Demonstrating different Machine Learning Model
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Updated
Aug 23, 2021 - Jupyter Notebook
Demonstrating different Machine Learning Model
Credit Card Fraud Detection: Study and Implementation
Compared the metrics and performance of different classification algorithms on Heart Failure dataset from UCI ML Repository
Using Machine Learning to rank a list of customers most likely to buy a Car Insurance for a cross-sell campaign.
A Python library of 'old school' machine learning methods such as linear regression, logistic regression, naive Bayes, k-nearest neighbors, decision trees, and support vector machines.
Data Science - K-Nearest Neighbors (KNN) Work
This repository has a code (function) for K-Nearest Neighbours models. The model is tested on a dataset and compared with the slkearn KNN models. There is runtime analysis and accuracy analysis of the sklearn KNN models for classification and regression.
In this project the data is been used from UCI Machinery Repository. Main aim of this project is to predict telling tumor of each patient is Benign (class – 2) or Malignant (class – 4) the models used are – Decision tree Classification, Logistic Regression, K-Nearest Neighbors, SVM, Kernel SVM, Naïve-Bayes and Random Forest Classification.
Flight_Price Prediction using Machine Learning.(Regression Use Case)
This is a repository to document my progress in learning the basics of common machine learning algorithms.
In this repository I am gonna show the main and most popular non-supervised clustering algorithms with short explanations.
The purpose of this project is to promote understanding -- my own and others' -- of fundamental data science and machine learning concepts and tools. It currently consists of one notebook that classifies fruit types based on weight, volume, and image data.
Exploring the world of Bioinformatics using various machine learning algorithms
To check the data belongs to which class of Iris plant. (Famous data Set: 'Iris.csv')
Created a Python program for K Nearest Neighbor Algorithm implementation from scratch. Determined the Euclidean distance between the data points to classify a new data point as per the maximum number of nearest neighbors. Implemented the algorithm on sklearn’s IRIS dataset which achieved an accuracy of 95.56%.
Build a Predictive model to forecast the sales of Rossmann store and uploaded the project to the challenge put forth by Rossmann Store
Implementation of the The k-nearest neighbors (KNN) algorithm. A machine learning algorithm that can be used to solve both classification and regression problems.
Data preprocessing of raw airline data and predicting prices through various different regression algorithms. Also dumping the model and reusing it for new data.
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