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.

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.

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.

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).

Abdulrahmankhaled11/Diamond-Price-Prediction

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

bharatkulmani/Dry-Bean

Project is about predicting Class Of Beans using Supervised Learning Models

Abhik35/Assignment-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. 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

Abhik35/Assignments-Naive-Bayes-salarydata

Prepare a classification model using Naive Bayes for salary data

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.

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.

LegallyNotBlonde/Movie_Analysis

Analyzed 5,000+ movies with Pandas and Colab to build a machine learning model predicting movie revenue.

melodygr/Classification_Project

Analysis of Terry Stops in Seattle

PranjaliNaik11/Logistic_Regression_Credit_Card_Approval

Credit Card Approval Prediction using Logistic Regression model

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.

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.

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.

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.

vaitybharati/EDA-1

Exploratory Data Analysis Part-1

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)

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.

watcharap0n/fastapi-model-iris

FastAPI create a machine learning from model iris resful API

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

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.

Kidaha12/CryptoClustering

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.

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.

saikrishnabudi/Clustering

Data Science - Clustering Work

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.

tmard/Deep_Learning_Challenge

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

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.

blleshi/Neural_Network_Binary_Classification

Venture Funding with Deep Learning (Neural Network Binary Classification)

helenaschatz/deep-learning-challenge

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.

saikrishnabudi/PCA-Principal-Component-Analysis

Data Science - PCA (Principal Component Analysis)

sultanazhari/determining-market-value-of-a-car

Rusty Bargain is a used car buying and selling company that is developing an app to attract new buyers. My job as data science is to create a model that can determine the market value of a car.

sultanazhari/works-with-data-masking

An insurance company called "Sure Tomorrow" wants to solve some problems with the help of machine learning. As a Data Science we're Predict the amount of insurance claims that a new client might receive and Protect clients' personal data without breaking the model with masking