-fuel-consumption-and-carbon-dioxide-emission-of-cars-using-simple-linear-regression

we split our data into training and test sets, create a model using training set, evaluate your model using test set, and finally use model to predict unknown value.

-telecommunication-company

you will load a customer dataset related to a telecommunication company, clean it, use KNN (K-Nearest Neighbours to predict the category of customers, and evaluate the accuracy of your model.

40_dasy_of_ai

air-quality-index-analysis

For each pollutant, an AQI value of 100 generally corresponds to a concentration in ambient air equal to the level of the national short-term ambient air quality standard for the protection of public health. AQI values ​​equal to or less than 100 are generally considered satisfactory. When AQI values ​​are above 100, the air quality is unhealthy: first for certain groups of sensitive people, then for everyone as AQI values ​​increase. The AQI is divided into six categories. Each category corresponds to a different level of health problem. Each category also has a specific colour. Colour allows people to quickly determine if the air quality is reaching unhealthy levels in their communities.Now let’s get started with Data Science project on Air Quality Index analysis with Python. I will recommend you to use Kaggle notebook for this task. The reason why I am recommending you to use a Kaggle notebook you will understand at the end of this article, as we are going to use some APIs provided by Kaggle so I hope you will use a Kaggle notebook for the task of Air Quality Index analysis with Python.

amazon-bestselling-books-analysis

amazon-product-reviews-sentiment-analysis

appwriteblog

automobile

awesome-github-profile-readme-templates

This repository contains best profile readme's for your reference.

bibhuti5

blogging-site

breast-cancer-detection

Breast Cancer Detection With ML

building-your-deep-neural-network-step-by-step

Starting September 2020, notebook items in course shells will become Ungraded Labs. Paid learners will be able to access their notebooks in the new Coursera lab environment; Auditors will lose access. We strongly encourage you to download your notebooks if you are auditing this course. You can also upgrade or applying for financial aid to access premium Lab items in your course. For more information, please see this forum link Welcome to your third programming exercise of the deep learning specialization. You will implement all the building blocks of a neural network and use these building blocks in the next assignment to build a neural network of any architecture you want. By completing this assignment you will: - Develop an intuition of the over all structure of a neural network. - Write functions (e.g. forward propagation, backward propagation, logistic loss, etc...) that would help you decompose your code and ease the process of building a neural network. - Initialize/update parameters according to your desired structure. This assignment prepares you well for the upcoming assignment. Take your time to complete it and make sure you get the expected outputs when working through the different exercises. In some code blocks, you will find a "#GRADED FUNCTION: functionName" comment. Please do not modify it. After you are done, submit your work and check your results. You need to score 70% to pass. Good luck :) !

calculator-gui-with-python

You have to install Kivy module (pip install kivy )

capgemini-traning-assesment

carserviceapplication

Full-stack Application using MERN stack.

color-detection-task-2

commands_for_sql

compitative_programming

cqrs-design-pattern

cruise-ship-data-set-using-pandas-numpy-seaborn-mathplotlib

This is a Cruise ship data set where we use all the tools for our research propose to gain some information about the ship, Passenger .To improve the sales

currencyconvertor

data-anz-program

This is the virtual internship program by ANZ . It takes 1 weeks to complete.

deep-neural-network---application

Starting September 2020, notebook items in course shells will become Ungraded Labs. Paid learners will be able to access their notebooks in the new Coursera lab environment; Auditors will lose access. We strongly encourage you to download your notebooks if you are auditing this course. You can also upgrade or applying for financial aid to access premium Lab items in your course. For more information, please see this forum link Congratulations! Welcome to the fourth programming exercise of the deep learning specialization. You will now use everything you have learned to build a deep neural network that classifies cat vs. non-cat images. In the second exercise, you used logistic regression to build cat vs. non-cat images and got a 68% accuracy. Your algorithm will now give you an 80% accuracy! By completing this assignment, you will: - Learn how to use all the helper functions you built in the previous assignment to build a model of any structure you want. - Experiment with different model architectures and see how each one behaves. - Recognize that it is always easier to build your helper functions before attempting to build a neural network from scratch.

deeplearning-example

detecting-fake-news-with-python-and-machine-learning

What is Fake News? A type of yellow journalism, fake news encapsulates pieces of news that may be hoaxes and is generally spread through social media and other online media. This is often done to further or impose certain ideas and is often achieved with political agendas. Such news items may contain false and/or exaggerated claims, and may end up being viralized by algorithms, and users may end up in a filter bubble. What is a TfidfVectorizer? TF (Term Frequency): The number of times a word appears in a document is its Term Frequency. A higher value means a term appears more often than others, and so, the document is a good match when the term is part of the search terms. IDF (Inverse Document Frequency): Words that occur many times a document, but also occur many times in many others, may be irrelevant. IDF is a measure of how significant a term is in the entire corpus. The TfidfVectorizer converts a collection of raw documents into a matrix of TF-IDF features. What is a PassiveAggressiveClassifier? Passive Aggressive algorithms are online learning algorithms. Such an algorithm remains passive for a correct classification outcome, and turns aggressive in the event of a miscalculation, updating and adjusting. Unlike most other algorithms, it does not converge. Its purpose is to make updates that correct the loss, causing very little change in the norm of the weight vector. Detecting Fake News with Python To build a model to accurately classify a piece of news as REAL or FAKE. About Detecting Fake News with Python This advanced python project of detecting fake news deals with fake and real news. Using sklearn, we build a TfidfVectorizer on our dataset. Then, we initialize a PassiveAggressive Classifier and fit the model. In the end, the accuracy score and the confusion matrix tell us how well our model fares. The fake news Dataset The dataset we’ll use for this python project- we’ll call it news.csv. This dataset has a shape of 7796×4. The first column identifies the news, the second and third are the title and text, and the fourth column has labels denoting whether the news is REAL or FAKE.

excel-fundamentals-for-data-analysis

MACQUARIE UNIVERSITY

exploratory-data-analysis----sports

exploratory-data-analysis--retail

face-mask-detection

Internship Project under The Sparks Foundation