An IT professional with 6+ years of experience having technical expertise in PYTHON, SQL,TABLEAU,MATLAB,C++,PANDAS,NUMPY

Harpreet Kaur  • Peerlist

Color constancy is an element of humanvision framework which guarantees that the apparentcolor of items under fluctuating light conditions generallyremains constant. It is fundamentally used to eliminatethe color cast in the picture. Color Cat is a quick andprecise learning-based methodology for accomplishingcomputational color constancy. However, despiteeverything it confronts a few limitations like poorbrightness due to normalization used. Furthermore itdoesn't promise edge preservation. So to overcome theseissues a CEA strategy has been proposed which is ahybrid model based on Color Cat, Edge preservationfilter and Adaptive histogram Equalization. As Adaptivehistogram Equalization is exceptionally valuable forcontrast improvement and edges are protected by edgepreservation filter. Experimental results show that theproposed CEA approach outperforms over existingtechnique

An integrated CEA approach for color light source estimation

Restoring the actual colors in images also called color constancy technique is still an open area of research in digital image processing. Many techniques has been proposed so far in order to improve the accuracy of the color constancy techniques further. This paper has presented a comparison between some well known latest color constancy techniques. The review has clearly shown that each technique has its own benefits and limitations as well. However none is promising in every case of vision processing. The comparisons have clearly shown that the ColorCat based constancy has better results over each technique.

A Comparative Review of Various Illumination Estimation Base

The Northwind Traders Data Analysis project embarks on a transformative journey through data to unearth valuable insights, optimize operations, and drive strategic decision-making for Northwind Traders, a dynamic and forward-thinking business. By harnessing the power of analytics, this project aims to illuminate trends, streamline processes, and enhance customer experiences, ultimately propelling Northwind Traders to new heights of success.

Northwind Traders Data Analysis

The Amazing Mart Data Analysis project embarks on a journey through the vast realm of retail data to extract actionable insights, identify growth opportunities, and enhance operational efficiency for Amazing Mart, a prominent retail chain. By harnessing the power of data analytics, this project aims to revolutionize decision-making processes, optimize inventory management, and elevate customer experiences.The Amazing Mart Data Analysis project strives to empower the retail chain with the knowledge to make informed decisions that drive growth, efficiency, and customer satisfaction. By leveraging data-driven insights, the project aims to position Amazing Mart at the forefront of the retail industry, setting new standards for customer-centric strategies and operational excellence.Analysis is performed on amazing mart data and Sales, Profit and discount has been shown with respect to Quarter of dates and dashboard is created using tableau.

Data Analysis on Amazing Mart (Tableau)

In the wake of the unprecedented global pandemic, the COVID-19 Data Analysis project aims to delve into the vast sea of data surrounding the pandemic's impact. This project seeks to extract valuable insights, trends, and patterns from diverse COVID-19 datasets, shedding light on the multifaceted dimensions of the virus's spread, healthcare response, and socio-economic consequences.Perform exploratory data analysis (EDA) to uncover initial trends, geographical hotspots, and temporal variations in COVID-19 cases and related metrics.

Covid-19 Data Analysis using python-pandas

Problem and Background:Employee well-being and work-life balance greatly depend on the presence of effective parental leave policies. Nevertheless, there exist disparities in leave allocation across genders and industries. Analyzing parental leave data sheds light on significant discrepancies in the distribution of paid and unpaid leaves, as well as variations between maternity and paternity leave.The data shows that 58% of companies offer paid maternity leave, while unpaid maternity leave is available in 33% of companies. In contrast, paid paternity leave is provided by only 7% of companies, and a mere 2% offer unpaid paternity leave. These figures highlight an imbalance in parental support, with a disproportionate emphasis on supporting mothers rather than fathers.Moreover, the data suggests that maternity leaves tend to be longer compared to paternity leaves. Approximately 94% of companies offer longer duration for maternity leave, whereas only 6% provide comparable lengths for paternity leave. This discrepancy raises concerns about gender equality and the equitable sharing of caregiving responsibilities.Additionally, the analysis indicates that the technology industry stands out by offering the highest number of parental leaves when compared to other industries.&nbsp;Solution: It is necessary to implement comprehensive policies that support both mothers and fathers in order to address existing disparities and foster a more equitable approach to parental leave. By ensuring comparable durations and offering paid parental leave for both genders, companies can contribute to a more balanced and inclusive work environment.Methodology and Project Scope:1.&nbsp;&nbsp;&nbsp;&nbsp; For the analysis of parental leave policies, data was gathered from various companies across industries. The leaves were categorized based on whether they were paid or unpaid, and whether they were classified as maternity or paternity leave. The top 15 companies offering parental leaves were identified based on the provided duration or number of weeks.2.&nbsp;&nbsp;&nbsp;&nbsp; The project aims to examine the distribution of leaves, identify any discrepancies, and analyze industry-specific trends. It focuses on collecting data from a diverse range of companies to ensure a comprehensive understanding of parental leave policies.3.&nbsp;&nbsp;&nbsp;&nbsp; The primary objective is to promote gender equality and work-life balance through more inclusive parental leave policies.Key Performance Indicators (KPIs) for this project may include:a. Increase the percentage of companies offering paid paternity leave.b. Reduce the disparity between maternity and paternity leave duration.c. Increase the overall percentage of companies offering parental leave.d. Reduce the percentage of companies providing only unpaid leave.The analysis of parental leave policies reveals significant disparities in leave distribution between genders and industries. Maternity leave is more prevalent and longer than paternity leave, with the technology industry leading in providing parental leave benefits.To address these issues, it is crucial to implement comprehensive policies that promote gender equality and work-life balance. Companies should strive to offer paid parental leave to both mothers and fathers, ensuring comparable duration. By adopting more inclusive approaches, organizations can contribute to creating a supportive and equitable work environment for all employees.

Data Analysis on Parental Leave

Data Analysis is performed on the dataset (https://www.kaggle.com/datasets/shilongzhuang/telecom-customer-churn-by-maven-analytics) by using Advanced Excel concepts like Pivot Table, Data visualization( Using charts and graphs to visually represent the data and identify trends, patterns and insights. Microsoft Word for the preparation of report.The Customer Churn table contains information on all 7,043 customers from a Telecommunications company in California in Q2 2022Each record represents one customer, and contains details about their demographics, location, tenure, subscription services, status for the quarter (joined, stayed, or churned).Goals: The key objectives of this project are as follows:1.&nbsp;&nbsp; Analyze the churn rate during the final quarter of 2022.2.&nbsp;&nbsp; Identify the primary drivers behind customer churn.3.&nbsp;&nbsp; Present recommendations to diminish future churn rates.Conclusion:In conclusion, the analysis of the telecom number churn report for the final quarter of 2022 shed light on significant findings. The primary causes of churn were determined to be related to customer service quality and price sensitivity.To effectively mitigate churn rates in the future, the report proposes several recommendations. Firstly, enhancing customer service can be achieved through the recruitment of additional agents, reducing waiting times, and implementing comprehensive training programs. These measures aim to enhance the overall customer experience and satisfaction.Furthermore, the report suggests implementing customer loyalty programs, such as offering promotions or discounts to long-standing customers. By rewarding customer loyalty, the company can foster stronger connections with its clientele and encourage long-term commitment.By implementing these recommended strategies, the company stands to reduce churn rates and enhance customer retention, ultimately leading to improved business performance and customer satisfaction.&nbsp;