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Data Scientist
June 18, 2026 – Present
food_delivery_backend_django
September 14, 2021 – September 14, 2021
food_delivery_backend_django — GitHub repository
View Projectdetection-of-width-of-cracks-in-concrte
February 27, 2021 – February 27, 2021
detection-of-width-of-cracks-in-concrte — GitHub repository
View ProjectVideo-player-App
December 29, 2020 – January 5, 2021
Video-player-App — GitHub repository
View ProjectSteel-Deformation-Detection
October 2, 2020 – October 3, 2020
Here I tried to classify between images of steel having deformation or not, using Deep learning techniques.
View ProjectCrack-Detection-in-Concrete
July 26, 2020 – March 12, 2021
In this, i have trained a model which detects whether the given image of concrete has crack or not. Dataset of 40k images(20k images of concrete with crack and 20k images of concrete without crack) was used to train and test the model. CNN architecture used is very similar to Yann LeCun. Raw JPG images data was pre-processed and augmented with the help of ImageDataGenerator class from Keras. After training model was giving around 98% accuracy.
View ProjectDecisionTrees
July 25, 2020 – March 12, 2021
In this, I have implemented Decision Trees from scratch. It prints all the decision tree steps for Iris dataset which is the part of Scikit learn library.
View ProjectFacialEmotionsRecognition
July 25, 2020 – March 12, 2021
Trained a model which classifies the expression (anger, surprise, happiness...etc) of the person. Dataset of around 13.5k images cloned from github. For image preprocessing ImageDataGenerator class from keras was used. After training, model was giving around 66% accuracy.
View ProjectText-Classification
July 22, 2020 – March 12, 2021
Here I used 20 News Group dataset in which around 11314 passages are given along with their groups(like religion, politics...etc). Here first i cleaned the data using NLP and later classification is done using sklearn's inbuilt Multinomial Classifier and Naive Bayes classifier(built from scratch) and accuracy of both classifier is being compared.
View ProjectCultural Fit Analysis
The candidate's projects show a strong interest in data science and machine learning, aligning with the target role of Data Scientist. The diversity of personal projects (image processing, NLP, fundamental ML algorithms) suggests a proactive learning attitude. However, the lack of team-based projects or professional experience makes it difficult to assess collaboration or broader cultural fit.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The candidate's experience level is listed as 0, and the only listed 'experience' is a current 'Data Scientist' role at 'None' starting in the future, which provides no operational context.