sklearn-library

A library that unifies the API for most commonly used libraries and modeling techniques for time-series forecasting in the Python ecosystem.

AutoML - Hyper parameters search for scikit-learn pipelines using Microsoft NNI

Problem Statement: Given the tweets from customers about various tech firms who manufacture and sell mobiles, computers, laptops, etc, the task is to identify if the tweets have a negative sentiment towards such companies or products.

Exploring sklearn 🌟

SVR for multidimensional labels

QuickCNN is high-level library written in Python, and backed by the Keras, TensorFlow, and Scikit-learn libraries. It was developed to exercise faster experimentation with Convolutional Neural Networks(CNN). Majorly, it is intended to use the Google-Colaboratory to quickly play with the ConvNet architectures. It also allow to train on your local system.

企业信用分析平台（EEAP）

Implementation of Linear regression on Boston House Pricing and Diabetes data sets using python.

Machine Learning/Pattern Recognition Models to analyze and predict if a client will subscribe for a term deposit given his/her marketing campaign related data

Application prepares data to learning process. Including preprocessing, cleaning, reformating, feature extraction using PyEEG library and learning using Sklearn tool.

A Machine Learning challenge #6 - "Predict the damage to a building", organised by Hacker earth. Rank: 242 out of 7540 participants.

kaggle 比赛 使用sklearn进行kaggle数据竞赛基础及实践

Python Data Analytics, Machine Learning & Natural Language Processing

A machine learning model which predicts the gender based on the data with which it is trained.

Building a iOS Application using Apple's Core ML Framework, we will builed a Linear SVC model using sklearn library on the SMS Data, users will type a message on their iPhone and our model will then identify whether the message is a Spam or a Ham (Not spam).

In this project, we have detected the malicious URLs using lexical features and boosted machine learning algorithms

Decision Tree Classifier model implemented in a python program.

Machine Learning 100 Days Challenge

A hackathon challenge solved using NLP where we actually try to predict the correct name of the recipe!

Uber Technologies, Inc., commonly known as Uber, is an American technology company. Its services include ride-hailing, food delivery (Uber Eats), package delivery, couriers, freight transportation, and, through a partnership with Lime, electric bicycle and motorized scooter rental. The company is based in San Francisco and has operations in over 900 metropolitan areas worldwide. It is one of the largest firms in the gig economy. so that i Make this Project so company can pred there weekly as well as monthly rides pred that can company help to mange there rides info correctly

People often save their money in the banks which offer security but with lower interest rates. Lending Club operates an online lending platform that enables borrowers to obtain a loan, and investors to purchase notes backed by payments made on loans. It is transforming the banking system to make credit more affordable and investing more rewarding. But this comes with a high risk of borrowers defaulting the loans. Hence there is a need to classify each borrower as defaulter or not using the data collected when the loan has been given.

Recommender Systems Evaluation using various algorithms and techniques.

Machine Learning algorithms implementation using Python

Fake news detection using Naïve Bayes in Python along with confusion matrix calculated using sklearn.

It is a nationality and personality detection based on machine learning.

This is what is called by the much talked about term, Big Data. The basis to any attempt to answer the question of which to learn first between Data Science or Machine Learning should be Big Data. Why this is so is very simple. It is on Big Data that both Data Science and Machine Learning are built.

It is a python machine learning model that predicts the price of a second hand car using different models like linear regression, random forest, gradient boosting, SVM It also uses libraries like pandas, numpy, matplotlib, seaborn to import datasets, plot graphs, remove outliers and clean the dataset for training and testing the model for better accuracy

A minimalist platform for learning, understanding and realising Iris Flower Classification.🌸

Text classification using Naive Bayes Algorithm¶