
Nidhi is Data Science Alumni of IIIT-B. She has a perfect mix of industrial & technical training experience of about 13+ years. She is heading FactualAI.
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FactualAI
Data Scientist
June 13, 2026 – Present
prediction-model-deployment
October 1, 2020 – October 1, 2020
prediction-model-deployment — GitHub repository
View ProjectImage_Classification_CNN
March 20, 2020 – March 20, 2020
Given a set of different dog breed images, built an image classifier to determine the breed of a dog in the image.In this project we built traditional CNN, CNN with data augmentation and finally transfer Learning by VGG16 model with weights pre-trained on Imagenet to solve the dog breed classification problem..
View ProjectFaceRecognition_Keras
March 20, 2020 – March 20, 2020
The Task is to Recognize Faces in the images taken from Pinterest
View ProjectCNN_DigitRecognition
March 20, 2020 – March 20, 2020
In this notebook, we will build a simple CNN-based architecture to classify the 10 digits (0-9) of the MNIST dataset. The objective of this notebook is to become familiar with the process of building CNNs in Keras. implement all the steps required to build a network - feedforward, loss computation, backpropagation, weight updates etc. We will go through the following steps: Importing libraries and the dataset Data preparation: Train-test split, specifying the shape of the input data etc. Building and understanding the CNN architecture Fitting and evaluating the model
View ProjectPolynomial_Regression
March 10, 2020 – March 10, 2020
Problem Statement: The objective of the problem is to predict values “current price” attribute from the given features of the Test data. Essentially, the company wants — To identify the variables affecting cars current prices, To create a linear model that quantitatively relates cars current prices with identified significant variables. To know the accuracy of the model, i.e. how well these variables can predict car prices. So interpretation is important!
View ProjectRandomForest_CreditDefault_Prediction
March 10, 2020 – March 10, 2020
build a random forest model to predict whether a given customer defaults or not. Credit default is one of the most important problems in the banking and risk analytics industry. There are various attributes which can be used to predict default, such as demographic data (age, income, employment status, etc.), (credit) behavioural data (past loans, payment, number of times a credit payment has been delayed by the customer etc.).
View ProjectRandomForest_AdaBoost
March 10, 2020 – March 10, 2020
In the pronblem given the task is to predict the volume of passengers in trains. This problem is based on MultiClass Classification Problem as the target column has 3 classes : low, medium and high. This Classification problem is best solved using Random Forest Classifier with Boosting Algorithm in order to have better accuracy as well as precision and recall and thus higher f1 score.
View ProjectRecommandation-System
March 10, 2020 – March 10, 2020
You are the sales manager for "BeerMart", an online beer store in the United States. You want to build a recommendation system (collaborative) for your store, where customers will be recommended the beer that they are most likely to buy.
View ProjectLogistic-Regression-Decision-Tree--Random-Forest
March 10, 2020 – March 10, 2020
Logistic-Regression-Decision-Tree--Random-Forest — GitHub repository
View ProjectClustering-PCA
March 10, 2020 – March 10, 2020
HELP International is an international humanitarian NGO that is committed to fighting poverty and providing the people of backward countries with basic amenities and relief during the time of disasters and natural calamities. It runs a lot of operational projects from time to time along with advocacy drives to raise awareness as well as for funding purposes. After the recent funding programmes, they have been able to raise around $ 10 million. Now the CEO of the NGO needs to decide how to use this money strategically and effectively. The significant issues that come while making this decision are mostly related to choosing the countries that are in the direst need of aid.
View ProjectCultural Fit Analysis
The candidate's projects are primarily personal and academic in nature, focusing on core data science algorithms. While this demonstrates technical interest, there is limited evidence of collaborative work, diverse team environments, or contributions to open-source projects. The single professional experience listed is current with no end date, making it difficult to assess long-term cultural alignment or adaptability. The target role is 'Data Scientist', which aligns with the technical skills demonstrated, but the breadth of experience beyond core ML algorithms is not evident.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions indicate a focus on technical problem-solving.