update readme

Author: jainish-jain

README.md

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+# Spam Comment Detector
 
+This repository is based on the flask framework web application, with the help of Machine Learning, the application is capable to detect that the comment is spam or not.  
+
+## File Structure
+
+📦Spam-comment-detector-with-flask
+ ┣ 📂static
+ ┃ ┗ 📂js
+ ┃    ┣ 📜app.js
+ ┃    ┣ 📜form.js
+ ┃    ┗ 📜particles.js
+ ┣ 📂templates
+ ┃ ┗ 📜form.html
+ ┣ 📜SPAM.csv
+ ┣ 📜app.py
+ ┗ 📜requirements.txt
+
+## Flask
+
+A microframework based on Werkzeug. It's extensively documented
+and follows best practice patterns.
+
+## Model
+
+#### CountVectorizer
+
+Convert a collection of text documents to a matrix of token counts.This implementation produces a sparse representation of the counts using
+scipy.sparse.coo_matrix.
+
+If you do not provide an a-priori dictionary and you do not use an analyzer
+that does some kind of feature selection then the number of features will
+be equal to the vocabulary size found by analyzing the data.
+
+#### Train Test Split
+
+Split arrays or matrices into random train and test subsets. Quick utility that wraps input validation and ``next(ShuffleSplit().split(X, y))`` and application to input data into a single call for splitting (and optionally subsampling) data in a oneliner.
+
+#### Naive Bayes classifier for multinomial models
+
+The multinomial Naive Bayes classifier is suitable for classification with
+discrete features (e.g., word counts for text classification). The
+multinomial distribution normally requires integer feature counts. However,
+in practice, fractional counts such as tf-idf may also work.
+
+### Requirements.txt
+
+- Click==7.0
+
+- Flask==1.1.2
+
+- pandas==1.1.0
+
+- sklearn==0.0
+
+- joblib==0.16.0
+
+- gunicorn==20.0.4
+
+- itsdangerous==1.1.0
+
+- Jinja2==2.10.3
+
+- MarkupSafe==1.1.1
+
+- Werkzeug==0.16.0
+
+### GUI
+
+<img title="" src="file:///guiimages/mainscreen.png" alt="">
+
+<img title="" src="file:///guiimages/spamscreen.png" alt="">
+
+<img title="" src="file:///guiimages/hamscreen.png" alt="">