A submission for HUAWEI - 2020 DIGIX GLOBAL AI CHALLENGE
This is a very Important part of Data Science Case Study because Detecting Frauds and Analyzing their Behaviours and finding reasons behind them is one of the prime responsibilities of a Data Scientist. This is the Branch which comes under Anamoly Detection.
It is a Problem Which I got During the ZS Data Science Challenge From Interview Bit Hiring Challenge Where I secured a 40th Rank out of 10,000 Students across India. It is a Dataset which requires Intensive Cleaning and Processing. Here I have Performed Classification Using Random Forest Classifier and Used Hyper Tuning of the Parameters to achi…
Deployed model to predict total sales for every item and shop for the next month, from a time-series dataset consisting of daily sales data
Exploring categorical features with various encodings and models
HackerEarth Machine Learning challenge: Of Genomes And Genetics
A machine learning model to accurately predict house prices based on various features such as quality, size, and location, utilizing Random Forest and XGBoost algorithms (Python)
A set of tools for machine learning (for the current day, there are active learning utilities and implementations of some stacking-based techniques).
Прогнозирование рыночной стоимости автомобилей
Data preprocessing for machine learning modelling. Quantile transformation for the outliers removal, replacing NULLs with medians, using target encoder and Z-score standardisation for the numeric variables.
TCD ML Comp. 2019/20 - Income Prediction (Ind.)
Creating a sophisticated web application for transaction analysis, incorporating ML, Bootstrap, Dash, and Plotly. Users can seamlessly upload credit card CSV files, exploring transactions interactively in both tabular and dashboard report formats.
A python machine learning library for advanced feature extraction and interpretation.
Final project for "How to win a data science competition" Coursera course
This repo contains code for experimenting with categorical encoding - WoE, Catboost, Target encoder, and many more.
Materials from a paper/talk for Southeast SAS User Group Conference
It contains the code and data for M5 Forecasting - Accuracy competition on Kaggle.
Life expectancy data processing
Encode Categorical Features based on Target/Class
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