naikshubham Goto Github PK

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Name: Shubham S. Naik

Type: User

Bio: Artificial Intelligence

Location: Pune

Shubham S. Naik's Projects

amazon-reviews-classifier

Objective is to create a ML model that will classify the Review as positive or negative using k-NN.

automated-incident-metrics

This script will automatically create the Incident Metrics and trigger automatic mail.

bank-cheque-ocr

Used computer vision with OCR to extract feilds from bank cheques, thereby automating the process of cheque processing in Banks.

Fundamentals of image analysis using NumPy, SciPy, and Matplotlib. We'll navigate through a whole-body CT scan, segment a cardiac MRI time series, and determine whether Alzheimer’s disease changes brain structure.

building-webapps-with-r-shiny

Building interactive web applications with R shiny.

c

my old C codes... sandbox

categorical-data-ml

Deal with Categorical data to solve data problems

cnn-hyperparameter-tuning-using-hyperas

Convolutional Neural Network Hyperparameter tuning using Hyperas and Hyperopt. The advantage of hyperas over sklearn GridSearchCV and RandomSearchCV is parallel execution using GPUs.

credit-card-fraud-detection

Objective is to find the top 10 similar transactions for any given transaction in the dataset using Linear Algebra.Calculate similarity(i,j) = cosine^-1(dot product (vi, vj) / (length(vi) * length(vj)) ) Find out top 10 transactions in the dataset which have the lowest similarity(i,j).

credit-risk-modeling

Prepare credit application data. After that, apply machine learning and business rules to reduce risk and ensure profitability.Ever applied for a credit card or loan, we know that financial firms process our information before making a decision. This is because giving us a loan can have a serious financial impact on their business. But how do they make a decision?

customer-segmentation-in-python

Customer Segmentation in Python

data-engineering

Data Engineering tasks

data-scientist-track

All necessary stuff to be a Data Scientist

data-visualization-in-python

Various data vizualizations

deep-learning-using-python

This repository contains keras/TensorFlow/Pytorch code for building Deep Learning models on datasets.

designing-machine-learning-workflows

Deploying machine learning models in production seems easy with modern tools, but often ends in disappointment as the model performs worse in production than in development. How to exhaustively tune every aspect of our model in development; how to make the best possible use of available domain expertise; how to monitor our model in performance and deal with any performance deterioration; and finally how to deal with poorly or scarcely labelled data. Digging deep into the cutting edge of sklearn, and dealing with real-life datasets from hot areas like personalized healthcare and cybersecurity.

distance-calculator-webapp-using-flask-and-google-map-api

In this project I have created a distance calculator Webapp and hosted it on cloud using Python's Flask Framework. User needs to enter the source and destination the Python code will then call Google Maps API and display the distance and time to reach the destination from source. You can access the webapp using https://naikshubham.pythonanywhere.com

face-identification

Detect face in a picture and recognize the identity of the face.

feature-engineering-ml

Feature Engineering techniques for ML

forecasting-using-python

Used ARIMA class models to forecast the future.

gender_predictor-ml

This is a simple data analysis project using sci-kit learn. In this project we have taken a sample data of Male and Female containing height,weight and shoe size. Later using DecisionTree classifier we are predicting whether the given pair of height,weight and shoesize is of Male or Female.

generalized-linear-models-glm

Extend regression toolbox with the logistic and Poisson models, by learning how to fit, understand, assess model performance and finally use the model to make predictions on new data.

gradient-boosting-with-xgboost

Gradient boosting is currently one of the most popular techniques for efficient modeling of tabular datasets of all sizes. XGboost is a very fast, scalable implementation of gradient boosting, with models using XGBoost regularly winning online data science competitions and being used at scale across different industries.

grand-travel-booking-portal-using-flask

Travel booking Webapp using Flask and python.

image-clustering-using-convnets-transfer-learning-and-k-means-

Image Clustering using Convnets Transfer Learning and KMeans. Builds clusters of similar Images.

importing-managing-financial-data-in-python

Data Science Skills for financial data

interactive-dictionary-using-python

I have build a code which does the function of Dictionary, it will ask user to enter a word and show the meaning of that word. Apart from that ,additional features included are auto similar words recognition.

introudction-to-mongodb-in-python

MongoDB is a tool to explore data structured as you see fit. As a NoSQL database, it doesn't follow the strict relational format imposed by SQL. By providing capabilities that typically require adding layers to SQL, it collapses complexity. With dynamic schema, you can handle vastly different data together and consolidate analytics. The flexibility of MongoDB empowers you to keep improving and fix issues as your requirements evolve.

iris_data_analysis_model

Toy Dataset: Iris Dataset: [https://en.wikipedia.org/wiki/Iris_flower_data_set] A simple dataset to learn the basics. 3 flowers of Iris species. [see images on wikipedia link above] 1936 by Ronald Fisher. Petal and Sepal: http://terpconnect.umd.edu/~petersd/666/html/iris_with_labels.jpg Objective: Classify a new flower as belonging to one of the 3 classes given the 4 features. Importance of domain knowledge.

learning-resources

"Technology Gold mine" to collect and share materials/resources

naikshubham Goto Github PK

Shubham S. Naik's Projects

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