Rahul Lal's repositories
Citibike_Analysis
The main purpose of this report is to study the New York CitiBike data for the month of August which is the busiest month and highlight the main talking points such us type of users, CitiBike user by Gender, and prepare a comparative study for a similar project/opportunity in Des Moines, Iowa.
Credit_Risk_Analysis
Credit risk is an inherently unbalanced classification problem, as good loans easily outnumber risky loans. Therefore, you’ll need to employ different techniques to train and evaluate models with unbalanced classes. Using the credit card credit dataset from LendingClub, a peer-to-peer lending services company,
Cryptocurrencies
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. We create a report that includes what cryptocurrencies are on the trading market and how they could be grouped
Movies-ETL
Amazing Prime loves the dataset and wants to keep it updated on a daily basis. We create one function that takes in the three files Wikipedia data, Kaggle metadata, the MovieLens rating data and creates an automated pipeline that takes in new data, performs the appropriate transformations, and loads the data into existing tables.
Pewlitt-Hackard-Analysis
Determine the number of retiring employees per title, and identify employees who are eligible to participate in a mentorship program. Write a report that summarizes your analysis and helps prepare Bobby’s manager for the “silver tsunami” as many current employees reach retirement age.
Unpaved_Road_Condition_Monitoring
The aim of this paper is to review existing methods already available in the literature, and present a research into the use of smartphones for classification of unpaved roads utilizing the machine learning techniques including K-Nearest Neighbor (KNN) and Support Vector Machines (SVM).
Amazon_Vine_Analysis
Work on the Watches dataset 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, you’ll use PySpark, Pandas, or SQL to determine if there is any bias toward favorable reviews from Vine members in your dataset.
GunViolence_US
The aim of this study is to visualize the gun violence data collected for the year 2018 and highlight what I think are the most important Factors contributing to gun violence.
Neural_Network_Charity_Analysis
With your knowledge of machine learning and neural networks, you’ll use the features in the provided dataset to help create a binary classifier that is capable of predicting whether applicants will be successful if funded by Alphabet Soup.
Toronto_Parking
The goal of this study is to find the most common type and location of parking infraction. We can then determine any alternate parking available at that location. We can also analyse the socio-demagraphic trends of the neighborhoods with most infractions.
belly_button_biodiversity
We need 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.
Election_Analysis
Write a code in VS Code to perform analysis on the election data provided and obtain the results
KickStarter_Analysis
Analyze data for Kickstarter campaigns across the globe and present Louise with information on trends
Mapping_Earthquakes
Plot the earthquake data in relation to the tectonic plates’ location on the earth, and show 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.
MechaCar_Statistical-Analysis
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.
Mission_to_Mars
Use BeautifulSoup and Splinter to scrape full-resolution images of Mars’s hemispheres and the titles of those images, store the scraped data on a Mongo database, use a web application to display the data, and alter the design of the web app to accommodate these images.
PyBer_Analysis
Analyze all the rideshare data from January to early May of 2019 and create a compelling visualization
School_District_Analysis
Use Jupyter Notebook and Pandas library to perform the analysis for standardized test for a school district
Stock_Analysis
Use VBA to write a code Which will help in performing automated analysis on the stock options
surfs_up
Create temperature reports to help determine if the surf shop can operate through out the year, leading to a sustainable business.
UFOs
The purpose of this project is to create a webpage that allows us to filter the table containing UFO sightings. Multiple filters on the webiste are Date, City, State, Country and shape which can be used at the same time
World_Weather_Analysis
Use beta testers input statements to filter the data for their weather preferences, which will be used to identify potential travel destinations and nearby hotels. The beta tester will then choose four cities to create a travel itinerary. Finally, using the Google Maps Directions API, we will create a travel route between these cities .