In this markdown file, there will be contribution guidelines.

I would like to implement a Bi-directional LSTM neural network architecture to generate Shakespeare styled text.
The dataset I'll be using is from kaggle and it contains almost 1,11,396 lines from Shakespeare's plays from the various Act-Scenes.

@Jayshah6699 I'm participating in DWOC. It would be nice if you could assign this issue to me.
Thank you.

Make a machine learning model to predict weather.

A file containing all types of graphs(along with implementation) so that everyone can look at it and use in their own projects.

The model takes the input of the 'Apple' stock prices and outputs the prediction of the Closing price of the stock. It is implemented on Python using Linear Regression Models.Used the Quandl API to apply ML to the stock market.
Please label this issue as DWOC.
Also please add the level.

I had made car price prediction project and deployed it using Flask framework.
Please allow me to contribute that here.

Please assign me this and enlighten me with your preferences.

I can work on this issue. I have trained the dataset from Kaggle and created a CNN model to classify whether the image is of dog or cat. @Jayshah6699 Kindly, assign the labels to this issue and then assign it to me.

can i do this project as predicting concrete strength using nueral networks?

I will try to improve and beautify README.md file present in your repository

This project is all about predicting the traffic signs. The tech stack used here is python,tensorflow,tkinter,cnn and son on. The dataset is obtained from kaggle. It has test and train dataset on which model is going to be trained . Apart from traing the model a tkinter [ python GUI] is made to upload the picture and predict the symbol. So basically it is a standalone application for classifying or predicting the various traffic signs.

I can work on this issue. @Jayshah6699 assign the labels to this issue and then assign it to me.

Hello there,
I would like to add an issue template for your repository. This template would have four issues namely bug, documentation, feature, proposal. Kindly assign me this issue as a part of DWOC.
THANK YOU

Abstract:

In this machine learning project, I cleaned, analyzed, and predicted two target variables – both revenue (numerical) and profitability (categorical), from the dataset of THE MOVIE DATABASE (TMDb).

Dataset:

The dataset contains around 5000 movies with 22 features and is obtained from Kaggle (here). The information available about each movie include its budget, revenue generated, genres, rating, vote count, popularity, actors and actresses and any more. However, I used an unclean version of the dataset for our project (Link used within notebook).

Implementation:

In this project, I will use this dataset to clean, analyze and determine whether any information about a movie can predict the total revenue of a movie. I will then attempt to predict whether a movie's revenue will exceed its budget (profitability). Also, I will test two different models for each prediction to check which predicts our target variable better.

The results obtained from this project will be helpful for the Movie Production Teams to analyze the rubrics of their Movie Idea before it moves on to the Production Phase.

It will use the bag of words model and Naive Bayes classification. The code will be in python.

In this project, we will use various models for predicting the 10-year CHD of a person based on the features provided in the dataset and use a web app for users to select any of the models and view the results obtained from them. Users won't be able to input data for prediction since there are a lot of features but will be able to input hyperparameters of their choice if required. Options will also be provided to choose among various metrics available for viewing.

Dataset:

Framingham from Kaggle, a single CSV file.

Models:

Using both scikit-learn and TensorFlow.

Web App:

Using either Streamlit or Flask (yet to be decided)

Web Hosting:

Using either Streamlit-sharing or Heroku

Any suggestions are always welcome.

It is well known that if a disaster occurs in one region, it is likely to happen again. Some regions have frequent earthquakes, but this is only a comparative amount compared to other regions.

So, predicting the earthquake with date and time, latitude and longitude from previous data is not a trend that follows like other things, it happens naturally.

So basically if we have past data we can predict future disasters.
Make machine learning model do it.

I can provide dataset if needed.

Hai,
I would love to add an issue template for your repository. This template would have four issues namely bug, documentation, feature, proposal and question

I can work on this issue.@Jayshah6699 add the labels and then assign it to me.

Anyone interested to solve this issue can tell here.. @Jayshah6699 add the labels.

Implemented and compared three types of linear classifiers to use for sentiment analysis of Amazon product reviews using self-made linear classifiers from scratch.

The goal of this project is to design a classifier to use for sentiment analysis of product reviews. The training set consists of reviews written by Amazon customers for various food products. The reviews, originally given on a 5 point scale, have been adjusted to a +1 or -1 scale, representing a positive or negative review, respectively.

In order to automatically analyze reviews, I did the following tasks:

Construct the three classifiers: the perceptron algorithm, the average perceptron algorithm, and the Pegasos algorithm.

Use these self-made classifiers on the food review dataset, using some simple text features.

Experiment with additional features and explore their impact on classifier performance.

Hi @Jayshah6699

I want to work on predicting the stock closing price of a company using bidirectional LSTM's.
Please assign me this issue so that I can start working

This project is about predicting the survival on the Titanic cruise. The tech stack used here is python, sklearn, pandas etc. The dataset is obtained from kaggle. It has test and train dataset on which model will be trained and tested.

Dataset: https://www.kaggle.com/nishant4k/chennai-house-pricing-

I would Love to create a logo for this project. Kindly add the labels and assign it to me if you're fine with the suggestion. @Jayshah6699

There are issues with the scikit learn library that it can't be fed with NULL (missing values) and String (Object) data-type.
Hence a Data Scientist(beginners or who don't have any trick yet) has to give his extra time to clean the Data (or dataset),

I want to build a python script that does the following tasks:

1. take input data
   -it can take any format (like .xslx, .csv )
2. drop duplicates rows, columns
3. Impute missing values.
4. Change the Object data-type to a model fed datatype( uint )
5. Output the file ( with same name and extension as given)

Whoo! the newly created file is ready to feed into any model

You may check one of my predicting models' file, where I have been gone through to clean the dataset before feeding into it a model.
check the file here.
@Jayshah6699 Please check it and put the tags accordingly and assign to me.

Hello again,
This is a multiclass classification problem

dataset Logistic-Regression: sklearn.dataset
There will be preprocessing and visualization of the data, and then tuning the model for the right accuracy
It will be achieved by changing various hyperparameters for the models,
at last accuracy with various metric like, MSE, R2, Confusion matrix,
@Jayshah6699 Please add the label and assign.

Rainfall Prediction is one of the difficult and uncertain tasks that have a significant impact on human society.

Timely and accurate forecasting can proactively help reduce human and financial loss.

This study will present a set of experiments that involves the use of common machine learning techniques to create models that can predict whether it will rain tomorrow or not based on the weather data for that day.

There is no issue template if you want to add one please let me know

Try to find create or clean existing dataset then upload it to kaggle and mention it with the relatable application.

New data science project folder.
Create a new issue with the project nameand in that explain your data science project(which is basically your folder name).

Rainfall Prediction is one of the difficult and uncertain tasks that have a significant impact on human society.

Timely and accurate forecasting can proactively help reduce human and financial loss.

This study will present a set of experiments that involves the use of common machine learning techniques to create models that can predict whether it will rain tomorrow or not based on the weather data for that day.

A text classification project made using Naive Bayes probability method. Given a piece of text, my model classifies the text into one of several classes that are in the training dataset.
Dataset used for training is the 20 Newsgroup Dataset - http://qwone.com/~jason/20Newsgroups/

I want to add my project which reviews your Twitter tweet with the help of a graph or any other chart type by google sheets in terms of the Number of retweets, Positive tweets, Negative Tweets, and their difference as a Net score.

Hello there,
I can add a welcome bot config file having a proper message that will show up when any user will open up an issue or pull-request for the first time as a part of DWOC. Please assign me this issue. For reference, kindly check out: https://github.com/apps/welcome
THANK YOU

The model takes input data of various users, the movies they have watched and if they like it or not. Later on, the model is trained on that data by implementing Real Boltzmann Machine using Pytorch to predict the movies in the test set that the concerned user may like to watch.

You can add models in different languages but for that, in the specific project folder if that language exists (for example python file or python model) then make another folder named that language and add the model in that folder with project name(for example if the project name is face mask detection then file name will be facemaskdetector.py or facemaskdetector.r)

Labels of the pull request will be assigned as per the accuracy of the model.

The dataset is picked up from kaggle , its about income of adult based on 15 attributes and classified as above 50K or below 50K , i tested classification algorithm, ann and got around 80% accuracy.

Create a new folder for new data science project then create your own named folder then upload your whole code data set used and all with one mark down file explaining everything

I want to work on this issue. The accuracy of my model was approximately 98%. Kindly @Jayshah6699 put the labels of DWOC and level and then assign it to me. I have made a CNN model for recognising the handwritten digits and used keras, tensorflow for training the MNIST dataset. I had trained and tested the model on Google Colab.

This is also a multiclass classification problem

dataset DecisionTree: https://www.kaggle.com/uciml/iris

Steps

• There will be preprocessing and visualization of the data,
• tuning the model for the right accuracyIt will be achieved by changing various hyperparameters for the models like depth of the tree, no. of the element on the node
• creating 3 models of the decision tree which uses the following split methods,
  • Gini Impurity
  • Chi-square
  • entropy
• accuracy with various metric like, MSE, R2, Confusion matrix,
• At last, a .png file showing the whole Decision tree
  @Jayshah6699 Please add the label and assign.