Jaya Tiwari

Results-driven data scientist with expertise in machine learning and deep learning algorithms. Experienced in analyzing biomedical images and developing models for cancer detection. Skilled in logistic regression, descriptive analysis, hierarchical clustering, linear regression, decision trees, and credit card scoring. Strong problem-solving and analytical abilities.

• Data science with SAS and R from Jigsaw Academy, Bangalore, India.
• ISTQB (International Software Testing Qualification Board, Foundation Level) certification – Foundation Course.

Deep Learning Model: UNET

• Breast Cancer detection using UNET architecture where we explored the various variants of UNET and identified the best variant with highest dice score for breast cancer detection.

• Differential analysis of Breast cancer which includes:

§ Classification of Breast tumour.

§ Determination of the size of tumour using OpenCV functions.

§ Differential Analysis by determining the growth rate of Breast tumour.

§ Differential Analysis of Breast Tumour by checking its status (Progressive, Regressive, New Tumour)

§ Determine the direction of progression or regression of Breast tumour.

§ Evaluation of the Model performance (Base UNet and Residual UNet)

Deep Learning Model: CNN 

· Participated in Hackathon to build a deep learning model to distinguish between Fake and Real images that were created using GAN

Regression Model:

• Built a model to predict apparent temperature using the weather dataset.

· Participated in hackathon to build a regression model to predict the price of houses in Bangalore City.

Classification Model:

· Participated in hackathon to build a model to predict whether a passenger was delighted considering his/her overall travel experience of traveling in Shinkansen (Bullet Train).

Clustering Model:

· Participated in hackathon to do clustering of similar patients for a given health care dataset based on various health parameters.

Time series Analysis:

· Participated in Hackathon and built a time series model to forecast the monthly sunspot count using the monthly average sunspot data.

Data Visualization:

· For the Maven Northwind challenge, built a top-level KPI dashboard for the executive team. Its purpose should be to allow them to quickly understand the company's performance in key areas, including Sales Trend, Product performance, key customer, Shipping cost.