Nitin Nandeshwar's Projects
Done EDA using Python and Tableau to identify patterns that indicate if a client has difficulty paying their installments which may be used to taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc
Python
Performed EDA, Data Pre-processing, Imbalance data and Supervised Machine learning to predict customer transaction is fraud using features such as services that customer has signed up for, customer account information, and demographic information about the customer.
Applied Clustering algorithm(K-means) for Customer Segmentation, which is partitioning a customer base into groups of individuals with similar characteristics.
Machine learning datasets used in tutorials on MachineLearningMastery.com
To identify the factors that lead to the high readmission rate of diabetic patients within 30 days post-discharge. Also, to identify the medicines that are the most effective in the treatment. (Primary Evaluation Metric: Recall).
Built a model from patients' historical data and their response to different medications. Used the trained decision tree to predict an unknown patient's class or find a proper drug for a new patient.
Explored image dataset and used data augmented technique to enhance the size of images. Further fade date into many custom models and pre-trained models (VGG, Inception, RSNET) to gain accuracy used terminology such as ensemble, transfer learning, and fine-tuning.
Holt-Winter, ARIMA, SARIMA, Prophet forecasting
Linkedin quiz assessment, answers and questions (aws-lambda, rest-api, javascript, react, git, html, jquery, maven, mongodb, xml, java, css, python ...) ответы на квиз
Deploy on AWS EC2, Flask, OneHotEncoder, LabelEncoder, Random Forest
Applying time series to predict Smart Home energy consumption and demonstrating how data can enable households to save energy (IoT Analytics)
VGG, Transfer Learning, AWS EC2,Flask
PLC Programming
Web scraping is a very powerful tool to learn for any data professional. With web scraping, the entire internet becomes your database. In this repository how to parse a web page into a data file (CSV) using a Python package called BeautifulSoup.
Create an excel report that contains all the meaningful information such as relevant charts, pivot tables, etc. Mention all the variable which are highly correlated. Used the linear regression model to train and forecast the houses sold in the year 2017 based on 2016 data. Interpret essential findings from the model.
Real Time Face Recognition in python using face_recognition library
Used UCI Dataset and Natural language processing (NLP) for classifying customer reviews and analyzing the sentiment as positive/happy or negative/sad.
ARM Controller, PT100, CNC Machine, Matlab Simulink
UR5 control using python programming