WorldQuant-University-Projects

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WorldQuant-University-Projects

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A repository containing all the projects that were carried out during my Applied Data Science course learning at WorldQuant University.

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# WorldQuant-University-Projects
![quant](https://user-images.githubusercontent.com/101701760/168424721-2ab686b1-51cf-4fb3-9bc1-7ad26828f937.png)

A repository containing all the projects that were carried out during my Applied Data Science course learning at WorldQuant University.

## The Projects
1. Predicting House Prices in Mexico
2. Predicting Apartment Prices in Bueno Aires 
3. Predicting Air Quality in Nairobi
4. Predicting Earthquake Damage in Nepal
5. Predicting Bankruptcy in Poland

### Predicting House Prices in Brazil
In this assignment, you'll work with a dataset of homes for sale in Brazil. Your goal is to determine if there are regional differences in the real estate market. Also, you will look at southern Brazil to see if there is a relationship between home size and price, similar to what you saw with housing in some states in Mexico.

### Predicting Apartment Prices in Mexico City
This was similar to the first project but we had to create a wrangle function that could do the following:
1. Subset the data in the CSV file and return only apartments in Mexico City ("Distrito Federal") that cost less than $100,000.
2. Remove outliers by trimming the bottom and top 10% of properties in terms of "surface_covered_in_m2".
3. Create separate "lat" and "lon" columns.
4. Mexico City is divided into 16 boroughs. Create a "borough" feature from the "place_with_parent_names" column.
5. Drop columns that are more than 50% null values.
6. Drop columns containing low- or high-cardinality categorical values.
7. Drop any columns that would constitute leakage for the target "price_aprox_usd".
8. Drop any columns that would create issues of multicollinearity.

### Predicting Air Quality in Nairobi
In this project we used a database server(MongoDB) and also predicted air quality in Nairobi Kenya for particular times.

### Earthquake Damage in Kavrepalanchok
In this assignment, we built a classification model to predict building damage for the district of Kavrepalanchok.

### Algorithms 
1. Linear Regression
2. Logistic Regression
3. Random Forest
4. Decision Trees

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