Jupyter Notebook - Size: 1.95 KB - Last synced at: over 1 year ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0

bogumilo/house-prices-xgboost

House prices dataset exploration and prediction. Workflow includes useful examples of Tensorflow pipelines including k-Nearest Neighbors imputer, Decision Tree Regression and XGBoost Regression

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sagar2168616/Heart-Disease-Prediciton

Using Logistic regression algorithm for predicting whether the patient has heart disease or not.

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framdani/ft-linear-regression

Linear regression model to predict the price of a car based on its mileage.

Language: Python - Size: 17.6 KB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

ankitraj1602/ML-SPECIALIZATION

Link to the specialization, which consists of 3 courses . - https://www.coursera.org/specializations/machine-learning-introduction

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NatenaelTBekele/Credit-Card-Users-Churn-Prediction

Classification model that will help the bank improve its services so that customers do not renounce their credit cards

Language: Jupyter Notebook - Size: 8.05 MB - Last synced at: over 1 year ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

NatenaelTBekele/Engineering_Colleges

To identify different types of engineering colleges in the country to better understand the state of affairs

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Naveen-Karanamu/Baltimore-Salary-Prediction

This is the Salary Prediction of the people of the Baltimore City -SVM-SVR

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Naveen-Karanamu/Student-Marks-Prediction

This is the Prediction of the student's Marks based on their Study Hours -SVM-SVR

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PranayChuramani21/Facial-Emotion-Recognition-Model

This project is based on an advanced model with the ability to predict the facial emotion of a person in an image.

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bushra-ansari/Predicting-Term-Deposit-Subscription-by-a-Client-by-SVM-Classifier

Support Vector Machine Classification model is applied on bank dataset containing 41188 rows and 21 columns. The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to assess if the product (bank term deposit) would be ('yes') or not ('no') subscribed.

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