This Project is about scrapping news article from a news website (Using python and newspaper library ) and posting them in WordPress based Website through xmlrpc

Sentiment Analysis with Insights using NLP and Dash This project show the sentiment analysis of text data using NLP and Dash. I used Amazon reviews dataset to train the model and further scrap the reviews from Etsy.com in order to test my model. Prerequisites: Python3 Amazon Dataset (3.6GB) Anaconda How this project was made? This project has been built using Python3 to help predict the sentiments with the help of Machine Learning and an interactive dashboard to test reviews. To start, I downloaded the dataset and extracted the JSON file. Next, I took out a portion of 7,92,000 reviews equally distributed into chunks of 24000 reviews using pandas. The chunks were then combined into a single CSV file called balanced_reviews.csv. This balanced_reviews.csv served as the base for training my model which was filtered on the basis of review greater than 3 and less than 3. Further, this filtered data was vectorized using TF_IDF vectorizer. After training the model to a 90% accuracy, the reviews were scrapped from Etsy.com in order to test our model. Finally, I built a dashboard in which we can check the sentiments based on input given by the user or can check the sentiments of reviews scrapped from the website. What is CountVectorizer? CountVectorizer is a great tool provided by the scikit-learn library in Python. It is used to transform a given text into a vector on the basis of the frequency (count) of each word that occurs in the entire text. This is helpful when we have multiple such texts, and we wish to convert each word in each text into vectors (for using in further text analysis). CountVectorizer creates a matrix in which each unique word is represented by a column of the matrix, and each text sample from the document is a row in the matrix. The value of each cell is nothing but the count of the word in that particular text sample. What is TF-IDF Vectorizer? TF-IDF stands for Term Frequency - Inverse Document Frequency and is a statistic that aims to better define how important a word is for a document, while also taking into account the relation to other documents from the same corpus. This is performed by looking at how many times a word appears into a document while also paying attention to how many times the same word appears in other documents in the corpus. The rationale behind this is the following: a word that frequently appears in a document has more relevancy for that document, meaning that there is higher probability that the document is about or in relation to that specific word a word that frequently appears in more documents may prevent us from finding the right document in a collection; the word is relevant either for all documents or for none. Either way, it will not help us filter out a single document or a small subset of documents from the whole set. So then TF-IDF is a score which is applied to every word in every document in our dataset. And for every word, the TF-IDF value increases with every appearance of the word in a document, but is gradually decreased with every appearance in other documents. What is Plotly Dash? Dash is a productive Python framework for building web analytic applications. Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python. It's particularly suited for anyone who works with data in Python. Dash apps are rendered in the web browser. You can deploy your apps to servers and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile ready. Dash is an open source library, released under the permissive MIT license. Plotly develops Dash and offers a platform for managing Dash apps in an enterprise environment. What is Web Scrapping? Web scraping is a term used to describe the use of a program or algorithm to extract and process large amounts of data from the web. Running the project Step 1: Download the dataset and extract the JSON data in your project folder. Make a folder filtered_chunks and run the data_extraction.py file. This will extract data from the JSON file into equal sized chunks and then combine them into a single CSV file called balanced_reviews.csv. Step 2: Run the data_cleaning_preprocessing_and_vectorizing.py file. This will clean and filter out the data. Next the filtered data will be fed to the TF-IDF Vectorizer and then the model will be pickled in a trained_model.pkl file and the Vocabulary of the trained model will be stored as vocab.pkl. Keep these two files in a folder named model_files. Step 3: Now run the etsy_review_scrapper.py file. Adjust the range of pages and product to be scrapped as it might take a long long time to process. A small sized data is sufficient to check the accuracy of our model. The scrapped data will be stored in csv as well as db file. Step 4: Finally, run the app.py file that will start up the Dash server and we can check the working of our model either by typing or either by selecting the preloaded scrapped reviews.

A web scrapping project to extract the course details from various online course providing websites.

3rd Web Project - Ecole 42 : Popcorn-time like website - stream and download movies scrapped from YTS and 1337x

This project shows how to use opera vpn to scrap website

This project is a web scraper/ web crawler, which takes a food recipe website and scrap its data to show the required recipes.

An Ultimate web scrapping and data analyst project and also machine learning project that send API to a car selling website and u can have prices of cars u want in your sql database and estimate your own car price by machine learning and so much more...

Julie an AI personal voice assistant using Python. It can make your work easier. She can send mail to who you want , she can open any websites , she can play youtube video you mentioned , she can open any softwares or applications you command her , she will wish you, she can tell time and date and most importantly if you want to search something or wanna know something she will scrap the data from google and can tell you what it is. Since its my mini-project, i have programmed Julie to work these commands later there will be an update in this project : you can order your food delivery by saying your commands alone , you can command Julie to set alarm , taking notes.

1> Background information Bluebikes is Metro Boston’s public bike share program, with more than 1800 bikes at over 200 stations across Boston and nearby areas. The bikes sharing program launched in 2011. The program aimed for individuals to use it for short-term basis for a price. It allows individuals to borrow a bike from a dock station after using it, which makes it ideal for one-way trips. The City of Boston is committed to providing bike share as a part of the public transportation system. However, to build a transport system that encourages bicycling, it is important to build knowledge about the current bicycle flows, and what factors are involved in the decision-making of potential bicyclists when choosing whether to use the bicycle. It is logical to make hypotheses that age and gender, bicycle infrastructure, safety perception are possible determinants of bicycling. On the short-term perspective, it has been shown that weather plays an important role whether to choose the bicycle. 2> Data collection The Bluebikes collects and provides system data to the public. The datasets used in the project can be download through this link (https://www.bluebikes.com/system-data). Based on this time series dataset (start from 2017-01-01 00:00:00 to 2019-03-31 23:00:00), we could have the information includes: Trip duration, start time and data, stop time and data, start station name and id, end station name and id, bike id, user type (casual or subscribed), birth year, gender. Besides, any trips that were below 60 seconds in length is considered as potentially false starts, which is already removed in the datasets. The number of bicycles used during a particular time period, varies over time based on several factors, including the current weather conditions, time of the day, time of the year and the current interest of the biker to use the bicycle as a transport mode. The current interest is different between subscribed users and casual users, so we should analyze them separately. Factors such as season, day of a week, month, hour, and if a holiday can be extracted from the date and time column in the datasets. Since we would analyze the hourly bicycle rental flow, we need hourly weather conditions data from 2017-01-01 00:00:00 to 2019-03-31 23:00:00 to complete our regression model of prediction. The weather data used in the project is scrapped using python selenium from Logan airport station (42.38 °N, 71.04 °W) webpage (https://www.wunderground.com/history/daily/us/ma/boston/KBOS/date/2019-7-15) maintained by weather underground website. The hourly weather observations include time, temperature, dew point, humidity, wind, wind speed, wind gust, pressure, precipitation, precipitation accumulated, condition. 3> The problem The aims of the project are to gain insight of the factors that could give short-term perspective of bicycle flows in Boston. It also aimed to investigate the how busy each station is, the division of bicycle trip direction and duration of the usage of a busy station and the mean flows variation within a day or during that period. The addition to the factors included in the regression model, there also exist other factors than influence how the bicycle flows vary over longer periods time. For example, general tendency to use the bicycle. Therefore, there is potential to improve the regression model accuracy by incorporating a long-term trend estimate taken over the time series of bicycle usage. Then the result from the machine learning algorithm-based regression model should be compared with the time series forecasting-based models. 4> Possible solutions Data preprocessing/Exploration and variable selection: date approximation manipulation, correlation analysis among variables, merging data, scrubbing for duplicate data, verifying errors, interpolation for missing values, handling outliers and skewness, binning low frequent levels, encoding categorical variables. Data visualization: split number of bike usage by subscribed/casual to build time series; build heatmap to present how busy is each station and locate the busiest station in the busiest period of a busy day; using boxplot and histogram to check outliers and determine appropriate data transformation, using weather condition text to build word cloud. Time series trend curve estimates: two possible way we considered are fitting polynomials of various degrees to the data points in the time series or by using time series decomposition functions and forecast functions to extract and forecast. We would emphasize on the importance to generate trend curve estimates that do not follow the seasonal variations: the seasonal variations should be captured explicitly by the input weather related variables in the regression model. Prediction/regression/time series forecasting: It is possible to build up multilayer perceptron neural network regressor to build up models and give prediction based on all variables of data, time and weather. However, considering the interpretability of model, we prefer to build regression models based on machine learning algorithms (like random forest or SVM) respectively for subscribed/casual users. Then the regressor would be combined with trend curve extracted and forecasted by ARIMA, and then comparing with the result of time series forecasting by STL (Seasonal and Trend decomposition using Loess) with multiple seasonal periods and the result of TBATS (Trigonometric Seasonal, Box-Cox Transformation, ARMA residuals, Trend and Seasonality).

Web scrapping project used to get Mutual Fund portfolio from moneycontrol website

In this project, I build a web scrapper to scrappe: football-data.co.uk website and performed Extraction Transformation & loading (ETL) on the screapped data to a Mysql database

This repo contains a scrapper for the Gutenberg's project website which contains 56,019 books free to read and download. In this repo also, you can find text file containing all the book data until April 2018 containing only the 'id', 'title' and 'authors' for every book in the dataset.

This is a Web scrapping project which pulls the latest Corona data from various websites to inform us about COVID-19 scenario around the globe and it also visualizes the intensity of the pandemic by plotting holistic graphs and plots. It also visualizes the data and sends an email to the user informing him about the Corona cases in a personalized manner.

What makes a good movie? Most of the top-rated movies in the International movie database (IMDB) are critically acclaimed and are generally a safe bet in terms of commercial success. Naturally, it would be interesting to investigate if these top movies have some distinct features responsible for their high ratings. This project aims to find out the type of natural cluster that exists among the top 250 movies from IMDB. Unsupervised machine learning techniques will be employed, more specifically, clustering algorithms. Hopefully, these clusters will give us information to observe the recurrent pattern. To build our dataset we used OMDB's web API which is RESTful web service to obtain movie information. For plot summaries we scrapped a movie's plot from IMDB's website using BeautifulSoup Library in python. Our final extracted dataframe had 250 rows and 113 columns. The inputs to our dataframe were all categorical features which were one hot encoded. We first used dimensionality reduction techniques such as PCA which was followed by K-Means and DB-SCAN clustering to find inherent clusters in the data.

This project will explore, analyse and visualise publicly available wells datasets from the United States offshore data centre, the USGS boreholes website - Bureau of Safety and Environmental Enforcement (BSEE) https://www.data.bsee.gov/Main/Default.aspx with a particular focus on the Gulf of Mexico (GOM) wells. This project will study sandstones quality as a reservoir, the production history of the operators on the Gulf of Mexico and a well summary report to highlight any possible problem. The reservoir quality analysis will examine relationships between average values of porosity, permeability, depth, temperature, pressure, thickness, age, and play type for data files from 2009 until 2019.The porosity plotted and shown in a wide range of plots as a function of permeability and burial depth. Also, the median (P50) porosity will be plotted against depth to examine the porosity trend. Moreover, this project will investigate the companies oil and gas production in the gulf of Mexico for the last five years. Lastly, the analysis will include an investigation of well summary reports of five wells. The project will include web scrapping to collect online well summary reports to generate a word cloud. The project results can be useful for specifying realistic distributions of parameters for both exploration risk evaluation and/or reservoir modelling by machine learning algorithms in the next project.

Just a few years ago, a company formed by three individuals decided that it would be making skateboards and sunglasses from recycled nylon. They were basing their efforts upon “trash” floating in the ocean, that they were determined should get cleaned up if they set the ball rolling and others joined them in the effort. “When we researched ocean waste, we learned that there’s a constant stream of nylon fishing nets being dumped into the ocean every year, nets that are just going to sit there for generations. This stuff doesn’t break down.” Today, the company pays fishermen in Chile to collect old nylon fishing nets, which are then recycled into skateboards and sunglasses. What is the material called Nylon? Nylon is a type of synthetic fiber fabric like polyester, made from petroleum products. Nylon was the first fabric made entirely in a laboratory and its invention represents the dawn of the age of synthetics. Nylon had started appearing in stores in 1939 in the form of women’s tights, but it was really the Second World War that opened up the market for Nylon. Nylon became widely available to the general public around the time of World War II. In fact during the war it extensively found of use in the making of parachutes and other military equipment. Prior to 1945, cotton and wool dominated the market; by the end of the war, synthetic fibers particularly nylon had eaten up a significant 25% of the market share. It is today commonly used to make clothing, backpacks and bags, stockings or tights, outdoor gear such as tents, rope, carpet, underwear and hosiery, nylon can also be found in the bristles of our toothbrushes, umbrellas, knits, and swimwear and active wear and many other items we use every day. The advantages of Nylon as a material First developed in the 1930s as an alternative to silk, there are lots of great qualities about the fabric. It is lightweight yet strong, and it is often touted for its quick-drying capabilities. Clothing manufacturers like it because it holds dye well. It is also less expensive to produce than silk and does not get damaged as easily. The making of nylon for fabric use Nylons are a family of materials called polyamides, made from reacting carbon-based chemicals found in coal and petroleum in a high-pressure, heated environment. This chemical reaction, known as condensation polymerization, forms a large polymer – in the form of a sheet of nylon. To make nylon fabric for apparel, this nylon sheet is then broken into chips, melted and drawn through a mechanical spinneret to produce individual fibres that are woven into fabric. This plastic is then put through an intensive chemical process, resulting in the strong, stretchy fibres that make it so useful as a fabric. So what is the idea about recycling Nylon? Since Nylon is made of petroleum products it will not biodegrade. Nylon doesn’t break down easily and accounts for about 10% of the debris in the ocean. According to the World Society for the Protection of Animals, more than 600,000 tons of fishing gear is dumped into oceans every year, including nylon nets. Fishermen often discard the nets because the alternative is much costlier – paying someone to dispose of them properly. For some reason locked deep in polymer chemistry, nylon is more difficult to recycle than polyester. After years of research, development, and testing, some recycled nylon fibers that are suitable for apparel and can pass the rigorous tests of manufacturability and product quality, is what the company found out. “Although we’ve been substituting non-recycled polyester for recycled versions for 20 years, only in the last five have we begun swapping out non-recycled nylon for its recycled replacement. Some of the recycled nylon we use comes from post-industrial waste fiber, yarn collected from a spinning factory, and waste from the weaving mills that can be processed into reusable nylon fiber. Another recycled nylon fiber we are experimenting with is re-created from discarded industrial fishing nets. Though a lot of experiments were conducted and extensive research on how nylon could be converted to its recycled biodegradable form was carried out, it was only in 2013 onwards that it actually produced desired results. In any case, incorporating as much recycled nylon as we can lessens our dependence on petroleum as a raw material source. It curbs discards, thereby prolonging landfill life and reducing toxic emissions from incinerators. It helps promote new recycling streams for nylon products that are no longer usable. And it causes less air, water, and soil contamination compared to using non-recycled nylon. Recycling of Nylon – a challenge in itself The economics of recycling nylon are not very appealing, however. An associate professor in plastic engineering at the University of Massachusetts Lowell, ran a research program on recycled fishing nets for the company. Nylon, he says, is not an easy or cheap material to recycle. Plus polymers, or plastics, are cheap to buy new which may be why many companies choose to use polyethylene terephthalate (PET) – the most common type of plastic found in soda and water bottles – instead . Contamination is another concern. Unlike metals and glass, which are melted at high temperatures, nylon is melted at a lower temperature, meaning some contaminants – non-recyclable materials and microbes or bacteria – can survive. This is why all nylons have to be cleaned thoroughly before the recycling process. “When you’ve dragged a fishing net through a boat, on the ocean floor, and wherever else, it’s a lot harder to clean before you can recycle it,” Johnston says. That’s why Johnston is supportive of circular economy business models, in which businesses keep resources in use for as long as possible, extract their maximum value and then recycle and reuse products and materials. “What would change the recycling scene is if we were charged per pound for all waste. Or if companies had to take back part of what they produced.” The company has an idea already: the company’s sunglasses come with a lifetime warranty. In fact it will fix any pair of glasses free of charge, or provide customers with new frames if their product is beyond repair. Old frames are recycled. And another Italian manufacturer Aquafil has nylon fibers in its carpets. After nearly 40 years of producing carpet yarn, a growing awareness of the environmental harm caused by synthetic materials made it want to turn towards a more environmentally friendly business model. In 2007, Aquafil began developing a machine that can churn through most kinds of nylons, producing new threads ready to be repurposed. Aquafil now sells these threads, called Econyl, to American brands such as Outerknown, an LA-based outerwear company started by pro surfer Kelly Slater, and swimwear giant Speedo. LA-based Masami Shigematsu works on product development for Speedo. She says that she had been actively searching for recycled nylon for years before she found Econyl. “It has to perform well. It can’t just be a sustainable material. Our products are being used by athletes who need it to function as good as new material.” In 2014, Shigematsu met with Aquafil and started experimenting with the fabric. Last year, Speedo rolled out two products with Econyl and has since expanded to include more than 50 products made with the material. Has corporate social responsibility become the modern gold rush? California-based Patagonia has also been adding more recycled nylon to its lineup. Currently, the company has more than 50 products that contain recycled nylon in various percentages. The Torrent shell jackets, for instance, have an outer layer textile made with 100% chemically recycled nylon. It took Patagonia nearly 15 years to develop the technology to recycle polyester to a point where it was as good as virgin polyester. Patagonia wants to go further than just use recycled nylon in its products. How to they Recycle Nylon Just about everyone has nylon around their home. It is in the backpacks our kids take to school, the pantyhose women wear to work and the cheap, reusable shopping bags everyone is handing out these days. There are very few places that accept nylon for recycling. It is unlikely that you can recycle it through your curbside program, and equally so that your local recycling center will have a handy bin that says, “Put your unwanted nylon here!” Your ability to recycle nylon depends largely on the form it takes; for example, nylon pantyhose are easier to recycle than nylon backpacks. But remember: If you cannot recycle an item made of nylon, you may be able to reuse it rather than putting it in the trash. The problem with nylon is that, like many fabrics, it is difficult to recycle, especially once it has been used. Second-hand fabrics typically need to be cleaned before they can be recycled, and it is often not cost-effective for companies to do that. However, there are a few nylon recycling options out there. How to recycle or reuse nylon bags Nylon bags are challenging to recycle unless you purchase one from a company that offers a take back program. San Francisco-based Timbuk2 is one such company. Once your nylon messenger or camera bag is worn out, simply stick it in a box and mail it to the company at the address provided on its website. Timbuk2 will reuse or recycle as many of the materials as possible. There is no charge for the company’s recycling services (other than the cost of postage), and customers that send in products to be recycled will receive a 20% discount on a future purchase. There may also be creative ways to reuse unwanted nylon bags. If you have a backpack that is in good shape that you no longer want, consider donating it to a thrift shop or a program that helps children get school supplies. If you have a large shopping bag with a hole it in, cut it apart and use the good nylon to make a smaller storage bag. How to recycle or reuse nylon fabric Leftover nylon fabric from a sewing project is a great material to reuse. See if your community has an organization that provides fabric and supplies to artists and schools. Materials for the Arts in New York City and The Scrap Exchange in Durham, NC, are a few examples. If you have nylon clothing you want to recycle, and you purchased that clothing from popular outdoor gear manufacturer Patagonia, you can return it to the company for recycling. Get more information about Patagonia’s recycling program on its website. How to recycle and reuse nylons or tights No Nonsense, which makes nylons, tights and other types of leggings, offers a recycling program for consumers. The first step is visit their pantyhose recycling page and print a prepaid mailing label. Next, place all your unwanted nylon leggings in a box and put on the shipping label. Drop it at your nearest post office or other mailing location, and your old nylons are on their way to a recycling facility. No Nonsense sends the material to a plant that recycles it into things like playground equipment, toys and vehicle insulation. There are lots of ways to reuse old nylons as well. Put a bar of soap in the toe of a clean nylon (make sure there is no run in that section). Tie off the open end and hang the sock by the sink. When you go to wash your hands, get them plenty wet then roll the sock between your hands. This works really well in potting sheds, barns or other places where a soap dish might not be practical. Use nylons to tie up tomatoes or other plants that need support as they grow. Fill a clean nylon with potpourri or lavender. Use it as a sachet in your drawers, car or any other area you want to smell fresh. But then what is to be Nylon’s impact on the planet? Different kinds of nylon have different properties, but the common threads between each are strength, durability and ability to be moulded into shape. The flip side is that no form of nylon is biodegradable; so once you no longer have a need for your torn stockings or old toothbrush, it sits in a landfill for at least 30 years. Nylon is in part derived from coal and petroleum. In producing nylon there is creation of Greenhouse gases: producing nylon creates nitrous oxide, a greenhouse gas that is 300 times more potent than carbon dioxide. Water: manufacturing nylon is a very thirsty process; large amounts of water are used for cooling the fibres, which can be a source of environmental contamination and pollution. Energy: manufacturing nylon is a very energy-hungry process, which contributes to environmental degradation and global warming. But, definitely there is the good side to it. Nylon is a plastic that can be recycled. There are several brands and accreditations that can help consumers find more sustainable nylon products. Econyl has developed an eco-friendly nylon made from recycled plastics in a closed loop system, drastically reducing waste and emissions. Nylon may certainly not be great for the environment, but there are plenty of brands working hard to turn that around!

Website archiving project for Mojok.co website, based on scrapping.

This project aims to scrap a US government website using the Scrapy framework

web scrapping tool that enables you to get data from a given website and then store it in a tabular form. For this project I was scrapping form jumia but you can scrap from any website

In www.moneycontrol.com, various recommenders post recommendations on the buy, sell and hold decisions of various stocks. This project was aimed to evaluate the performance of the recommenders over the time and under various market conditions. The work involves scrapping few hundred pages of information from the moneycontrol website and then scrap the stock price of each stocks from the National Stock Exchange (NSE) of India starting from the date of recommendation up to a year later. Cleaning and arranging the scrapped data had consumed a major portion of the project. Finally, a meaningful visual dashboard is created to summarize important information and rank various recommenders.

This is my first project after studying python for 20 days. I tried to use everything I learned as a beginner. With this project, I tried to scrap a website (http://patorjk.com) to get and create a database for ascii arts. Then display user inputs with another script which I call ascii_art

A scrapper written in Rust to create TP projects from David Bouchet's debug pro website.

This project is to scrap/collect ARN holders data from AMFI Website

A demo project for scrapping website using puppeteer(js/ts) and scrapy(python)

This is a project that used the Jumia website to illustrate the steps of Web Scrapping.

All reviews for places scrapped and placed in database from top website; useful for Data Science and Machine Learning Purposes for related projects.

This is the project scrapping many websites from google based on user's input made by python. Let's make new one world with shinobi.

This project uses the data collected (Web Scrapped) from a website that list the houses for sale in Rwanda, and employs machine learning to predict the price of a house given its neighborhood, plot size, number of bedrooms and bathrooms, etc.

The main feature that this project provides is predicting the future trend of stock prices. About 3 months of data have been scraped through pakistan data portal website and on the basis of that data stock prices will be predicted. Only the companies that listed in pakistan data portal are enlisted for stock prediction. A web platform using advanced technologies has been built. Stock management is basically between buyer, seller and comapany. For gold and currency rates, an api has been created. Main tools and technologies involved are node.js, react, web scrapping, python, machine learning , Material design bootstrap.

Twitter Parsing I've recently discovered the Chris Albon Machine Learning flash cards and I want to download those flash cards but the official Twitter API has a limit rate of 2 weeks old tweets so I had to find a way to bypass this limitation : use Selenium and PhantomJS. Purpose of this project : Check every 2 hours, if he posted new flash cards. In this case, download them and send me a summary email. Face Recognition Modern face recognition with deep learning and HOG algorithm. Using dlib C++ library, I have a quick face recognition tool using few pictures (20 per person). Playing with Soccer data As a soccer fan and a data passionate, I wanted to play and analyze with soccer data. I don't know currently what's the aim of this project but I will parse data from diverse websites, for differents teams and differents players. NYC Taxi Trips Kaggle playground to predict the total ride duration of taxi trips in New York City. Kaggle Understanding the Amazon from Space Use satellite data to track the human footprint in the Amazon rainforest. Deep Learning model (using Keras) to label satellite images. Predicting IMDB movie rating Project inspired by Chuan Sun work How can we tell the greatness of a movie ? Scrapping and Machine Learning