GitHub topics: standardscaler | Ecosyste.ms: Repos

Marlyn-Mayienga/Titanic-Survival-Prediction

Predicting passenger survival on the Titanic using an ensemble machine learning approach, achieving a Kaggle score of 0.77990. This project leverages stacking with Random Forest, Gradient Boosting, and SVM, enhanced by feature engineering and hyperparameter tuning, to model survival patterns effectively.

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alvinadar/Logistics-Regression

Implementation of logistics regression for Student pass fail and HR Retention analysis

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alvinadar/Hierarchical-Clustering

Agglomerative HC step by step concept

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RichmondDjwerter/Autoencoder-Based-Multi-Modal-Movie-Recommendation-System

This Multi-Modal Movie Recommendation System leverages a combination of structured numerical features and deep text embeddings to provide accurate and personalized movie recommendations. Unlike traditional recommender systems that rely solely on user ratings or metadata, this model integrates numerical attributes (such as popularity and ratings)

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maheshvarade/Mobile-Price-Classification-using-ML-SVM-Logistic-Regression-

This project tackles the challenge faced by a new mobile company founded by Prabhat, who wants to compete with tech giants like Apple and Samsung. The goal is to predict the price range of a mobile phone based on its features — not the exact price, but whether it's low, medium, high, or very high cost.

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MUHAMMADAKMAL137/Pytorch_Training_Pipeline_Using_Dataset_And_Dataloader_Class.ipynb

Binary classification of breast cancer using PyTorch. Used StandardScaler, LabelEncoder, Dataset, DataLoader, custom nn.Module model, BCELoss, and SGD. Focused on implementing a complete training pipeline, not optimizing accuracy.

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CamilaJaviera91/apache-beam-pipeline-first-approach

This code demonstrates how to integrate Apache Beam with scikit-learn datasets and perform simple data transformations. It loads the Linnerud dataset from scikit-learn, converts it into a Pandas DataFrame for easier manipulation.

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SridharYadav07/Code_Alpha_Creditcard_Scoring

This repository serves as a comprehensive example of how to preprocess data, train a Random Forest classifier, and evaluate its performance using several key metrics. The insights gained from the confusion matrix, classification report, and ROC-AUC score help to assess where the model excels and where improvements might be needed.

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iambitttu/Cancer-Classification-with-Neural-Network

Implement the project using Python and deep learning frameworks such as TensorFlow/Keras. Utilize libraries like pandas for data manipulation, scikit-learn for data preprocessing and evaluation, and matplotlib/seaborn for visualization.

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BeheshteSadeghi/ChurnModelling

This project aims to train the best model for predicting customer churn. In this project; first, data is studied and several diagrams are depicted for storytelling. Since we face with unbalanced data, categorical data, outliers, unscaled data and ecxess of features, related packages from python is utilized to prepare data for modelling.

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themihirmathur/Soiligator

Soiligator is an advanced machine learning project designed to optimize irrigation management by predicting whether irrigation is necessary based on environmental and soil-related data.

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Ali-jalil88/Mlflow-DL-ANN

Gender Classification Using ANN with MLflow

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Anupreet02/CryptoClustering

This module analyzes and clusters a dataset of cryptocurrencies based on price change percentages over different timeframes. Using K-means clustering, Principal Component Analysis (PCA), and the elbow method, this project aims to find optimal clusters for understanding cryptocurrency behavior.

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Shyamkumarnagilla/Diabetes-Prediction-using-K-Nearest-Neighbors-classification

This project predicts diabetes risk in patients using the K-Nearest Neighbors (KNN) classification algorithm. By analyzing health data, the model assists in early diabetes detection, providing insights that support preventive care.

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Logeshwaran-KS/Medicinal-Plants-Detection-Using-Machine-Learning

Medicinal Plants Detection

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Fahrettinsolak/AI-Map-Based-Geographic-Clustering-Project

This project focuses on clustering crime incidents in San Francisco using the K-Means algorithm. The dataset is obtained from Kaggle and contains information about crime types, geographical coordinates, and other relevant features. The goal is to identify crime hotspots through geographic clustering and visualize the clusters on an interactive map.

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harmanveer-2546/Ad-Click-Prediction-Analysis-and-Insights

To predict whether a user will click on ad or not.

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Awakk3n/Fraud-Detection-ML-Model

Online Fraud Detection ML Model via XGBoost Classifier

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harmanveer-2546/NBA-HOMECOURT-ADVANTAGE-DATA-ANALYSIS

In this repo we're diving into NBA game data to explore the phenomenon of home court advantage.

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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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AbhinavSinha02/Music-suggestion-system

This project is a music recommendation system that uses cosine similarity and StandardScaler to provide personalized music recommendations. The system uses collaborative filtering to recommend songs to users based on their listening history and preferences.

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tejasayya/Alzheimer-s-Disease-Analysis

A research study on How do factors like alcohol consumption, age, ethnic background, and medical history affect the risk of developing Alzheimer's disease?

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harmanveer-2546/Mystery-Planet

Exoplanet Hunting in Deep Space.

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yashika-malhotra/Machine-Learning---Linear-regression-on-Education-Institute

In this analysis, I built a model to predict graduate admissions using Linear, Ridge, Lasso, and ElasticNet regressions. CGPA, GRE, and TOEFL scores emerged as key predictors. ElasticNet effectively handled multicollinearity and balanced L1 and L2 regularization.

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5hraddha/interconnect

Interconnect : Clients Churn Prediction using ML

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williakn3/Machine-Learning-Project--2

Machine Learning Project

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jElhamm/Singular-Value-Decomposition-Data-Mining

"This repository hosts an implementation of the Singular Value Decomposition (SVD) algorithm tailored for data mining tasks. SVD is utilized for efficient dimensionality reduction, aiding in the extraction of key patterns and features from large and complex datasets."

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harmanveer-2546/Finding-you-next-perfect-house

In this exploratory data analysis, we compare a dataset which consists of various features about renting of houses available on these renting platforms listed by owners of these houses, and try to derive some constructive conclusions by performing Descriptive statistics of the available features.

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harmanveer-2546/Prediction-Of-Ticket-Cancellation

The objective is to develop a model that accurately predicts whether users will cancel their tickets. Each cancellation incurs a fine for the ticket registration site from the passenger company.

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Md-Emon-Hasan/Data_Preprocessing

A comprehensive collection of scripts and techniques for efficient data preprocessing in data analysis and machine learning projects.

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muhammad3245571106/Student-Grade-Prediction

This is the Data Mining Porject for predicting the grade of student before the final examination and before the Mid 2 examination. I use Python and Jupyter Notebook for this Project.

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Kishan1807/Data-Cleaning-and-Best-model-selection-

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jmarihawkins/neural-network-challenge-1

The purpose of this project is to predict student loan repayment success using a neural network. Neural networks are computational models inspired by the human brain's structure and function, consisting of layers of interconnected nodes or "neurons" that can learn to recognize patterns in data.

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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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isadays/BayesianInference

The model predicts the treatment success rate for new TB cases with high accuracy and robustness. Two different approaches: PCA and Bayesian Inference. The Bayesian regression analysis reveals that c_new_sp_tsr and new_sp_fail are significant predictors of the treatment success rate, while other predictors show less certainty in their effects.

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m-rishab/Job-recruitment-prediction-and-HR-Dashboard-using-plotly

This project features make it ideal for dynamic HR dashboards, offering insights into candidate profiles and recruitment processes.

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ThatCoryGirl/unsupervised-ml-challenge

Python, unsupervised machine learning

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Bramitha-gowda-M/T20-world-cup-prediction-system

T20 World Cup Prediction System -- This GitHub repository contains the code for a T20 World Cup prediction system implemented in Python. The project utilizes popular libraries such as pandas, NumPy, and XGBoost for data manipulation, cleaning, and building predictive models.

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alaa-aleryani/Deep_Learning_Challenge

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Simrank23/Houses-Price-Prediction

Using Python I built a house price prediction model. By analyzing various factors like Income, total rooms, total bedrooms, and other features. I processed and cleaned the data, performed feature engineering, and trained a machine learning algorithm to make accurate predictions.

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Shipra-09/Project-Vehicle-Insurance

This Github repository contains cross selling of health insurance customers on vehicle insurance product. We have to predict whether a customer would be interested in Vehicle Insurance or not by building a ML model. Exploring Insights/Inferences by performing EDA on the given project data. Finding the high accuracy

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RiteshopShrivastava/Hierarchical_Clustering

Unsupervised-ML---K-Means-Clustering-Non-Hierarchical-Clustering-Univ. Use Elbow Graph to find optimum number of clusters (K value) from K values range. The K-means algorithm aims to choose centroids that minimise the inertia, or within-cluster sum-of-squares criterion WCSS. Plot K values range vs WCSS to get Elbow graph for choosing K (no. of clusters)

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alaa-aleryani/CryptoClustering

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saikrishnabudi/Random-Forests

Use Random Forest to prepare a model on fraud data. Treating those who have taxable income <= 30000 as "Risky" and others are "Good" and A cloth manufacturing company is interested to know about the segment or attributes causes high sale.

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saikrishnabudi/K-Nearest-Neighbour-KNN

Prepare a model for glass classification using KNN and Implement a KNN model to classify the animals in to categories.

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Luiggi-piero/python-sklearn-curso-5

Machine Learning clasificación con SKLearn

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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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Shipra-09/ML-Project-K-Means-Clustering

This Github repository contains projects related to K-means Clustering. Exploring Insights/Inferences by performing EDA on the given project data (Airlines data).

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Shipra-09/ML-Project-Random-Forest

This Github repository contains projects related to prediction with Random Forest Classifier. Exploring Insights/Inferences by performing EDA on the given project data (Company Sales).

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Shipra-09/ML-Project-Decision-Tree

This Github repository contains projects related to prediction with Decision Tree. Exploring Insights/Inferences by performing EDA on the given project data (Iphone purchase).

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Shipra-09/ML-Project-KNN-Classification

This Github repository contains projects related to KNN classification. Exploring Insights/Inferences by performing EDA on the given project data (Iphone purchase and Bangalore house price).

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Asmagithu/Prediction-with-Binomial-Logistic-Regression-19th-November

This repo includes Prediction with Binomial Logistic Regression.

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PankajVispute/I-phone-purchase-project--Prediction-with-KNN-Classification

Prediction of customer will purchase iPhone or not using KNN classifier model and multiple supervised ML model.

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SINGHxTUSHAR/Forest-Fire-Prediction-RidgeRegression

This model predicts the forest fire by using the Ridge-Regression algorithm.

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shaloy-lewis/cardiovascular_risk_prediction

Cardiovascular Risk Prediction

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Wayneotc/Heart-Failure

This repository contains the code and data for a comprehensive survival analysis and prediction study conducted on patients with advanced heart failure. The study focused on 299 patients classified as class III/IV heart failure.

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Warpedro1/Machine-learning-behind-the-curtain

This dataset was used to learn more about how some machine learning models work: KNN, Naive Bayes, and Decision Tree. It also includes some model evaluation metrics: Precision, Recall, Accuracy, and F1-Score. These metrics were derived from the confusion matrix.

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chiomauche/CryptoClustering

The purpose of the study was to predict if cryptocurrencies can be affected by a 24 hour or 7 day price changes

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chiomauche/deep-learning-analysis

The purpose of the study is to create a binary classifier that can predict whether applicants will be successful if funded by Alphabet Soup

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vaitybharati/K-means

K-means

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vaitybharati/DB-Scan

DB-Scan

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patelpurvip/Spotify-hitpredictor

A machine learning exercise using the Spotify "hit predictor" dataset, with data analysis of past "hits" by decade. Deployment using Flask via Heroku.

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Sachin-crypto/Model_Evaluation_After_Standardization

The KNN model is evaluated on the sklearn's breast cancer dataset to check if the accuracy of the model is impacted when the features of the dataset is standardized (scaled) using StandardScaler.

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helenaschatz/CryptoClustering

The main objective of this project was to explore, evaluate and discover valuable insights, by leveraging the power of unsupervised learning to predict if cryptocurrencies are affected by 24-hour or 7-day price changes.

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JeremyTallant/unsupervised-machine-learning-challenge

Using unsupervised machine learning techniques for clustering a dataset of patients with myopia.

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vmueller13/CryptoClustering

Utilize sklearn to predict the changes in cryptocurrencies in a given time period.

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GunturWibawa/SpotifyPopularityProbe

In the digital music era, understanding artist popularity on Spotify is vital. This project taps into Spotify's data, analyzing key factors driving artist prominence. Through our insights, we illuminate what sets successful artists apart in this dynamic platform.

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NotShrirang/Machine-Learning-from-Scratch

ML / DL Algorithms implemented from scratch. Developed with only numpy as dependency. Machine Learning Algorithms such as Support Vector Machine, Linear Regression, Artificial Neural Networks and other data transformation algorithms are implemented. Project is released as a python package and can be download from Python Package Installer.

Language: Python - Size: 86.9 KB - Last synced at: 25 days ago - Pushed at: about 2 years ago - Stars: 3 - Forks: 1

rahulg-101/Ads-Click-Through-Rate

In this project, I have developed a Machine Learning model to predict whether users will click on ads. By analyzing various characteristics of users who click on ads, we can gain valuable insights and optimize ad campaigns for better engagement.

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suyinwb/Cryptocurrencies

The data Martha will be working with is not ideal, so it will need to be processed to fit the machine learning models. Since there is no known output for what Martha is looking for, she has decided to use unsupervised learning. To group the cryptocurrencies, Martha decided on a clustering algorithm. She’ll use data visualizations to share her findings with the board.

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suyinwb/Neural_Network_Charity_Analysis

From Alphabet Soup’s business team, Beks received a CSV containing more than 34,000 organizations that have received funding from Alphabet Soup over the years. Within this dataset are a number of columns that capture metadata about each organization

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hanydief/CryptoClustering

Using Python and unsupervised machine learning to predict if cryptocurrencies are affected by 24-hour or 7-day price changes.

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margaretkhendre/Nonprofit-Funding-vs-Deep-Learning-Challenge

In this repository, Google Colab is paired with neural networks and deep learning to examine data from the nonprofit, Alphabet Soup. This nonprofit desires a tool that can help it select applicants for funding, so a binary classifier will be created to predict whether applicants will be successful if funded by Alphabet Soup.

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Annbelbella/CryptoClustering

Using Python and unsupervised machine learning to predict if cryptocurrencies are affected by 24-hour or 7-day price changes .

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iamkirankumaryadav/Predictive-Analytics

Predictive Analytics

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iamkirankumaryadav/Algorithms

Machine Learning with Scikit Learn

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Palemravichandra/Electricity-Prediction

Electrical_Power_Generation_Prediction

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Palemravichandra/Laptop_Price_Prediction

Laptop_Price_Prediction

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JLeigh101/deep-learning-challenge

NU Bootcamp Module 21

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chadbarlow/gt-data-bootcamp_challenge-19_unsupervised-ml

Unsupervised machine learning for predicting cryptocurrency market trends

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ddS08/Detecting-Parkinsons-Disease

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rochitasundar/Stock-clustering-using-ML

The project involves performing clustering analysis (K-Means, Hierarchical clustering, visualization post PCA) to segregate stocks based on similar characteristics or with minimum correlation. Having a diversified portfolio tends to yield higher returns and face lower risk by tempering potential losses when the market is down.

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