Clustering of the company's clients and a little analytics

Using the features in the provided dataset, creating a binary classifier that can predict whether applicants will be successful if funded by Alphabet Soup.

Module 19 Challenge uses unsupervised machine Learning to predict if Cryptopcurrencies are affected by 24hour or 7day price changes. Normalise data, K-Means Clustering, elbow method and PCA analysis.

Machine Learning clasificación con SKLearn

Correlations, NumPy, Pandas, Seaborn, PCA, Matplotlib, Scatterplot

Exploring machine learning with nueral networks for a charity analysis. Adjusting the model to try and improve accuracy to predict which projects are likely to be successful.

pca description and covering its various dimension along with knn

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

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

This Jupyter Notebook serves as a comprehensive guide to performing support vector machine (LinearSVC) classification and calculating accuracy scores for machine learning tasks. It provides step-by-step instructions and code examples for building, training, and evaluating a LinearSVC classifier

Data Science - Random Forest Work

The feature engineering techniques discussed are - dimensionality reduction(pca), scaling(standard scaler, normalizer, minmaxscaler), categorical encoding(one hot/dummy), binning, clustering, feature selection. These are techniques performed on a dataset consisting of Californian House Prices.

Using supervised machine learning to predict credit risk. Trying oversampling, under sampling, combination sampling and ensemble learning to find the model with the best fit

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.

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.

Advertisement Expenditure analysis using Linear Regression

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

This repo includes Prediction with Binomial Logistic Regression.