SHAMINE (RID : 474kwbrsbmh)

designation   Jr. Machine Learning/NLP Engineer

location   Location : Mumbai

experience   Experience : 1 Year

rate   Rate: $16 / Hourly

Availability   Availability : Immediate

Work From   Work From : Offsite

designation   Category : Information Technology & Services

Last Active: 30+ Days ago
Shortlisted : 1
Total Views : 8
Key Skills
Python Machine Learning Tableau NLP
Discription
  • Created a network graph of skills and related skills. Applied community detection technique to find different related skills and find the shortest path for a particular skill.
  • Performed TF-IDF, SVD, Hashing, Binning on attributes to predict salary (regression analysis) and predict salary range (classification analysis).
  • The future three roles were predicted for a current role various techniques used were MultiOutputClassifier, ClassifierChain, Neural Network, and the best model was selected.
  • Trained the data using FastText library to find Skills and Roles which were semantically similar.
  • Predicted max. 4 Roles based on Technical Skills (multilabel problem) using different Classifiers.
  • Trained data using T5 model to generate job description for a given Role, Industry and Technical Skills

PROJECT: CREATING A CHATBOT

NLP was applied to clean the questions and answers. 

For creating a Chatbot LSTM neural network was used.

 PROJECTS

BUILD A CAT BREED IMAGE CLASSIFICATION MODEL WITH THE INCEPTION CNN ARCHITECTURE (1/07/2021) 

The dataset is of 10 breeds of cats, trained the dataset using the Inception CNN architecture.

Dataset is split into training and testing sets using “image_dataset_from_library” and then images are trained using RMSprop optimizer.


DETECTION OF FAKE NEWS VIA NLP (26/08/2020)

Cleaned the dataset using Python tools, used techniques like tokenization, stopwords, lemmatization, Tf-idf vectorization. 

Used Logistic regression & Random Forest supervised techniques on the data for detection. 

Used LIME to reflect the contribution of each feature to the prediction of a data sample.


A STUDY OF MOBILE PRICE DATASET USING DIFFERENT CLASSIFICATION TECHNIQUES

Imputed missing values, used Extra Trees Classifier for selecting the top 10 features.

The price of mobile was predicted using Neural Network and other classification techniques like Random Forest, Naïve Bayes, K-Nearest Neighbor, Decision Tree to select the model which gives the highest accuracy and 

less bias. 








 
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