Always know what to expect from your data.
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Updated
May 31, 2024 - Python
Always know what to expect from your data.
OpenRefine is a free, open source power tool for working with messy data and improving it
As the name suggests, this application helps banks decide whether a loan should be sanctioned by assessing various factors from the borrower's profile.
This involves representing data graphically through charts, graphs, maps, and other visual elements. Interactive dashboards and reports that allows users to ask ad-hoc questions, test hypotheses, and gain deeper insights by engaging with the visual data directly.
BootCamp del profe alejo trabajaremos el problema de covid
Installer for DataKitchen's Open Source Data Observability Products. Data breaks. Servers break. Your toolchain breaks. Ensure your team is the first to know and the first to solve with visibility across and down your data estate. Save time with simple, fast data quality test generation and execution. Trust your data, tools, and systems end to end.
In this notebook, I have done Data Cleaning, Data Wrangling, EDA and Feature Engineering. After that I trained the dataset using Machine Learning Algorithm Random Forest Regressor.
This dashboard includes interactive visualizations and reports on employee attendance, preferred working modes, and utilization of work-from-home and sick leave policies, demonstrating the impact on strategic decision-making and employee wellbeing
Data Cleaning and Exploratory Data Analysis with SQL in MySQL Workbench
This repository build to showcase a project on customer personality analysis. In this I have classified the customer into two clusters.
Developed a comprehensive HR analytics dashboard using Power BI. Addressed the challenges faced by HR professionals in volatile market conditions, including fluctuating salaries, employee retention, and recruitment. Integrated real-time data visualization and insights to aid decision-making processes.
cleanPyData is a Python package for data cleaning and preprocessing. It handles missing values, normalizes data, extracts features, and detects outliers, making your data ready for analysis or machine learning.
Here I conducted EDA on a diverse datasets, including movies, sales, and gaming data. Did data cleaning, visualization, and interpretation using libraries like pandas, NumPy, Matplotlib, and Seaborn to extract actionable insights for informed decision-making processes.
Vrinda Store
This project revolves around the analysis and prediction of customer churn using a dataset sourced from Kaggle. The dataset underwent thorough cleaning in Python to address missing values, invalid data types, and normalization tasks. The analysis was subsequently visualized using Power BI. Lastly, a Random Forest model was employed for prediction,
Collection of the assignments for Data Science Engineering Methods on National Stock Exchange Dataset and TMNIST dataset
Car Insurance Prediction Project
Using Python as a pipe to load data into data storage systems.
This project analyses the sales data of Vrinda Store to understand customer behavior and ultimately increase sales in the coming year.
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