grid-search-cv

shahriar-rahman/Netflix-Customer-Retention-using-GPR

Forecasting Netflix Customer Retention based on Gaussian Process Regression

ahing/airbnb-feature-analysis

Using a dataset provided by Airbnb, analysis and predictions will be made to understand what effects the total price of an Airbnb

amit-timalsina/California-Housing-Price-Prediction

I have built a Model using the Random Forest Regressor of California Housing Prices Dataset to predict the price of the Houses in California.

Ankit-Kumar-Saini/Machine-Learning-Lab-Plaksha

Programming assignments covering fundamentals of machine learning and deep learning. These were completed as part of the Plaksha Tech Leaders Fellowship program.

copev313/Predicting-Credit-Card-Approvals

A machine learning model built to predict if a credit card application will get approved.

murtadapy/disaster-response-pipelines

Disaster response project that implements data engineering tactics to classify messages sent during a real-world disaster. This project uses ETL and ML pipelines and uses the Flask library to deploy the final result on a website.

murtadapy/iris-flower-classification

Analyze and Build a machine learning (ML) model on the Iris Flower dataset

prithvimurjani/ML-Utility-Repo

A repo packed with common and important machine learning techniques and algorithm implementations using sklearn.

shanuhalli/Assignment-Decision-Trees

Use decision trees 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.

shanuhalli/Assignment-KNN

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

shanuhalli/Assignment-Random-Forest

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.

niklas-joh/Machine-Learning-Project-Supervised-Learning

Identify the most efficient machine learning model to identify potential donors. Project covers Linear Regression, Perceptron Algorithm, Decision Trees, Naive Bayes, Support Vector Machines and Ensemble Methods.

PriyeshDave/Accident-Severity-Predictor

RTA severity predictor is an application which predicts the severity of road traffic accident, so as to pave the way for improving the safety level of road traffic.

PriyeshDave/Site-Energy-Intensity-Predictor

In this project, a regression-based performance prediction model was developed to estimate building energy consumption based on simplified façade attribute information and weather conditions.

Seghelicious/Cars45

Akash1070/Machine-Learning-

Learning Machine Learning Through Data

Ayomikun17/Diabetes-Prediction-

Diabetes Prediction with Tree based models (Random Forest and XGBoost). Grid Search CV and Randomized Search CV used to optimize parameters

claudiamartinez14/Loan-Default-Prediction-Machine-Learning-Project

The "Potential Customers Prediction" project uses machine learning to predict which customers are likely to make a purchase. It involves data cleaning, exploratory analysis, and building models to identify potential buyers based on demographics and behavior. The model's performance is evaluated using metrics like accuracy and precision.

Davityak03/Ridge-and-Lasso-Regression

Implented ridge and lasso regression by understanding the use of parameters

existentialplantperson/Week_14

Week 14 - Multiple Linear Regression and Logistic Regression

Faisal-AlDhuwayhi/Disaster-Response-Pipeline

Building Machine Learning and ETL Pipelines to categorize emergency messages based on the needs communicated by the sender

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.

Lefteris-Souflas/Modern-Slavery-Analysis

Jupyter notebook using machine learning techniques to explore the complex drivers of modern slavery. Models from a research paper are replicated and evaluated . Actions also include filling missing data, training regression models, and analyzing feature importance.

ofir-frd/Prediction-of-Music-Genre

Classify music into genres by classical machine learning models

Zauverer/Support_Vector_Machine