Reddit-flair-detection

This is a reddit flair detection repository developed using flask and python, it's live at https://flair-it-up.herokuapp.com/.

Structure

The directory contains web sub directories and a sub directory for hosting model and other scripts:

1. app.pyThe file which contains all the main backend operations of the website and used to run the flask server locally.

2. Procfile for setting up heroku.

3. model contains the saved model.

4. requirement.txt contains all the dependencies.

5. templates contains the html file.

6. static contains the css file.

7. nltk.txt contains the nltk dependency.

8. Scripts the directory contains scripts for data extraction, model, expolatory data analysis and experiment log manager notebooks.

Codebase

The entire code has been developed using Python programming language and is hosted on Heroku. The analysis and model is developed using nltk library and various machine learning models, The website is developed using Flask.

How to run the project:

1. Open the Terminal.
2. Clone the repository by entering https://github.com/abhishek-parashar/Reddit-flair-detection.
3. Ensure that Python3 and pip are installed on the system.
4. Create a virtualenv by executing the following command: virtualenv venv.
5. Activate the venv virtual environment by executing the follwing command: source venv/bin/activate.
6. Enter the cloned repository directory and execute pip install -r requirements.txt.
7. Now, execute the following command: flask run and it will point to the localhost server with the port 5000.
8. Enter the IP Address: http://localhost:5000 on a web browser and use the application.

Dependencies

The following dependencies can be found in requirements.txt:

1. praw
2. scikit-learn
3. nltk
4. Flask
5. Gensim
6. pandas
7. numpy
8. scikit-learn
9. gunicorn
10. XGBoost

Approach

I went through a lot of litrature and Youtube videos for the following task, the resources can be seen in the resorces section. After going through these resources and tutorials. I collected data from reddit using Praw module. I used nltk to remove bad words and applied various machine learning models to it. Only top 10 comments were taken, I used TFID DICT VECTORIZER to convert to word embeddings. Finaly deployed it using Flask and Heroku.

Results

Title as Feature

Machine Learning Algorithm	Test Accuracy
Linear SVM	0.7418032786885246
Logistic Regression	0.75409836
Random Forest	0.7336065573770492
MLP	0.5327868852459017
XGBoost	0.7008196721311475

Body as Feature

Machine Learning Algorithm	Test Accuracy
Linear SVM	0.3442622950819672
Logistic Regression	0.3237704918032787
Random Forest	0.3770491803
MLP	0.2663934426229508
XGBoost	0.3688524590163934

URL as Feature

Machine Learning Algorithm	Test Accuracy
Linear SVM	0.2745901639344262
Logistic Regression	0.3073770491
Random Forest	0.2622950819672131
MLP	0.2254098360655737
XGBoost	0.2172131147540983

Comments as Feature

Machine Learning Algorithm	Test Accuracy
Linear SVM	0.430327868852459
Logistic Regression	0.4344262295081967
Random Forest	0.438524590163
MLP	0.3073770491803279
XGBoost	0.4180327868852459

Title + Comments + URL as Feature

Machine Learning Algorithm	Test Accuracy
Linear SVM	0.7090163934426229
Logistic Regression	0.7131147540983607
Random Forest	0.7745901639344263
MLP	0.5532786885245902
XGBoost	0.8278688

Inferences

There various iterferences as discussed in the EDA notebook from the results we can infer that combined features give the best result probably because of the larger word embeddings present. We can also infer that the title as a feature also provides better results this can be attributed to the fact that title mainly consists of the required words or embeddings that is, it is in line with the flairs.

Declaration

Since I am not that well versed in html, css I got the HTML and CSS sheets from here -https://github.com/krishnaik06/Deployment-flask

References

1. For data collection:

1. https://towardsdatascience.com/scraping-reddit-data-1c0af3040768
2. https://api.mongodb.com/python/current/tutorial.html

2. For Building machine learning model:

1. https://medium.com/themlblog/splitting-csv-into-train-and-test-data-1407a063dd74
2. https://towardsdatascience.com/multi-class-text-classification-model-comparison-and-selection-5eb066197568
3. https://medium.com/@robert.salgado/multiclass-text-classification-from-start-to-finish-f616a8642538
4. https://www.analyticsvidhya.com/blog/2018/04/a-comprehensive-guide-to-understand-and-implement-text-classification-in-python/
5. https://www.districtdatalabs.com/text-analytics-with-yellowbrick
6. Applied AI course- https://www.appliedaicourse.com/

3.For Building the Website and Deploying it:

1. https://towardsdatascience.com/designing-a-machine-learning-model-and-deploying-it-using-flask-on-heroku-9558ce6bde7b
2. https://towardsdatascience.com/deploying-a-deep-learning-model-on-heroku-using-flask-and-python-769431335f66
3. https://medium.com/analytics-vidhya/deploy-machinelearning-model-with-flask-and-heroku-2721823bb653
4. https://www.youtube.com/watch?v=UbCWoMf80PY
5. https://www.youtube.com/watch?v=mrExsjcvF4o
6. https://blog.cambridgespark.com/deploying-a-machine-learning-model-to-the-web-725688b851c7