feature-scaling
There are 133 repositories under feature-scaling topic.
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.
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
Chapter-12-Data-Preparation-for-Fraud-Analytics
Chapter 12: Data Preparation for Fraud Analytics
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.
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.
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.
Beginner-Machine-Learning-in-Python-ChatGPT-Bonus-2023
An introduction into the world of machine learning with a comprehensive Udemy online course, designed for beginners, to learn Python programming fundamentals and gain valuable insights into the practical applications of machine learning.
ml-notebooks
A series of Jupyter notebooks, to know about Machine Learning, its implementation, and identifying its best practices.
Naive-Bayes
This project aims to understand and build Naive Bayes classifier to predict the salary of a person.
Machine-Learning
Machine Learning in Scikit-Learn and TensorFlow
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
MLB_Team_RunsAllowed_Prediction
MLB Team Runs Allowed Prediction Project (Linear Regression)
Weather-Visibility-Prediction
This is a Project which uses live weather data using API, and predicts visibility in the weather.
Customer-Car-Purchase-Prediction
Here we make Predictions of car purchase by customer using Machine Learning algorithms.
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
Data-preprocessing-ML-
Importing Dataset, missing data,
DSND-Term1-Identify_Customer_Segments
Apply unsupervised learning techniques to identify customers segments.
rescale
:straight_ruler: Generic feature scaling methods
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.
machinelearning
Aplicação de Aprendizado de Máquina (Machine Learning) para o setor de energia. #Previsão #Carga #Geração #Afluentes
customer-segments
Machine Learning Engineer Nanodegree, Unsupervised Learning, Creating Customer Segments
ML_recipes.md
A collection of working snippets used for machine learning related tasks.
Data-Scientist-Salary-Prediction
Created a machine learning model that estimates salary of data scientist based on the features like rating, company_founded, etc.
ML-Prediction-Model
IN PROGRESS. Machine learning model aimed at predicting the probability of a customer purchasing a car, based on demographic and financial data. This model development is in progress and serves as an integral component of my learning process, specifically targeting skills needed for machine learning deployment and optimization.
Lead-Scoring-Case-Study
A lead scoring model for the company Education X to improve lead conversion rate
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.
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.
BankruptcyPrediction
Predicting company bankruptcy using various machine learning models. The dataset is sourced from Kaggle: Company Bankruptcy Prediction.
Supervised-Machine-Learning
Implementation of necessary supervised machine learning algorithms for regression and classification.
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.
mean-normalization-data-separation
Mean Normalization and Data Separation is a project exploring feature scaling and data preparation techniques in machine learning. It includes a detailed implementation of mean normalization to distribute data evenly around zero and covers methods for separating datasets for training and testing models. Presented in an interactive Jupyter Notebook.
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.
Spam-Email-Detection-ML
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.