Topic: standard-scaler Goto Github
Some thing interesting about standard-scaler
Some thing interesting about standard-scaler
standard-scaler,Collection of Regression models with maximum accuracy [.98] to predict Dimond price
User: abdulrahmankhaled11
Home Page: https://www.kaggle.com/code/abdulrahmankhaled1/diamond-price-prediction
standard-scaler,Perform clustering (K means clustering) for the airlines data to obtain optimum number of clusters. Draw the inferences from the clusters obtained. Data Description: The file EastWestAirlinescontains information on passengers who belong to an airline’s frequent flier program. For each passenger the data include information on their mileage history and on different ways they accrued or spent miles in the last year. The goal is to try to identify clusters of passengers that have similar characteristics for the purpose of targeting different segments for different types of mileage offers ID --Unique ID Balance--Number of miles eligible for award travel Qual_mile--Number of miles counted as qualifying for Topflight status cc1_miles -- Number of miles earned with freq. flyer credit card in the past 12 months: cc2_miles -- Number of miles earned with Rewards credit card in the past 12 months: cc3_miles -- Number of miles earned with Small Business credit card in the past 12 months: 1 = under 5,000 2 = 5,000 - 10,000 3 = 10,001 - 25,000 4 = 25,001 - 50,000 5 = over 50,000 Bonus_miles--Number of miles earned from non-flight bonus transactions in the past 12 months Bonus_trans--Number of non-flight bonus transactions in the past 12 months Flight_miles_12mo--Number of flight miles in the past 12 months Flight_trans_12--Number of flight transactions in the past 12 months Days_since_enrolled--Number of days since enrolled in flier program Award--whether that person had award flight (free flight) or not
User: abhik35
standard-scaler, Prepare a classification model using Naive Bayes for salary data
User: abhik35
standard-scaler,predicting breast cancer using machine learning models
User: akash1070
standard-scaler,To forecast the success of Alphabet Soup funding applicants, I will develop a binary classification model utilizing a deep neural network.
User: anvithachaluvadi
standard-scaler,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.
User: asimchakraborty
standard-scaler,Project is about predicting Class Of Beans using Supervised Learning Models
User: bharatkulmani
standard-scaler,This is Date Fruit Data taken from Kaggle. This data severs a classification problem to solved. Using various features of the fruit classify the fruit to its type.
User: datarohit
Home Page: https://www.kaggle.com/code/datarohitingole/catboostclassifier-logisticregressioncv-90
standard-scaler,Examples of techniques that can be used to optimize neural network models (some techniques can apply more generally).
User: emeralddawns
standard-scaler,NBA Games stats simulator & predictor : Predict tomorrow games results and consult past games statistics
User: endy02
standard-scaler,Eric-Simon-Neural-Network-Challenge
User: ericlsimon
standard-scaler,Using supervised machine learning models to determine credit worthiness
User: gpawlows
standard-scaler,clustering with crypto!
User: gpawlows
standard-scaler,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.
User: helenaschatz
standard-scaler,GridSearchCV For Model optimization
User: hohasby
standard-scaler,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.
User: iamjr15
standard-scaler,NU Bootcamp Module 19
User: jleigh101
standard-scaler,Model predicting whether a bank customer will churn or not
User: jodiambra
Home Page: https://jodiambra.github.io/Beta-Bank-Churn-Predictions/
standard-scaler,Using supervised machine learning models to determine credit worthiness
User: jordanjaner
standard-scaler,Clustered cryptocurrencies with K-means algorithm
User: kimco2
standard-scaler,Analysis of Terry Stops in Seattle
User: melodygr
standard-scaler,Feature_Scaling_Normalization_MinMaxScaling_MaxAbsScaling_RobustScaling
User: nani757
standard-scaler,import datasets, perform exploratory data analysis, scaling & different models such as linear or logistic regression, decision trees, random forests, K means, support vectors etc.
User: nmathias0121
standard-scaler,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.
User: octavioduarte
standard-scaler,Used libraries and functions as follows:
User: patilsukanya
standard-scaler,Credit Card Approval Prediction using Logistic Regression model
User: pranjalinaik11
standard-scaler,Data Science - Clustering Work
User: saikrishnabudi
standard-scaler,Data Science - PCA (Principal Component Analysis)
User: saikrishnabudi
standard-scaler,ML model to predict whether the user has diabetes.
User: sarahcodebyte
standard-scaler,ML model to predict whether the person has Parkinson's Disease.
User: sarahcodebyte
standard-scaler,This project involves predicting used car prices using linear regression in Jupyter Notebook. Used car price prediction is an important task in the automotive industry, as it helps estimate the value of pre-owned vehicles based on various factors such as mileage, brand, age, etc.
User: shaadclt
standard-scaler,This project involves the prediction of salary based on position using Support Vector Regression (SVR) in Jupyter Notebook. The dataset contains information about different positions and their corresponding salaries. Through this analysis, we aim to build a regression model that accurately predicts the salary based on the given position.
User: shaadclt
standard-scaler,Perform Clustering (Hierarchical, K Means Clustering and DBSCAN) for the airlines and crime data to obtain optimum number of clusters. Draw the inferences from the clusters obtained.
User: shanuhalli
standard-scaler,Predict the Burned Area of Forest Fire with Neural Networks and Predicting Turbine Energy Yield (TEY) using Ambient Variables as Features.
User: shanuhalli
standard-scaler,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.
User: shanuhalli
standard-scaler, Our goal is to look through this dataset and classify songs
User: shivam-ray
standard-scaler,Preprocess data for PCA, Reduce Data Dimensions using PCA, Clustering Cryptocurrencies using K-Means, and Visualizing Cryptocurrencies Results.
User: sjwedlund
standard-scaler,Non-profit foundation funding predictor using deep learning and neural networks.
User: tmard
standard-scaler,Assignment-07-DBSCAN-Clustering-Crimes. Perform Clustering for the crime data and identify the number of clusters formed and draw inferences.
User: vaitybharati
standard-scaler,Assignment-07-K-Means-Clustering-Airlines. Perform clustering (K means clustering) for the airlines data to obtain optimum number of clusters. Draw the inferences from the clusters obtained. The file EastWestAirlinescontains information on passengers who belong to an airline’s frequent flier program. For each passenger the data include information on their mileage history and on different ways they accrued or spent miles in the last year. The goal is to try to identify clusters of passengers that have similar characteristics for the purpose of targeting different segments for different types of mileage offers.
User: vaitybharati
standard-scaler,Exploratory Data Analysis Part-1
User: vaitybharati
standard-scaler,EDA (Exploratory Data Analysis) -1: Loading the Datasets, Data type conversions,Removing duplicate entries, Dropping the column, Renaming the column, Outlier Detection, Missing Values and Imputation (Numerical and Categorical), Scatter plot and Correlation analysis, Transformations, Automatic EDA Methods (Pandas Profiling and Sweetviz).
User: vaitybharati
standard-scaler,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)
User: vaitybharati
standard-scaler,Unsupervised-ML---DBSCAN-Clustering-Wholesale-Customers. Import Libraries, Import Dataset, Normalize heterogenous numerical data using standard scalar fit transform to dataset, DBSCAN Clustering, Noisy samples are given the label -1, Adding clusters to dataset.
User: vaitybharati
standard-scaler,FastAPI create a machine learning from model iris resful API
User: watcharap0n
standard-scaler,This repository contains clustering techniques applied to minute weather data. It contains K-Means, Heirarchical Agglomerative clustering. I have applied various feature scaling techniques and explored the best one for our dataset
User: y656
standard-scaler,
User: zauverer
standard-scaler,
User: zauverer
standard-scaler,
User: zauverer
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