Urvish Desai's repositories
Movies-ETL
Amazing Prime loves the dataset and wants to keep it updated on a daily basis. Britta needs assistance to create an automated pipeline that takes in new data, performs the appropriate transformations, and loads the data into existing tables. this project will refactor the code from this module to create one function that takes in the three files—Wikipedia data, Kaggle metadata, and the MovieLens rating data—and performs the ETL process by adding the data to a PostgreSQL database.
Amazon_Vine_Analysis
We’ve been tasked to analyzing Amazon reviews written by members of the paid Amazon Vine program. The Amazon Vine program is a service that allows manufacturers and publishers to receive reviews for their products. Companies like Sell by pay a small fee to Amazon and provide products to Amazon Vine members, who are then required to publish a review. In this project, we’ll have access to approximately 50 datasets. Each one contains reviews of a specific product, from clothing apparel to wireless products. We’ll need to pick one of these datasets and use PySpark to perform the ETL process to extract the dataset, transform the data, connect to an AWS RDS instance, and load the transformed data into pgAdmin. Next, we’ll use PySpark, Pandas, or SQL to determine if there is any bias toward favorable reviews from Vine members in your dataset. Then, we’ll write a summary of the analysis for Jennifer to submit to the SellBy stakeholders.
AWS-Overview-and-MFA-Setup
Amazon Web Services (AWS) is a comprehensive, pay-as-you-go cloud computing platform offering a multitude of services globally. Multi-Factor Authentication (MFA) is an essential security measure that uses multiple authentication methods, including any authenticator app, to verify user identity.
bikesharing
Implementing Tableau This is project will be performed as one of the stakeholder like to see the Analysis of the bike sharing. For this analysis, we’ll use Pandas to change the "tripduration" column from an integer to a datetime datatype.
Credit_Risk_Analysis
We are building up the skills in the data preparation, statistical reasoning, and machine learning. applying the Machine Learning to solve the real world challenge: credit card risk. Credit risk is an inherently unbalanced classification problem, as good loans easily outnumber risky loans. Therefore, we’ll need to employ different techniques to train and evaluate models with unbalanced classes. Using imbalanced-learn and scikit-learn libraries to build and evaluate models using resampling.
Cryptocurrencies
We and Martha have done our research. We understand what unsupervised learning is used for, how to process data, how to cluster, how to reduce our dimensions, and how to reduce the principal components using PCA. It’s time to put all these skills to use by creating an analysis for your clients who are preparing to get into the cryptocurrency market. Martha is a senior manager for the Advisory Services Team at Accountability Accounting, one of your most important clients. Accountability Accounting, a prominent investment bank, is interested in offering a new cryptocurrency investment portfolio for its customers. The company, however, is lost in the vast universe of cryptocurrencies. So, they’ve asked us to create a report that includes what cryptocurrencies are on the trading market and how they could be grouped to create a classification system for this new investment. The data Martha will be working with is not ideal, so it will need to be processed to fit the machine learning models. Since there is no known output for what Martha is looking for, she has decided to use unsupervised learning. To group the cryptocurrencies, Martha decided on a clustering algorithm. She’ll use data visualizations to share her findings with the board.
Election_Analysis
This is project's goal is to help Seth and Tom to submit the election audit results to the election commission. however, election commission has reqested some additional data to complete the audit. They need voter turnout for each county, percentage of votes from each county out of the total count. The county with the highest turnout.
Mapping_Earthquakes
Basil and Sadhana like how you created your earthquake map with two different maps and the earthquake overlay. Now, Basil and Sadhana would like to see the earthquake data in relation to the tectonic plates’ location on the earth, and they would like to see all the earthquakes with a magnitude greater than 4.5 on the map, and they would like to see the data on a third map. The brief overview of the project is : - Create an earthquake data in respect to the tectonic plates location on the earth - Display the earthquake data with magnitude greater than 4.5 on the map. - The additional requested map.
MechaCar_Statistical_Analysis
A few weeks after starting a new role, Jeremy is approached by upper management about a special project. AutosRUs’ newest prototype, the MechaCar, is suffering from production troubles that are blocking the manufacturing team’s progress. AutosRUs’ upper management has called on Jeremy and the data analytics team to review the production data for insights that may help the manufacturing team In this challenge, we’ll help Jeremy and the data analytics team do the following: Perform multiple linear regression analysis to identify which variables in the dataset predict the mpg of MechaCar prototypes Collect summary statistics on the pounds per square inch (PSI) of the suspension coils from the manufacturing lots Run t-tests to determine if the manufacturing lots are statistically different from the mean population Design a statistical study to compare vehicle performance of the MechaCar vehicles against vehicles from other manufacturers. For each statistical analysis, you’ll write a summary interpretation of the findings.
Neural_Network_Charity_Analysis
Its about to assist Alphabet Soup predict where its about making an investments, then analyzing data from more than 34,000 organizations and have funding received from Alphabet Soup over the years.
Greater-Toronto-Property-Costs
This project is about the analysis of the Toronto property prices dataset. We have to do analysis on the data that is on the file. We will perform: The Statistical analysis, GTA pricing overview, Checking that if the data is evenly distributed, Taking the sample data from dataset to check whether it is linear regression or not. Visualization :Property prices in GTA in respect to the no of listing ,No of listing in respect to the different regions in GTA, What's the no of bedrooms looks like in respect to no of listing What's the no of bathrooms looks like in respect to no of listing
Kickstarter_Analysis
Louise’s play Fever is near to its fundraising goal in a less amount of time. She like to have an analysis of the different campaigns fared in respect to their launch dates and their funding goals. This project is about using the data that been gathered and analysing-visualizing the campaign outcome. The key factors that we used to analyze the events are based on their launch dates and funding goals.
Mission-to-Mars
The Robin's web app that is created during this module it looks good and functioning well. however, she likes to add more polish to it. She was admiring images of the Mars's hemispheres online and realized that the site is scraping-friendly. She is interested in modifying the current web app to include all the four of the hemisphere images. To appoach this we will use Beautifulsoup and Splinter to scrape full-resolution images of the Mars's hemisphere and the title of those images, store the scraped data on the Mongo database, use a web application to display the data, the alter the design of the web app to accommodate these images.
Pewlett-Hackard-Analysis
This project is taken under the manager request to determine the number of retiring employees per title and identify employees who are eligible to participate in the mentorship program. Using this data we wil help Bobby's manager for the " silver tsunami" as many of the employees reach retirement age.
Plotly
Roza has a partially completed dashboard that she needs to finish. She has a completed panel for demographic information and now needs to visualize the bacterial data for each volunteer. Specifically, her volunteers should be able to identify the top 10 bacterial species in their belly buttons. That way, if Improbable Beef identifies a species as a candidate to manufacture synthetic beef, Roza's volunteers will be able to identify whether that species is found in their navel.
Project_Credit_Analysis
Credit Score is a huge factor when it comes to Financial ability check, car , Mortgage, line of credit approvals, also checked by banks, different financial institutions etc. Our goal for this Project is to clean the unwanted data that we got and then used to do analysis and visualization on it. We will show the results in the Poor, Standard and Good credit score based on the data.
PyBer_Analysis
This assignment is given by the V. Isualize for using our Python skills and knowledge of Pandas. In this Analysis we are generating a summary Dataframe of the ride-sharing data by the city type. The results is done in the visualization of the multiple-line graph that shows the total weekly fares for each city type. After calculating the data we have to use it for decision making at PyBer.
Russia_Ukraine_War_Twitter_Data
In this Project we will Analysis the data from twitter that how people uses their opinion on the Russia and Ukraine war
School_District_Analysis
The school board has notified Maria and her supervisor that the student_complete file stats the proof of academic dishonesty; in particular the reading and math grades for Thomas High School, seems like the ninth graders data has been altered. The school board is not aware about the whole records and unknown to full extent of the academic dishonesty they want to go with the state-testing standards and want to help Maria in regards to this. She requested to replace the math and reading scores for the Thomas High school with NaN while keep the rest data intact. once that data been removed then Maria like to do analysis on how it will affect the overall analysis.
stock-analysis
Steve interested in the workbook that been prepared. At the clicking of a button, he can examine a whole dataset as per year. Now, to dig little more into this, for his dad and mom, he wants to amplify the dataset to consist of the whole stock marketplace over the previous few years. However it seems like the execution of the code is slow. The agenda of this project is to edit the code using all the data one time in order to fetch the same information and determine wheather after editing the code it made the script faster.
surfs_up
The purpose of the project is to review patterns in Hawaii for opening a surf and ice-cream shop.The Analysis we does in this module was appreciated by W. Avy however it seems he wants more information about the temperature trends before opening the surf shop. Specifically, he wants temperature data for the months of June and December in Oahu, in order to determine if the surf and ice cream shop business is sustainable year-round.
UFOs
Dana’s webpage and dynamic table are working as intended, but she’d like to provide a more in-depth analysis of UFO sightings by allowing users to filter for multiple criteria at the same time. The purpose of this project is to create an interactive and well responsive website for UFO data. The table on the website shows the UFO sightings displayed in the table. The filter of the table is done by Date, City, State, Country and Shape.
Unemployment_Analysis
Using the the data set that we currently have we will do the analysis of the unemployment data using the Power BI , will make some dashboards and check on the data patterns.
Windows_Application
Number guessing game using c sharp and Dot Net framework
World-Economics-Data
This project is about doing analysis on the World Economics Data. The factors such as Corruption , Tourism,Unemployment and Cost of Living how it affects Economics and also doing comparision in between countries.
World_Weather_Analysis
Jack loves the PlanMyTrip app. Beta testers love it too. And, as with any new product, they’ve recommended a few changes to take the app to the next level. Specifically, they recommend adding the weather description to the weather data we’ve already retrieved in this module. Then, We'll have the beta testers use input statements to filter the data for their weather preferences, which will be used to identify potential travel destinations and nearby hotels. From the list of potential travel destinations, the beta tester will choose four cities to create a travel itinerary. Finally, using the Google Maps Directions API, we will create a travel route between the four cities as well as a marker layer map.