Data Science - PCA (Principal Component Analysis)
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Updated
Jan 7, 2024 - Jupyter Notebook
Data Science - PCA (Principal Component Analysis)
The goal of this machine learning project is to predict the selling price of a new home by applying basic machine learning (regression)concepts to the housing prices data.
Using machine learning and neural networks, utilizing the features in the provided dataset to create a binary classifier that can predict whether applicants will be successful if funded by Alphabet Soup.
Eric-Simon-Neural-Network-Challenge
Our goal is to look through this dataset and classify songs
Feature_Scaling_Normalization_MinMaxScaling_MaxAbsScaling_RobustScaling
predicting breast cancer using machine learning models
Using supervised machine learning models to determine credit worthiness
ML model to predict whether the person has Parkinson's Disease.
import datasets, perform exploratory data analysis, scaling & different models such as linear or logistic regression, decision trees, random forests, K means, support vectors etc.
Models bank loan applications to classify and predict approval decisions using customer demographic, financial, and loan data. Applies machine learning algorithms like logistic regression and random forest for enhanced automation.
GridSearchCV For Model optimization
Non-profit foundation funding predictor using deep learning and neural networks.
Examples of techniques that can be used to optimize neural network models (some techniques can apply more generally).
Example of classification using the RandomForest algorithm, with visual exploratory analysis using seaborn and matplotlib plots, and data normalization using One-Hot Encoding and StandardScaler. Covered in the datascienceacademy course.
clustering with crypto!
NBA Games stats simulator & predictor : Predict tomorrow games results and consult past games statistics
Preprocess data for PCA, Reduce Data Dimensions using PCA, Clustering Cryptocurrencies using K-Means, and Visualizing Cryptocurrencies Results.
Add a description, image, and links to the standard-scaler topic page so that developers can more easily learn about it.
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