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-nitish847-covid-19-data-analysis-and-visualization-

This Notebook aims to explain the analysis of coronavirus spread in the world and across majorly affected countries using python. Seaborn and Matplotlib libraries are used to get beautiful visualizations. One can expect to learn how to code data manipulation and visualization from this file. Dataset used here is available on GitHub, Filename: "countries-aggregated.csv". Data here gets updated on a daily basis. This Dataset contains the record of 188 countries in terms of confirmed cases, recovered cases, and death cases from 22nd January 2020 to 26th May 2020.

all-csv-ml-data-files-download

Find all major CSV Files for data processing

highly-imbalanced-credit-card-fraud-detection-and-analysing-roc--and-auc.

This contains the fraud detection(Binary Classification problem) solved using Logistic regression. Highly Imbalanced problem is solved through SMOTE technique and Proper Data analysis and visualization is done and an AUC score of more than 99% is achieved

odi-match-prediction-machine-hack-hackathon

pandas-profiling-eda

predict-employee-turnover-churn

This file consists of complete python code on Predicting Employee Turnover. Predictive modelling using RandomForestClassifier is done. Hyper parameter tuning by comparing accuracy through various Hyperparameters' values is done. And High Accuracy and Recall score of more than 95% is achieved. Data set is available on Kaggle.