standard-scaler | Topic | Ecosyste.ms: Repos

Topic: "standard-scaler"

shaadclt/Car-Price-Prediction-LinearRegression

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.

Language: Jupyter Notebook - Size: 198 KB - Last synced at: 29 days ago - Pushed at: almost 2 years ago - Stars: 3 - Forks: 0

shaadclt/Salary-Prediction-SupportVectorRegressor

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.

Language: Jupyter Notebook - Size: 27.3 KB - Last synced at: 29 days ago - Pushed at: almost 2 years ago - Stars: 3 - Forks: 0

KhaledTofailieh/Titanic-Survive-Prediction

Language: Jupyter Notebook - Size: 4.88 KB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 3 - Forks: 0

bharatkulmani/Dry-Bean

Project is about predicting Class Of Beans using Supervised Learning Models

Language: Jupyter Notebook - Size: 35.3 MB - Last synced at: almost 2 years ago - Pushed at: about 2 years ago - Stars: 2 - Forks: 0

shanuhalli/Assignment-Clustering

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.

Language: Jupyter Notebook - Size: 5.49 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 2 - Forks: 1

vaitybharati/P23.-EDA-1

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).

Language: Jupyter Notebook - Size: 215 KB - Last synced at: over 1 year ago - Pushed at: almost 4 years ago - Stars: 2 - Forks: 1

PranjaliNaik11/Prediction_Credit_card_approval

Credit Card Approval Prediction using Logistic Regression model

Language: Jupyter Notebook - Size: 105 KB - Last synced at: 12 months ago - Pushed at: 12 months ago - Stars: 1 - Forks: 0

Abdulrahmankhaled11/Diamond-Price-Prediction

Collection of Regression models with maximum accuracy [.98] to predict Dimond price

Language: Jupyter Notebook - Size: 937 KB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 1 - Forks: 0

shanuhalli/Assignment-Neural-Networks

Predict the Burned Area of Forest Fire with Neural Networks and Predicting Turbine Energy Yield (TEY) using Ambient Variables as Features.

Language: Jupyter Notebook - Size: 2.6 MB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 1 - Forks: 0

y656/Weather-data-clustering

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

Language: Jupyter Notebook - Size: 241 KB - Last synced at: over 1 year ago - Pushed at: almost 3 years ago - Stars: 1 - Forks: 1

DataRohit/Date-Fruit-Classification

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.

Language: Jupyter Notebook - Size: 291 KB - Last synced at: 17 days ago - Pushed at: almost 3 years ago - Stars: 1 - Forks: 0

vaitybharati/P31.-Unsupervised-ML---DBSCAN-Clustering-Wholesale-Customers-

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.

Language: Jupyter Notebook - Size: 4.88 KB - Last synced at: over 1 year ago - Pushed at: almost 4 years ago - Stars: 1 - Forks: 0

vaitybharati/Assignment-07-DBSCAN-Clustering-Crimes-

Assignment-07-DBSCAN-Clustering-Crimes. Perform Clustering for the crime data and identify the number of clusters formed and draw inferences.

Language: Jupyter Notebook - Size: 23.4 KB - Last synced at: over 1 year ago - Pushed at: almost 4 years ago - Stars: 1 - Forks: 0

vaitybharati/P30.-Unsupervised-ML---K-Means-Clustering-Non-Hierarchical-Clustering-Univ.-

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)

Language: Jupyter Notebook - Size: 72.3 KB - Last synced at: over 1 year ago - Pushed at: almost 4 years ago - Stars: 1 - Forks: 0

vaitybharati/Assignment-07-K-Means-Clustering-Airlines-

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.

Language: Jupyter Notebook - Size: 90.8 KB - Last synced at: over 1 year ago - Pushed at: almost 4 years ago - Stars: 1 - Forks: 1

vaitybharati/EDA-1

Exploratory Data Analysis Part-1

Language: Jupyter Notebook - Size: 2.93 KB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 1 - Forks: 0

SunnyRao07/stroke-risk-prediction

Predicting stroke risk using machine learning models based on healthcare and demographic data.

Language: Jupyter Notebook - Size: 1.63 MB - Last synced at: 10 days ago - Pushed at: 13 days ago - Stars: 0 - Forks: 0

SunnyRao07/Water-Quality-Analysis

A machine learning project that predicts water potability based on chemical and physical attributes, using models like Logistic Regression, Random Forest, and XGBoost.

Language: Jupyter Notebook - Size: 584 KB - Last synced at: 10 days ago - Pushed at: 13 days ago - Stars: 0 - Forks: 0

JaspreetSingh-exe/Vehicle-Price-Prediction

Vehicle Price Prediction is a machine learning project that estimates vehicle prices using features like make, model, year, mileage, and more. It employs multiple regression models, including Linear Regression, Random Forest, Gradient Boosting, CatBoost, and Stacking Regressor, with GridSearchCV for tuning.

Language: Jupyter Notebook - Size: 1.17 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

Kidaha12/CryptoClustering

Language: Jupyter Notebook - Size: 65.4 KB - Last synced at: 8 months ago - Pushed at: 8 months ago - Stars: 0 - Forks: 0

blleshi/Neural_Network_Binary_Classification

Venture Funding with Deep Learning (Neural Network Binary Classification)

Language: Jupyter Notebook - Size: 278 KB - Last synced at: about 2 months ago - Pushed at: 9 months ago - Stars: 0 - Forks: 0

SkredX/Market-analysis-and-optimization-using-Clustering

The project uses data preprocessing steps, such as handling missing values, encoding categorical variables, and standardizing features. It applies the K-Means clustering algorithm and visualizes the results using various libraries like Matplotlib, Seaborn, and Plotly.

Language: Jupyter Notebook - Size: 1.29 MB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0

octavioduarte/RandomForest

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.

Language: Jupyter Notebook - Size: 374 KB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0

jmarihawkins/CryptoClustering

This project aims to cluster various cryptocurrencies based on their market performance using machine learning techniques. The analysis involves several key steps: normalizing the data, reducing its dimensionality with Principal Component Analysis (PCA), and using K-Means clustering to identify distinct groups.

Language: Jupyter Notebook - Size: 414 KB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

AnvithaChaluvadi/Venture-Funding_Module13Challenge

To forecast the success of Alphabet Soup funding applicants, I will develop a binary classification model utilizing a deep neural network.

Language: Jupyter Notebook - Size: 2.18 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

iamjr15/Bank-Loan-Approval-Prediction

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.

Language: Jupyter Notebook - Size: 83 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

saikrishnabudi/PCA-Principal-Component-Analysis

Data Science - PCA (Principal Component Analysis)

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saikrishnabudi/Clustering

Data Science - Clustering Work

Language: Jupyter Notebook - Size: 4.11 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

emeralddawns/Optimize-Neural-Network

Examples of techniques that can be used to optimize neural network models (some techniques can apply more generally).

Language: Jupyter Notebook - Size: 10.1 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

manjugovindarajan/Trade-Ahead-StockClustering-using-ML

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 faces lower risk by tempering potential losses when the market is down.

Language: Jupyter Notebook - Size: 2.57 MB - Last synced at: 8 months ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

ericlsimon/neural-network-challenge

Eric-Simon-Neural-Network-Challenge

Language: Jupyter Notebook - Size: 11.7 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

tmard/Deep_Learning_Challenge

Non-profit foundation funding predictor using deep learning and neural networks.

Language: Jupyter Notebook - Size: 894 KB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0

pradeshgv/Used_Car_Price_Prediction

Language: Jupyter Notebook - Size: 812 KB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0

kimco2/CryptoClustering

Clustered cryptocurrencies with K-means algorithm

Language: Jupyter Notebook - Size: 1.38 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0

jodiambra/Beta-Bank-Churn-Predictions

Model predicting whether a bank customer will churn or not

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samyakmohelay/Spotify-Hit-Predictor

A Spotify Hit Predictor capable of predicting whether any given track would be a 'Hit' or 'Flop'. By using the Spotify "hit predictor" dataset, with data analysis of past "hits" by decade. Deployment using Flask via Heroku.

Language: Jupyter Notebook - Size: 9.4 MB - Last synced at: almost 2 years ago - Pushed at: about 2 years ago - Stars: 0 - Forks: 0

nmathias0121/ml-model-algorithms

import datasets, perform exploratory data analysis, scaling & different models such as linear or logistic regression, decision trees, random forests, K means, support vectors etc.

Language: Python - Size: 47.9 KB - Last synced at: over 1 year ago - Pushed at: about 2 years ago - Stars: 0 - Forks: 0

AsimChakraborty/House_price_prediction

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.

Language: Jupyter Notebook - Size: 1.85 MB - Last synced at: almost 2 years ago - Pushed at: about 2 years ago - Stars: 0 - Forks: 0

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.

Language: Jupyter Notebook - Size: 2.82 MB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 0 - Forks: 0

shanuhalli/Assignment-KNN

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

Language: Jupyter Notebook - Size: 585 KB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 0 - Forks: 0

shanuhalli/Assignment-PCA

Perform Principal component analysis and perform clustering using first 3 principal component scores both Heirarchial and K Means Clustering and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data.

Language: Jupyter Notebook - Size: 1.69 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0