malivinayak / HR_Analayzer

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HR_Analayzer

๐Ÿ˜€โ„๐•–๐•๐•๐•  ๐”ผ๐•ง๐•–๐•ฃ๐•ช๐• ๐•Ÿ๐•–๐Ÿ˜€,

๐Ÿ˜ŠI am delighted to share that, I deployed๐ŸŽ‰ ๐‘š๐‘ฆ ๐‘“๐‘–๐‘Ÿ๐‘ ๐‘ก project as a website. I've always thought that deployment is a very difficult process and I can't make it. But now after my first deployment, everything seems easy.

๐Ÿš€๐—ฃ๐—ฟ๐—ผ๐—ท๐—ฒ๐—ฐ๐˜ : ๐—›๐—ฅ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜‡๐—ฒ๐—ฟ HR Analyzer is data analytics mini project created using logistic regression.

๐Ÿ’ฏ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด: โœด ๐ŸŽ€ ๐ˆ๐Ÿ ๐ฒ๐จ๐ฎ ๐ฐ๐š๐ง๐ญ ๐ญ๐จ ๐๐จ ๐ฌ๐จ๐ฆ๐ž๐ญ๐ก๐ข๐ง๐  ๐ญ๐ก๐ž๐ง ๐๐จ๐ง'๐ญ ๐ญ๐ก๐ข๐ง๐ค ๐š๐ง๐ ๐œ๐ซ๐ž๐š๐ญ๐ž ๐ฎ๐ง๐ง๐ž๐œ๐ž๐ฌ๐ฌ๐š๐ซ๐ฒ ๐œ๐ก๐š๐จ๐ฌ, ๐ฃ๐ฎ๐ฌ๐ญ ๐ ๐จ ๐Ÿ๐จ๐ซ ๐ข๐ญ. ๐ŸŽ€ โœด

๐Ÿค”๐๐ซ๐จ๐›๐ฅ๐ž๐ฆ ๐’๐ญ๐š๐ญ๐ž๐ฆ๐ž๐ง๐ญ: A client is a large MNC and they have 9 broad verticals across the organization. One of the problem client is facing is around identifying the right people for promotion and prepare them in time. Currently the process, they are following is lengthy and hectic, this leads to delay in transition to their new roles. Hence, company needs our help in identifying the eligible candidates at a particular checkpoint so that they can expedite the entire promotion cycle. They have provided multiple attributes around Employee's past and current performance along with demographics. Now, The ๐˜๐—ฎ๐˜€๐—ธ is to predict whether a potential promote at checkpoint in the test set will be promoted or not after the evaluation process.

๐Ÿ’ก๐—ฆ๐—ผ๐—น๐˜‚๐˜๐—ถ๐—ผ๐—ป: https://lnkd.in/dJEDMAsH

๐Ÿ“ƒ๐ƒ๐š๐ญ๐š๐ฌ๐ž๐ญ ๐ƒ๐ž๐ฌ๐œ๐ซ๐ข๐ฉ๐ญ๐ข๐จ๐ง๐Ÿ“ƒ ๐’๐’‚๐’Ž๐’† - Name of employee
๐’…๐’†๐’‘๐’‚๐’“๐’•๐’Ž๐’†๐’๐’• - Department of employee
๐’“๐’†๐’ˆ๐’Š๐’๐’ - Region of employment
๐’†๐’…๐’–๐’„๐’‚๐’•๐’Š๐’๐’ - Education Level
๐’ˆ๐’†๐’๐’…๐’†๐’“ - Gender of Employee
๐’“๐’†๐’„๐’“๐’–๐’Š๐’•๐’Ž๐’†๐’๐’• ๐’„๐’‰๐’‚๐’๐’๐’†๐’- Channel of recruitment
๐’๐’ ๐’๐’‡ ๐’•๐’“๐’‚๐’Š๐’๐’Š๐’๐’ˆ๐’” - no of other trainings completed in previous year on soft skills, technical skills etc.
๐’‚๐’ˆ๐’† - Age of Employee
๐’‘๐’“๐’†๐’—๐’Š๐’๐’–๐’” ๐’š๐’†๐’‚๐’“ ๐’“๐’‚๐’•๐’Š๐’๐’ˆ - Employee Rating for the previous year
๐’๐’†๐’๐’ˆ๐’•๐’‰ ๐’๐’‡ ๐’”๐’†๐’“๐’—๐’Š๐’„๐’† - Length of service in years
๐‘ฒ๐‘ท๐‘ฐ๐’” ๐’Ž๐’†๐’• >80% - if Percent of KPIs >80% then 1 else 0
๐’‚๐’˜๐’‚๐’“๐’…๐’” ๐’˜๐’๐’? - if awards won during previous year then 1 else 0
๐’‚๐’—๐’ˆ ๐’•๐’“๐’‚๐’Š๐’๐’Š๐’๐’ˆ ๐’”๐’„๐’๐’“๐’† - Average score in current training evaluations

๐Ÿ‘จโ€๐Ÿ’ป๐‘ป๐’†๐’„๐’‰๐’๐’๐’๐’๐’ˆ๐’Š๐’†๐’”/๐‘ณ๐’‚๐’๐’ˆ๐’–๐’‚๐’ˆ๐’†๐’”: Python, Flask, Logistic Regression, Data Analytics, HTML, CSS, Git, Heroku, etcc

๐Ÿ“š๐‘ณ๐’Š๐’ƒ๐’“๐’‚๐’“๐’Š๐’†๐’” ๐’‡๐’๐’“ ๐’‚๐’๐’‚๐’๐’š๐’”๐’Š๐’” ๐’‚๐’๐’… ๐‘ด๐‘ณ: numpy, pandas, scipy.stats, seaborn, matplotlib, sklearn, etc

๐Ÿ–ฅ๏ธ๐‘ซ๐’†๐’‘๐’๐’๐’š๐’†๐’… ๐’๐’ : Heroku

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