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Assessing your cultural and operational fit
ChatBot
December 19, 2018 – December 19, 2018
This ChatBot is based on Python with NLTK. Its a basic chatbot.
View ProjectLanguage-Detection-From-Text---Bi-gram-based
May 6, 2018 – May 6, 2018
Language-Detection-From-Text---Bi-gram-based It uses Bi-gram language model and bi-gram frequency addition classifier for language identification task. Trained over 6 languages namely German, English, Spanish, French, Italian and Dutch. The original source of the text corpus is wortschatz leipzig corpora. Both the train and test corpus were taken from this corpora. The training corpus consists of 30000 sentences from news/web domain. Test corpus 10000 unseen sentences from news/web domain. Also, the chosen six languages were such that the same languages are present in the LIGA twitter dataset which consists of 9066 tweets. Note : Directory path used for train and test corpus in code language-test.py, language-train.py and liga_test.py needs to be properly set accordingly.
View ProjectPredict-the-Happiness-HackerEarth-Challenge
May 6, 2018 – May 6, 2018
It uses 2-layered fully connected/Dense Neural network model to predict whether the hotel reviews at TripAdvisor site are positive sentiment or negative sentiment. It is a python implementation utilizing Keras library for DNN. This problem statement came from a HackerEarth challenge: "Predict the Happiness" The accuracy score achieved was 88% when prediction file (sample_submisson.csv) is uploaded to their portal. The link for corpus/dataset download is given in blog-post.
View ProjectObject-recognition
May 6, 2018 – May 6, 2018
In this blog-post, we will demonstrate how to achieve 90% accuracy in object recognition task on CIFAR-10 dataset with help of following concepts: 1. Deep Network Architecture 2. Data Augmentation 3. Regularization
View ProjectSentiment-Analysis-using-tf-idf---Polarity-dataset
May 6, 2018 – May 6, 2018
It uses machine learning models to do sentiment polarity analysis on movie reviews. In other words, to classify opinions expressed in a text review (document) in order to determine whether the reviewer’s sentiment towards the movie is positive or negative.
View ProjectMail-Spam-Filtering
May 6, 2018 – May 6, 2018
Mail-Spam-Filtering It uses machine learning models to predict whether the email is spam or ligitimate. Best thing would be to follow my blog-post for implementation. The description about the steps to build a spam filter from scratch can be read from my blog: https://appliedmachinelearning.wordpress.com/2017/01/23/nlp-blog-post/ It is a python implementation using Naive Bayes Classifier and Support Vector Machines from Scikit-learn ML library. The results has been shown on two publicly open corpus. Ling-spam corpus Euron-spam corpus The link for corpus/dataset download is given in blog-post. Note : Directory path used for training and testing models in lingspam_filter.py and euron-spamfilter.py needs to be properly set accordingly.
View ProjectText-classification-and-clustering
May 6, 2018 – May 6, 2018
It demonstrates the example of text classification and text clustering using K-NN and K-Means models based on tf-idf features.
View ProjectTitanic-Sink-Analysis
April 9, 2018 – April 9, 2018
The project is based on statistical analysis with R, which provides the survival prediction based on age,sex ratio,tickets,male,female,children etc.
View ProjectCultural Fit Analysis
The candidate's projects are exclusively personal and academic in nature, focusing heavily on machine learning and data science. While this aligns with the 'Data Scientist' target role, the lack of team-based projects, professional experience, or diverse domain exposure limits the assessment of cultural fit. The projects demonstrate individual initiative and a strong interest in the field.
Soft Skills & Operational Fit
The provided data is insufficient to assess soft skills or operational fit. Project descriptions indicate a focus on technical implementation and problem-solving.