Akash Naskar's repositories
amazon_fine_foods_reviews
Given a review in Amazon, determine whether the review is positive (Rating of 4 or 5) or negative (rating of 1 or 2).
Amazon-Fashion-Discovery-Engine
Recommend similar apparel searched by user on Amazon.
Amazon-Recommendation-engine
Built a content based recommendation engine for recommending apparel items or products at Amazon, using text and image data retrieved from website.
implementing-tensorflow
A series of Jupyter notebooks that walk you through the fundamentals of Deep Learning in Python using TensorFlow 2 (mostly, LOL)
NLP_Research
Contains all project codes regarding my research project.
Santander_customerSatisfaction
In this problem, a company wants to find out which customers are mostly using these services. So that it can focus more on those customers and serve them better using Loyalty score • After trying with logistic regression,svm,RF, DT we found out Lightgbm performs fastest and gives the best score
Taxi_Time_Pred
Our objective is to build a model that predicts the total ride duration of taxi trips in New York City. For this we were given a primary dataset released by the NYC Taxi and Limousine Commission, which included pickup time, geo-coordinates, number of passengers, and several other variables.
Test_Bench_Time_Reduction_for_MercedesBenz
You are required to reduce the time that cars spend on the test bench. Others will work with a dataset representing different permutations of features in a Mercedes-Benz car to predict the time it takes to pass testing. Optimal algorithms will contribute to faster testing, resulting in lower carbon dioxide emissions without reducing Daimler’s standards.
deep-learning-drizzle
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
DSA_FAANG_Python
DSA in Python from basic to advanced. Improving my programming skills here.
Email_Campaign_Effectiveness_Prediction
Email Campaign Effectiveness Prediction
football-tracking-data-from-TV-broadcast
get football tracking data from TV broadcast using yoloV5, Deep sort
lstm_donors_choose
Keras functional api on DonorsChoose dataset.
Predicting-how-effective-an-email-campaign-will-be
Predicting how effective an email campaign will be.
Predictive-Maintenance-using-ML
Predictive Maintenance using Machine Learning
Resume-and-CV-Summarization-and-Parsing-with-Spacy-in-Python
Resume and CV Summarization and Paring with Spacy in Python
Sentiment_Analysis_of_Amazon-Products
Project on the sentiment analysis of Amazon reviews based on ML algorithms
Statistics-for-Data-Science
Learning Statistics is one of the most Important step to get into the World of Data Science and Machine Learning. Statistics helps us to know data in a much better way and explains the behavior of the data based upon certain factors. It has many Elements which help us to understand the data better that includes Probability, Distributions, Descriptive Analysis, Inferential Analysis, Comparative Analysis, Chi-Square Test, T Test, Z test, AB Testing etc.
TensorFlow-Tutorials
TensorFlow Tutorials with YouTube Videos
upgrad_pgdmlai
assignments and group case studies from PGDMLAI course by upGrad & IIITB