This is a simple Regression Model which calculates the equation of the Regression Line on the plot and then uses it to predict a student's score.
Link: GitHub Repository
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This is a simple model which first vectorizes the training data using TF-IDF and then uses Passive Aggressive Classifier to train on the input data.
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This is a simple Classification Model trained using the XGBoost Algorithm and then uses the training to predict whether a patient is sufferring from the disease or not.
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Here, we will be using the concept of Reinforcement Learning. We know that Supervised Learning in which we have the labelled dataset and unsupervised learning which has the unlabelled dataset. Reinforcement Learning is very similar to the way we teach children or pets by giving a positive remark whenever they do something good or obey us properly and we give a negative remark or scold them whenever they do something good.
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For this project, we will be using the Picker Library. Here, we will take the input and the data should consist of stories, questions and answers. Then, we have the word index and it takes the dictionary from the tokenizer. The other parameters which we will needs is the length of the longest story and the length of the longest question. We need these parameters for the Pad Sequences Function.
The output of this function is that we need to vectorize the data into the padded sequence. We will first loop through all the entire data and then we will convert the row word into the word index value and then we will append each set to the appropriate output list. Once we convert the word to the number, we will pad the sequences so that they are of equal length. So, basically, we have to return the tuple.
Link: GitHub Repository
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The model predicts the price of the house using Linear Regression. The algorithms used are:
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The model compares several different algorithms and predicts whether a family will be granted the loan from the bank or not. Some of the algorithms which are being used are:
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The model predicts whether the tweet given by the used is Positive, Negative or Neutral based on the training using Bi-LSTM.
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The project is a Transfer Learning and CNN trained model which can predict whether the patient has a suffering from Cancer or not by checking the images of the infected areas on the body. The model has been trained on a variety of images through which it predicts the required.
Link: GitHub Repository
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Simple Scientific Calculator Application
Link: GitHub Repository
Simple application to keep the score of a team. This application is not automated.
Link: GitHub Repository
Simple application to convert the input Currency from one country to another country. This is a static application which consists of a list of countries and isn't dynamic.
Link: GitHub Repository
This Application consists of a several basic applications of day-to-day use combined together for quick access. It consists of:
Link: GitHub Repository
This is a Decentralised voting application which runs on a Decentralised network. This project was tested using the Ganache and Ethereum.
When the user enters the website, we will use a pulgin called 'geoPlugin', which is an API based on the IP Address of the user, returns the country code of the user. The country code is a two character universal code, which does not change, whereas any other thing for the country might change, for example, the language of the country might change. After fetching the country code, we are going to use a list of codes, named as the 'country_list' to get the details for that country. Then, we will send the country name to the API and then we will fetch all the details of the country.
We need to add a script to out code for the plugin to work. Then we will be using the function geoplugin_countryCode(); to return the country code of the user.
We will also be using some Promises and Callbacks.
Link: GitHub Repository
Link: Website
This website is built using PHP and is integrated with a database using MySQL. The website was tested using the XAMPP Server.
Link: GitHub Repository
This website is built using PHP and is integrated with a database using MySQL. The website was tested using the XAMPP Server. The database used in this website is normalised to 5NF Form.
Link: GitHub Repository