Predicting HR Churn with Python and Machine Learning

Employee turnover imposes a substantial financial burden, necessitating proactive retention strategies. The aim is to leverage HR analytics, specifically employing a systematic machine learning approach, to predict the likelihood of active employees leaving the company. Using a systematic approach for supervised classification, the study leverages data on former employees to predict the probability of current employees leaving. Factors such as recruitment costs, sign-on bonuses, and onboarding productivity loss are analysed to explain when and why employees are prone to leave. The project aims to empower companies to take pre-emptive measures for retention. Contributing to HR Analytics, it provides a methodological framework applicable to various machine learning problems, optimizing human resource management, and enhancing overall workforce stability. This research contributes not only to predicting turnover but also proposes policies and strategies derived from the model's results. By understanding the root causes and timing of employee departures, companies can proactively implement measures to mitigate turnover, thereby minimizing the associated financial and operational burdens

Coordinated analysis of two graphite grains from the CO3.0 LAP 031117 meteorite: First identification of a CO Nova graphite and a presolar iron sulfide subgrain

Presolar grains constitute remnants of stars that existed before the formation of the solar system. In addition to providing direct information on the materials from which the solar system formed, these grains provide ground-truth information for models of stellar evolution and nucleosynthesis. Here we report the in-situ identification of two unique presolar graphite grains from the primitive meteorite LaPaz Icefield 031117. Based on these two graphite grains, we estimate a bulk presolar graphite abundance of 5-3+7 ppm in this meteorite. One of the grains (LAP-141) is characterized by an enrichment in 12C and depletions in 33,34S, and contains a small iron sulfide subgrain, representing the first unambiguous identification of presolar iron sulfide. The other grain (LAP-149) is extremely 13C-rich and 15N-poor, with one of the lowest 12C/13C ratios observed among presolar grains. Comparison of its isotopic compositions with new stellar nucleosynthesis and dust condensation models indicates an origin in the ejecta of a low-mass CO nova. Grain LAP-149 is the first putative nova grain that quantitatively best matches nova model predictions, providing the first strong evidence for graphite condensation in nova ejecta. Our discovery confirms that CO nova graphite and presolar iron sulfide contributed to the original building blocks of the solar system.Peer ReviewedPostprint (author's final draft

INFLUENCE OF METAL NITRATE TO FUEL RATIO ON THE MAGNETIC PROPERTIES OF NIFE2O4

Objective: Nickel ferrite nanoparticles with dimensions below 30 nm have been synthesized by sol-gel auto-combustion process. The nitrate-citrate gels were prepared from metal nitrates and citric acid solutions under various molar ratios of the metal nitrate to citric acid of 1, 2, 3 4 and 5 by sol-gel process. The results showed that nitrate citrate gels exhibit a self propagating behaviour after ignition in air at room temperature. The ratio of nitrates to citric acid also affects the combustion process. The as-prepared powder was annealed at 5000C for 6 hrs. The phase composition and structural properties of the obtained samples are investigated by X-ray diffraction (XRD) and Scanning Electron Microscopy (SEM). Analysis of the XRD patterns showed the presence of α-Fe2O3 phase and other refractions corresponding to cubic spinel structure. The lattice constant obtained from XRD data increases with metal nitrate to fuel (citric acid) ratio. PACS No: 75.50.Gg, 74.25.Ld, 43.35.C

Changes in biological productivity associated with Ningaloo Niño / Niña events in the southern subtropical Indian Ocean in recent decades

Using observations and long term simulations of an ocean-biogeochemical coupled model, we investigate the biological response in the southern subtropical Indian Ocean (SIO) associated with Ningaloo Niño and Niña events. Ningaloo events have large impact on sea surface temperature (SST) with positive SST anomalies (SSTA) seen off the west coast of Australia in southern SIO during Ningaloo Niño and negative anomalies during Niña events. Our results indicate that during the developing period of Ningaloo Niño, low chlorophyll anomaly appears near the southwest Australian coast concurrently with high SSTA and vice-versa during Niña, which alter the seasonal cycle of biological productivity. The difference in the spatiotemporal response of chlorophyll is due to the southward advection of Leeuwin current during these events. Increased frequency of Ningaloo Niño events associated with cold phase of Pacific Decadal Oscillation (PDO) resulted in anomalous decrease in productivity during Austral summer in the SIO in the recent decades

Automatic semantic segmentation and classification of remote sensing data for agriculture

Automatic semantic segmentation has expected increasing interest for researchers in recent years on multispectral remote sensing (RS) system. The agriculture supports 58 % of the population, in which 51 % of geographical area is under cultivation. Furthermore, the RS in agriculture can be used for identification, area estimation and monitoring, crop detection, soil mapping, crop yield modelling and production modelling etc. The RS images are high resolution images which can be used for agricultural and land cover classifications. Due to its high dimensional feature space, the conventional feature extraction techniques represent a progress of issues when handling huge size information e.g., computational cost, processing capacity and storage load. In order to overcome the existing drawback, we propose an automatic semantic segmentation without losing the significant data. In this paper, we use SOMs for segmentation purpose. Moreover, we proposed the particle swarm optimization technique (PSO) algorithm for finding cluster boundaries directly from the SOMs. On the other hand, we propose the deep residual network to achieve faster training process. Deep Residual Networks have been proved to be a very successful model on RS image classification. The main aim of this work is to achieve the overall accuracy greater than 85 % (OA > 85 %). So, we use a convolutional neural network (CNN), which outperforms better classification of certain crop types and yielding the target accuracies more than 85 % for all major crops. Furthermore, the proposed methods achieve good segmentation and classification accuracy than existing methods. The simulation results are further presented to show the performance of the proposed method applied to synthetic and real-world datasets

DEVELOPMENT AND VALIDATION OF UV SPECTROPHOTOMETRIC METHODS FOR DETERMINATION OF MEGLUMINE IN BULK

UV, first, second and third derivative spectrophotometric methods have been developed for the determination of meglumine. The solutions of standard and sample were prepared in distilled water. For the first method i.e. calibration curve UV spectrophotometric method, the quantitative determination of the drug was carried at 254 nm and the linearity range was found to be 10 – 60 µg/ml. For the first, second, third derivative spectrophotometric methods the drug was determined at 247 nm, 216 nm, 266 nm with the linearity range 10 – 60 µg /ml. The calibration graphs constructed at their wavelength of determination were found to be linear for UV and derivative spectrophotometric methods. All the proposed methods have been extensively validated. There was no significant difference between the performance of the proposed methods regarding the mean values and standard deviations

TIRSPEC : TIFR Near Infrared Spectrometer and Imager

We describe the TIFR Near Infrared Spectrometer and Imager (TIRSPEC) designed and built in collaboration with M/s. Mauna Kea Infrared LLC, Hawaii, USA, now in operation on the side port of the 2-m Himalayan Chandra Telescope (HCT), Hanle (Ladakh), India at an altitude of 4500 meters above mean sea level. The TIRSPEC provides for various modes of operation which include photometry with broad and narrow band filters, spectrometry in single order mode with long slits of 300" length and different widths, with order sorter filters in the Y, J, H and K bands and a grism as the dispersing element as well as a cross dispersed mode to give a coverage of 1.0 to 2.5 microns at a resolving power R of ~1200. The TIRSPEC uses a Teledyne 1024 x 1024 pixel Hawaii-1 PACE array detector with a cutoff wavelength of 2.5 microns and on HCT, provides a field of view of 307" x 307" with a plate scale of 0.3"/pixel. The TIRSPEC was successfully commissioned in June 2013 and the subsequent characterization and astronomical observations are presented here. The TIRSPEC has been made available to the worldwide astronomical community for science observations from May 2014.Comment: 20 pages, 21 figures, 2 tables. Accepted for publication in Journal of Astronomical Instrumentatio