Skilled Mechanical Engineer, Data Enthusiast, and always open to learning new skills.

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Darshan Murkute • Peerlist

<h1 class="rich-heading">Parental_Leave_Policies</h1>"Parental Leave Policies: A New Frontier in Employee Benefits"<h2 class="rich-heading">Overview of the Data.</h2>Crowdsourced parental leave data from 1,601 companies across different industries, including paid/unpaid maternity and paternity leave weeks.<ul><li>Company - Company name</li><li>Industry - Company Industry &amp; sub-industry (Industry: Sub-industry)</li><li>Paid Maternity Leave- Paid weeks off from work for mothers after the birth of their child</li><li>Unpaid Maternity Leave- Unpaid weeks off from work for mothers after the birth of their child</li><li>Paid Paternity Leave- Paid weeks off from work for fathers after the birth of their child</li><li>Unpaid Paternity Leave- Unpaid weeks off from work for fathers after the birth of their child</li></ul>NOTE: This is user-reported data. Where users report conflicting information, consensus numbers (if any) or the median are shown. "N/A" means no information has been reported.<h2 class="rich-heading">Task</h2><ol><li>Which companies offer the most paid parental leave weeks?</li><li>Is maternity leave typically longer than paternity leave?</li><li>What is the distribution of parental leave weeks offered?</li><li>Are there noticeable differences between industries?</li></ol><h2 class="rich-heading">Data Understanding</h2>A dataset of 1600 records with 6 fields is a valuable resource for analysis. However, before any analysis can be performed, the data must be cleaned and preprocessed to ensure that it is accurate and consistent.The data cleaning process involves identifying and correcting errors in the data, such as duplicate records, missing values, and incorrect data types.<h2 class="rich-heading">Data Preprocesssing</h2>Replacing null values with 0 as we do not have data about the companies and filling null values with mean/median/mode will lead us to some wrong interpretation of data and results. Also, doing some aggregate between the columns like sum and average to get the desired data.<h2 class="rich-heading">Data Visualization</h2>Data Visualization is created by using matplot and seaborn liebrary. Some distributions are also visualized to get important insights of the data by using some statistical concepts.<h2 class="rich-heading">Conclusion</h2><ol><li>companies offer the most paid parental leave weeks are as follows -</li></ol><ul><li>Grant Thornton</li><li>LAC-Group</li><li>Flatiron Health</li><li>Cloudworkers</li><li>LeverX</li><li><a target="_blank" rel="noopener noreferrer nofollow" class="text-green-text rich-link break-all link-break" href="http://Rangle.io">Rangle.io</a></li><li>Washburn Center For Children</li><li>Salesforce</li><li>JHC Systems</li><li>Dynatrace</li></ul><ol start="2"><li>As we can see in the above scatter plot, most of the company is providing Maternity Leave and does not offers Paternity leave or while some of the companies provide less paternity leave than maternity <a target="_blank" rel="noopener noreferrer nofollow" class="text-green-text rich-link break-all link-break" href="http://leave.So">leave.So</a>, We can say Maternity Leave is longer than Paternity Leave.</li><li>Distribution of both Maternity and Paternity Leave is almost seems like normal distribution with very less skewed to the right.std for maternity leave is greater than paternity leave. and spread of the data in maternity leave is more and wide than that of paternity leave as more than 50% data in paternity leave is very close to mean. And in maternity data the data is spreaded over some std value.</li><li>In every Industry Paid Maternity leave is longer than Paid Paternity leave.</li><li>Also Unpaid Maternity Leave is longer than Unpaid Paternity Leave.</li><li>Only Pharmacies and Drug Stores offers equal Unpaid Maternity as well as Paternity Leave.</li><li>Printing Industry offers same weeks of Paid Maternity and Paternity Leave. Also it offers highest no. of unpaid week leave</li></ol>

Parental Leave Policies

<h3 class="rich-heading">Problem &amp; background</h3>Airbnb data for 250,000+ listings in 10 major cities, including information about hosts, pricing, location, and room type, along with over 5 million historical reviews. In this we have to determine difference in Airbnbmarket in different cities, trends and cities which offers best value for travel.<h3 class="rich-heading">Solution</h3>The data is quite large but according to the problems we are going to analyze on we do not need much data, so we will simply keep the data we require to perform our analysis. Even though with quite big data, I am using Excel to generate insights though it might take some time to perform some operation. As cleaning and understanding data is quite fast on Excel by means of sorting, filtering tools. And to generate insight we have Pivot table and then will generate visualization for more clear and meaningful understandings.<h1 class="rich-heading">Conclusion</h1>The analysis will help to understand and review of a home staying company Airbnb for good stay in city as per customers choice ad their requirements. The reviews will help to know which city, staying place and its price is best option to travel. Will also be advantageous for people to know more about places and plan their trips accordingly.

Airbnb Listing and Reviews

Executive Summary:&nbsp;This is the analysis report of the web site rotten tomatoes, it is an American review-aggregation website for film and television.&nbsp;The data set contains information about the movies from year 1914-2019. It contains data about 1500+ movies, critics consensus, its ratings, genres, theater release date, on streaming date, Runtime in minutes, tomatometer rating and audience ratings. The analysis will provide insights and at last the sentiment analysis of the critic’s consensus.&nbsp;Key Findings and Insights:&nbsp;·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Comedy genre has the highest count of movies released from 1914 whereas R rated movies have highest count released from 1914.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; It is found that highest no. of movies was released in the year 2014 and in this year 85 movies were released in the month of October.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; In this data, 43% movies have Rotten Status, 18% are certified fresh, and 39% have Fresh status.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Paramount Pictures Studio produces 180 movies which has highest rating varies between 80-100 and Sony Pictures Home Entertainment Studio produces 28 movies which has Lowest Rating in between 0-9.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; There are discrepancies between Tomatometer ratings and audience ratings.&nbsp;.Recommendations:&nbsp;According to my findings, some recommendations are made:&nbsp;·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Sony pictures home entertainment studio can produce good rated movies so their ratings will increase.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Filmmaker and studios should consider public opinions about the movies to reduce the discrepancies.&nbsp;Conclusion:&nbsp;Film makers and producers will get brief idea about the insights generated by the analysis of Movies data. And they can use this information to gain advantage over the film or movie industry.

Rotten Tomato Movie Ratings

<h1 class="rich-heading">Problem &amp; background</h1><h1 class="rich-heading"></h1>A Telecom Customer Company providing a data related to their customers (Data Source- Maven Analytics). The data includes a unique Id for every each customer called as Customer ID, demographic information of the customers, and various services used by the customer, customer’s monthly charges and average revenue generated by the customer, also provide data about customer’s status i.e. Churned, Stayed, Joined. The Data provided is recorder within the last quarter Q2-2022.Here we are suppose to analyze the data, clean the data and then use it to generate insights. Here we suppose to find out is the company losing high value customer and will also provide some suggestion to reduce the churning.&nbsp; Will also sneak deep to understand which are the top 12 cities where the customer churning is high and what will be the major reason for&nbsp;<h1 class="rich-heading">Solution</h1>I will prefer to use Microsoft excel to clean the data, transforming into desired data and visualize it. The data we are using is comparatively small and can be handle by Microsoft Excel hence it is most easy choice to select within different software.&nbsp; We will use slicer, Pivot table&nbsp; and various charts to generates insights and a good looking dashboard.&nbsp;&nbsp;&nbsp;<h1 class="rich-heading">Methodology &amp; Project scope</h1>Our final motive of this analysis is to calculate and answer some of the analysis questions and make this calculation logical and understandable.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Firstly, the most important thing is to prepare a data dictionary, without understanding the data dictionary, it will be difficult to know about data, understanding the column names and what actual story the data tells you, this can only happen after knowing the data dictionary·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;Data dictionary contains 3 columns and a lot of important information about the data.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; For initializing the calculation or analysis part, I am going to generate pivot tables and will perform required mathematical operations to understand the data and generate insights.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Visualization is generated right after the pivot table and also answered some of the important questions of my analysis.&nbsp;<h1 class="rich-heading">Conclusion</h1>&nbsp; This analysis will help the Telecom company to know more about their customer , why customer churn is happening and also will provide some crucial suggestion to minimize the Customer churn.&nbsp;<h1 class="rich-heading"></h1>

Telecom Customer Churn

<h2 class="rich-heading">About Dataset</h2>Context All the data of this dataset is scrapped from Wikipedia. You can find all the Bollywood movies released in the time span of 5 years from 2018 to 2022. It gives you information related to the movie's release month, its director, casters as well as the production House. It is a short simple and ready-to-use dataset as it contains zero null values and also 0 duplicate values. This dataset contains csv as well as Excel file to improve workability throughout the software. Both files contain the same data of Bollywood movies.Content List of Bollywood movies released between 2018-2022.Data Columns<ul><li><a target="_blank" rel="noopener noreferrer nofollow" class="text-green-text rich-link break-all link-break" href="http://Sr.No">Sr.No</a></li><li>Released Month</li><li>Title</li><li>Director</li><li>Cast</li><li>Studio(Production House) -Releasing Year</li></ul>Source The data is scrapped from <a target="_blank" rel="noopener noreferrer nofollow" class="text-green-text rich-link break-all link-break" href="https://en.wikipedia.org/wiki/Lists_of_Hindi_films">https://en.wikipedia.org/wiki/Lists_of_Hindi_films</a>.Inspiration The fan base of Bollywood movies in India is huge, so I decided to create this dataset.

Creating Dataset of Bollywood Movies

<h1 class="rich-heading">Problem &amp; background</h1><h1 class="rich-heading"></h1>A marketing campaign is held to reach out to people who can be their customers and also their customers whom were inactive. This Marketing campaign data of 2,240 customers of Maven Marketing, including customer profiles, product preferences, campaign successes/failures, and channel performance. Our task is to determine how average customer looks like so we could target these categorical people in the society, another task is various factors that may influence sales on the web channel, and the last but not least to tell whether which campaign is most successful and which channel is underperforming.<h1 class="rich-heading">Solution</h1>A data of the customers and the result of various marketing campaign, is the data we are going to perform our analysis by using Microsoft Excel, as the data is not to large Excel is the perfect tool to work with, its feature called “Pivot Table” will help to generate insights from the data and answer our analysis related question.<h1 class="rich-heading">Methodology &amp; Project scope</h1>Our final motive of this analysis is to calculate and answer some of the analysis questions and make this calculation logical and understandable.<ul><li>Firstly, the most important thing is to prepare a data dictionary, without understanding the data dictionary, it will be difficult to know about data, understanding the column names and what actual story the data tells you, this can only happen after knowing the data dictionary</li><li>&nbsp; &nbsp;Data dictionary contains 2 columns and a lot of important information about the data.</li><li>&nbsp; For initializing the calculation or analysis part, I am going to generate pivot tables and will perform required mathematical operations to understand the data and generate insights.</li><li> Visualization is generated right after the pivot table and also answered some of the important questions of my analysis.</li></ul><h1 class="rich-heading">Conclusion</h1>&nbsp; This analysis will help the marketing company to know more about which type of campaign company must conduct, also by performing this analysis anyone can understand the best product company offers. Company will know which type of people in the society is the one to target, so company could generate more customer and eventually more profit.&nbsp;

Marketing Analysis

<h1 class="rich-heading">Problem &amp; background</h1><h1 class="rich-heading"></h1>A year's worth of sales from a fictitious pizza place, including the date and time of each order and the pizzas, served, with additional details on the type, size, quantity, price, and ingredients. Here I need to find out no. of customer a pizza shop can expect per day, how many pizzas a customer would order, and is there any peak hours. Furthermore I, need to find out the bestseller pizza of the shop, total revenue generated and finally determining pizza’s which are not popular among the people. Also I am going to determine the seasonality in customer visiting and ordering the shop.&nbsp;<h1 class="rich-heading">Solution</h1>The data is not so large so analysis can be performed on Microsoft Excel is easy and less time consuming. We can generate pivot table by using the data and furthermore creating visualization to get clear understanding of the insights, so we get answers of the analysis problems and can take decision based on the analysis. Here will determine no. of customer a pizza shop can expect per day and how many pizzas a customer would order so accordingly pizza shop will be prepared to serve the customer. Also we can make decision on which pizzas should be dropout of the menu and which these needs to be promoted among the people.<h1 class="rich-heading">Methodology &amp; Project scope</h1>The Final data should be analyzed and easy to understand is the goal behind performing this project<ul><li>&nbsp; Firstly, the most important thing is to prepare a data dictionary, without understanding the data dictionary, it will be difficult to know about data, understanding the column names and what actual story the data tells you, this can only happen after knowing the data dictionary</li><li>&nbsp;&nbsp;&nbsp; From each data collected we can make required pivot table from it and use excel functions to perform calculation and then create an understanding pivot charts for the recommended analysis part of this project to provide solution.</li><li>&nbsp;&nbsp;&nbsp;&nbsp; And them the dashboard is prepared in which calculated and analyzed tables are visualized for better and clear understanding</li></ul>&nbsp;<h1 class="rich-heading">Conclusion</h1>This analysis can help people to understand which pizza s popular among people, it will make it easier for the foodie people who are always trying new stuff to choose what’s best a pizza shop can offer. Also the analysis will help the pizza shop to know more about their sales of pizza’s, and help&nbsp; them to decide which pizza’s must be drop out of the menus or which of these should promote, this will help the pizza shop to generate more profit in the upcoming year.