About Me
Looking for an expert in Machine Learning, Data Science and Artificial Intelligence.
Let's talk,
As a freelancer, I'm turning my attention towards helping businesses making online. The Datasets here are different, but the e...
I have direct experience in the following technologies/topics:
The tools I use are:
pandas
numpy
seaborn
matplotlib
scikit-learn
seaborn
nltk
gensim
spacy
plotly
TensorFlow
Keras
Jupyter and Colab
I can work on:
Dimensionality Reduction
Regression
Classification
Clustering
random-forest
k nearest neighbours
naivebayes
Logistic Regression
svm
LinearRegression
Decision Trees
GBDT
XGBoost
LGBMClassifier
CatBoostClassifier
Stacking
K-Means
Hierarchical Clustering
Principle Component Analysis
Gradient Boosting
Ada Boost
Recommendation Systems
one hot encoding
Sentiment Analysis
Natural Language processing
Skills
Data & Analytics
Others
Software Engineering
Programming Language
Web Development
Positions
Portfolio Projects
Company
Severstal: Steel Defect Detection
Role
Machine Learning Engineer
Description
•Given an image of a steel sheet predict the type of defect from one of the four types of defects defined.
•pre-processed the Images using gamma correction, contrast enhancement and added Run-length encoding.
•Built a UNET model for localizing and classifying the surface defects on the steel sheet.
•Achieved a Dice coefficient of 0.82 for classifying images.
Skills
Data Science Artificial Intelligence OpenCV TensorFlow Keras Machine Learning Machine Learning Algorithms PythonTools
Jupyter Notebook NumPyCompany
Intel Image Classification
Role
Machine Learning Engineer
Description
• Predicted the scene of a given image using a neural network.
• Prepared, augmented using ImageDataGenerator in Keras in the Intel scene classification dataset for classification.
• Built a Deep learning Network to Classify images into 6 categories with an accuracy of 93 percent.
Skills
Artificial Intelligence Artificial Neural Networks Keras Machine Learning Machine Learning Algorithms Data Science Data Modeling Image ProcessingTools
Jupyter NotebookCompany
NETFLIX MOVIE RECOMMENDER SYSTEM
Description
•Predicted the rating that a user would give to a movie that he has not yet rated and Minimize the difference between predicted and actual rating.
•Merged movies with users and their rating in a single data frame, Computed User-User Similarity matrix and Movie-Movie similarity matrix, Featurized the data by selecting the top 5 ratings given by similar users to a particular user and also top 5 ratings given to similar movies with respect to a particular movie.
•worked with different machine learning models and Achieved a Root Mean Square Error of less than 1.08.
Skills
Machine Learning Machine Learning Algorithms Data Cleansing Data Modeling Data Science NumPy SciKit-Learn Matplotlib SeabornTools
Jupyter NotebookCompany
NEWYORK TAXI DEMAND PREDICTION
Role
Data Scientist
Description
•Predicted the number of pickups of a given Location Using a Regression Model.
•Cleaned the data based on several features, got different regions based on clusters, created the different baseline models for different months, taken the previous five values as the feature, included the exponential average forecasting feature and Fourier Transform Features.
•applied the different models like linear Regression, Random Forest and XgBoost Regression models and achieved a mean absolute percentage error of less than 12%.
Skills
Machine Learning Machine Learning Algorithms Data Cleansing Data Modeling Data Science Artificial IntelligenceTools
Jupyter NotebookCompany
SELF DRIVING CAR
Role
Machine Learning Engineer
Description
• Implemented End to End Deep learning Network to predict the degree of the steering angle with the help of 25 minutes of Footage.
• Used convolutional neural networks (CNNs) to map the raw pixels from a front-facing camera to the steering commands for a self-driving car.
Tools
Jupyter NotebookCompany
Stack Overflow: Tag Prediction
Description
•Predicted the tags based on the content that was there in the question posted on Stack overflow.
•Predicted as many tags as possible with high precision and recall.
•Performed NLP using Bag of Words, Tf-idf Vectorizer and Classified the Tags Using Logistic, linear regression models.
•Achieved Hamming Loss of less than 0.0004 and a macro-f1 score of less than 0.09.
Skills
Machine Learning Machine Learning Algorithms Data Cleansing Data Modeling Data Science Natural Language ProcessingTools
Jupyter NotebookCompany
Quora question pair similarity
Description
• Identify which questions asked on Quora are duplicates of questions that have already been asked.
• Achieved a Log-loss of 0.36
Skills
Natural Language Processing The Natural Language Toolkit - NLTK Data Cleansing Data Modeling Data Science Machine Learning Machine Learning AlgorithmsTools
Jupyter NotebookCompany
AMAZON FINE FOOD REVIEWS
Description
• Performed sentiment analysis to classify the polarity of the review given by the Amazon user , given the textual reviews and related features of the product.
• Performed Exploratory Data Analysis, data cleaning, data visualization and text featurization (BOW, tfidf, word2vec).Buildseveral ML models like KNN, Logistic regression,SVM,Random Forest,GBDT,LSYM(RNNs)etc.
Skills
Natural Language Processing Data Cleansing The Natural Language Toolkit - NLTK Machine Learning Machine Learning Algorithms LSTM Artificial Intelligence Data Modeling Data Science PandasTools
Jupyter Notebook