Srinidhi K.

Srinidhi K.

Product Developer with experience of over 5 years and a Certified Data Science Professional.

Bengaluru , India

Experience: 5 Years

Srinidhi

Bengaluru , India

Product Developer with experience of over 5 years and a Certified Data Science Professional.

24022.1 USD / Year

  • Notice Period: 45 Days

5 Years

Now you can Instantly Chat with Srinidhi!

About Me

Product Developer with experience of over 5 years and a Certified Data Science Professional. Presently working on API development, Android application development, and independent case studies based on machine learning and deep learning. Adept at ...

Show More

Portfolio Projects

Sentence Correction using Recurrent Neural Network

Company

Sentence Correction using Recurrent Neural Network

Description

Research paper: https://cs224d.stanford.edu/reports/Lewis.pdf
Objective: This paper proposes an efficient pre-processing method for changing text data to come closer to standard English
that will improve the performance of state-of-the-art NLP models.
Models: Vanilla Encoder-Decoder, Encoder-Decoder with Bahadanau Attention
• Implemented the Bahadanau Attention based on the research paper - https://arxiv.org/pdf/1409.0473.pdf
Results: An average BLEU score of 0.77 with LSTM units, data augmentation, and Bahadanau Attention implementation

Show More Show Less

Kaggle: Instacart Market Basket Analysis

Company

Kaggle: Instacart Market Basket Analysis

Description

Objective: To use Instakart’s outsourced data on customer orders over time to predict which previously purchased products will
be in the user’s next order.
Models:Logistic Regression, Decision Tree Classifier, Random-Forest (ensemble), Light-GBM, XGBoost, CatBoost
Featurization: User-related features, product-related features, User x Product-related features.
• Implemented the Custom Ensemble Classifier based on the research paper -
https://pdfs.semanticscholar.org/449e/7116d7e2cff37b4d3b1357a23953231b4709.pdf
Results: An F1-Score of 0.365,0.371,0.367,0.377,0.376,0.379 and 0.377 in the order of models mentioned above.

Show More Show Less

DonorsChoose.org Application Screening

Company

DonorsChoose.org Application Screening

Description

Objective: To predict whether or not a DonorsChoose.org project proposal submitted by a teacher will be approved.
Models: Naive Bayes, Decision Trees, Gradient Boosted Decision Trees
Featurization :For Text features - BOW,TFIDF,W2V,Sentient Analysis. For Categorical Features - One hot encoding, Response coding. For Numerical Features - Normalization.
Results: Achieved an AUC score of 0.62,0.69 for Decision Trees, and GBDT

Show More Show Less