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https://github.com/prankshaw/ml-applications

A wide collection of various Machine learning applications including sentiment analysis, handwriting classification, recommendation engine and many more!!!!
https://github.com/prankshaw/ml-applications

classification fiters google-api handwritten-digit-recognition image image-manipulation machine-learning prediction recommendation-system sentiment-analysis tensorflow

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A wide collection of various Machine learning applications including sentiment analysis, handwriting classification, recommendation engine and many more!!!!

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README 

        
# Machine Learning Application

 Readme will be updated with complete description soon, website will be soon launched.


A wide collection of various Machine learning applications including sentiment analysis, handwriting classification, recommendation engine, color extraction, fashion recommender and identifier, Google API usage and many more!!!!




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    Some applications have been converted into web apps using Flask framework to simulate real world application of ML algorithms, and many more are yet to come.

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    Others can be interacted with, easily, by downloading the required file and having Jupyter Notebook installed on machine.

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    The google API usage files are complete code snippets which can be used just by copy-paste, with the only requuirement for an API key.

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    ## Description
    


    ### Amazon Apparel Product Recommendation System
    


    Applied Natural Language Feature Engineering and built a content based recommendation system using NLP Models (Bag of words and TF-IDF()TFIDF, short for term frequency–inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus.) in problem of recommending similar products on e-commerce websites by using Amazon product advertising API. 

    #### Tech Stack - Python, Bag of Words odel, TF-IDF, Word2Vec, NLP
    


    ### Handwritten Digit Recognition 
    


    Applied K Nearest Neighbours Algorithm in recognizing handwritten digits (0 to 9) from the MNIST dataset. Developed a system from scratch which includes a machine to understand and classify the images of handwritten digits as 10 digits (0–9). Accuracy of the model was around 98%. 

    #### Tech Stack - Python, ML(KNN), OpenCV, MNIST Database
    


    ### Dominant Color Extraction 
    


    Applied K Means Clustering Unsupervised Algorithm in Segmenting an image into set ok K Dominant Colors. Extracted the K Dominant Colors from the image and then re-colorized it using those K Colors. 

    #### Tech Stack - Python, ML(K-Means), OpenCV 
    


    ### Spam or Ham Identification Web App 
    


    Using Bag of words model we find the ‘term frequency’, i.e. number of occurrences of each word in the dataset, along with Term Frequency-Inverse Document Frequency to find probabality of each word, followed by additive smoothing.

    #### Tech Stack - Python, ML(Classification Algorithms)
    


    ### Tweets Sentiment Analysis
    


    It contains a basic use caseof Natural language processing, and using data classifiers on the dataset. Support vector machines are usedto effectively classify and accuracy is checked using F1 score and ROC-AUC curve.

    #### Tech Stack - Python, ML(Classification Algorithms)- SVM
    


    ### Movie Recommender System 

    ### Fashion Accessories Identification

    ### Flower Species Detection Web App
    


    ### Credits
    


    The above code and application produced are just some of the Machine Learning applications that I worked on, through these years, and I have selected some of the relevant and intersesting applications for this repository, to help others learn and understand the real world applications of machine learning, so as to aid them to get out of theoretical knowledge of books. These work are develoed throughout my college period, while studying machine learning and deep learning, with help of lots of books, blogs, youtube videos and many more, who have helped me learn. Some of them might be inspired from these teachings, and will have some changes and learnings of my own, while most are original work, produced after deep understanding of the underlying technique. I will appreciate anybody who is willing to contribute or learn can just fork the repository and May the training parameters forever be in your favour :)
     Thank You!!