Marketing Analysis

This project investigates the impact of marketing spend on enrollments and sales. Using real-world data, Pearson correlation analysis and linear regression models were employed to explore the relationships between marketing investments, enrollments, and sales performance.

Data Analysis with Python

I used Python to analyze real estate data from Brazil. I cleaned the data, explored the distribution of home prices and sizes, and compared home prices across different regions. I also looked at the relationship between home prices and the area in the state of Rio Grande do Sul.

Linear Regression Model to Predict Apartment Prices in Rio Grande do Sul

This project implements a linear regression model to predict the price of apartments in Rio Grande do Sul. The model is able to learn from the training data and make better predictions than the baseline model.

Mexico City Real Estate Data Cleaning.

This project focuses on cleaning and wrangling real estate data for Mexico City. The goal is to prepare a clean dataset that can be used for analysis and a machine learning model.

Mexico City Real Estate Price Prediction Using Size

This project contains a Jupyter notebook that predicts the price of apartments in Mexico City based on their size. The notebook uses a linear regression model.

Mexico City Real Estate Price Prediction Using Location (Longitude and Latitude)

This project predicts apartment size using location (longitude and latitude). EDA is performed, a linear regression pipeline is built to predict prices using the locations as features. The model performance is evaluated and location is found to not be a strong predictor of price in the dataset.

Mexico City Real Estate Price Prediction Using Neighborhood

This project develops a machine learning model to predict Mexico City apartment prices from neighborhood data. Encoding techniques and ridge regression are used to handle issues like categorical data and overfitting. The model identifies the most influential neighborhoods for pricing.

Students' On Track Metrics

Description: Developed a system to categorize student engagement levels using login activity, module completion, and module frequency. Built a model to cluster the students into categories based on their engagement level.