Reddit Flair Detector

A Reddit Flair Detector web application to detect flairs of India subreddit posts using Machine Learning algorithms. The application can be found live at Reddit Flair Detector.

Structure

Data Scrape Notebook

contains code used to scrape data off many posts from reddit using praw api and save all that data in a csv file. goto notebook to know more

Data Models Notebook

contains code used to do the data analysis and train various machine learning models and check the accuracy on different features. The models giving best results were downloaded.

Different Features are :-

• Title - Title of the post
• Url - URL of the post
• Body - Body of the post
• Comments - Comments of the post
• All features combined - Combination of all the above mentioned features

Different Models are :-

• Naive Bayes Classifier
• Linear SVM - Usually converged after 5-7 iterations
• Logistic Regression - Usually converged after 100-150 iterations
• Random Forrest - Showed the best results at most times
• Basic Neural Network - Tried varioius approaches adjusting number of layers ,number of neurons per layer and number of iterations but did not show good results
  goto notebook to know more

Data Directory

the directory contains 4 different csv files containg data that was scraped using Data Scrape notebook

• data1.csv
  • Initial Data Scraping of 100 posts per category
• data2.csv
  • Data Scraping of categories done individually so that i can create a dataset with zero empty values
  • Done because their was very poor performance on flair prediction using body as a feature so I thought that was because of some posts not having body in the dataset
• data3.csv
  • Data scraping of 100 posts per category but redifing combined_features
  • after data2 their was minimal improvement in body so I thought that body is not a useful feature thats why I excluded body from combined features
• data4.csv
  • similar to data3 it just has 150 posts per category to train on a larger dataset

Models Directory

contains best performing models trained on all data.csv files. Random Forrest models could not be uploaded due to their large size

Website Directory

contains the flask implementation of the app. It takes in a url for reddit india post and gives the prediction according to the trained models

Running the website locally

Create a virtual environment, install the dependencies, start the server.

$ virtualenv -p python3 env
$ source env/bin/activate
$ pip install -r requirements.txt
$ python3 app.py

Results

Title as Feature

Machine Learning Algorithm	Accuracy_data1.csv	Accuracy_data2.csv	Accuracy_data3.csv	Accuracy_data4.csv
Naive Bayes	0.65	0.63	0.67	0.65
Linear SVM	0.71	0.65	0.72	0.69
Logistic Regression	0.72	0.68	0.73	0.69
Random Forest	0.70	0.66	0.74	0.69
MLP	0.47	0.48	0.54	0.52

Body as Feature

Machine Learning Algorithm	Accuracy_data1.csv	Accuracy_data2.csv	Accuracy_data3.csv	Accuracy_data4.csv
Naive Bayes	0.25	0.26	0.24	0.27
Linear SVM	0.38	0.35	0.35	0.39
Logistic Regression	0.37	0.33	0.29	0.36
Random Forest	0.35	0.35	0.40	0.37
MLP	0.28	0.26	0.24	0.27

Comments as Feature

Machine Learning Algorithm	Accuracy_data1.csv	Accuracy_data2.csv	Accuracy_data3.csv	Accuracy_data4.csv
Naive Bayes	0.36	0.34	0.32	0.36
Linear SVM	0.35	0.40	0.40	0.43
Logistic Regression	0.38	0.41	0.37	0.44
Random Forest	0.38	0.37	0.40	0.44
MLP	0.25	0.32	0.35	0.39

Combined features(Title + Comments + Body[data1 and dadta2]) as Feature

Machine Learning Algorithm	Accuracy_data1.csv	Accuracy_data2.csv	Accuracy_data3.csv	Accuracy_data4.csv
Naive Bayes	0.53	0.53	0.55	0.53
Linear SVM	0.72	0.71	0.68	0.68
Logistic Regression	0.75	0.75	0.68	0.71
Random Forest	0.78	0.78	0.72	0.70
MLP	0.47	0.52	0.40	0.41

References

1. For data scraping from reddit:

1. http://www.storybench.org/how-to-scrape-reddit-with-python/
2. https://towardsdatascience.com/scraping-reddit-data-1c0af3040768
3. https://praw.readthedocs.io/en/latest/

2. For applying various machine learning model:

1. https://towardsdatascience.com/multi-class-text-classification-model-comparison-and-selection-5eb066197568
2. https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html#building-a-pipeline
3. https://machinelearningmastery.com/prepare-text-data-machine-learning-scikit-learn/

3.For Deploying the model on heroku:

1. https://towardsdatascience.com/designing-a-machine-learning-model-and-deploying-it-using-flask-on-heroku-9558ce6bde7b