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Featured Projects

No. of Projects: 6

Predicting House Prices

In this mini-project, we will predict a House’s Sale Price. We will be using Ames Housing Dataset, it has information about each house such as Lot Area, Garage Area, Year Built, and more.

Skills: Linear Regression, Feature Engineering, Hyperparameter Tuning, RMSE, Cross Validation

Predicting Car Prices

We will predict a Car's market price using its attributes like motor's displacement, the weight of the car, the miles per gallon, how fast the car accelerates, and more.

Skills: KNN, Feature Engineering, Hyperparameter Tuning, RMSE, Cross Validation.

Answering Business Questions using SQL

In this mini-project, we’ll be working with a modified version of a database called Chinook. The Chinook database contains information about a fictional digital music shop - kind of like a mini-iTunes store. It contains information about the artists, songs, and albums from the music shop, as well as information on the shop’s employees, customers, and the customers purchases.

We’ll provide solutions to few Business Questions by analysing data from Chinook Database.

Skills: Pandas, Matplotlib, SQLite3, SQL (SELECT, FROM, WITH _ AS, CASE, WHEN, THEN, EXCEPT, THEN, JOIN, COUNT, CAST, GROUP BY, LIMIT)

Designing and Creating a Database

In this mini-project, we will be working with a file of Major League Baseball games from Retrosheet.

We will design and create a Database with normalised tables by compiling data from various sources.

Skills: Pandas, SQLite3, SQL (SELECT, FROM, CREATE, JOIN, ALTER, UPDATE, INSERT, UNION, DROP, CASE, WHEN, THEN, LIMIT, GROUP BY)

Storing Storm Data

In this mini-project, we will be working with a CSV File.
We will convert it into a SQL Database with following features:

Have data stored in normalised tables.
Have data stored in a Storage Efficient Way.
Have Users that can update, read, and insert data into a table of the database.
Have Users that can only read from a table.

Skills: Pandas, PostgreSQL (connect, cursor, execute, autocommit, mogrify), SQL (CREATE TABLE, USER _ WITH PASSWORD, GROUP, GRANT, REVOKE, DROP _ IF EXISTS, INSERT INTO)

Investigating Fandango Movie Ratings

In October 2015, Walt Hickey from FiveThirtyEight published a popular article where he presented strong evidence which suggest that Fandango’s movie rating system was biased and dishonest.

In this mini-project, we’ll analyze more recent movie ratings data to determine whether there has been any change in Fandango’s rating system after Hickey’s analysis.

Skills: Pandas, Matplotlib

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