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bank-campaign-classification

Advanced R Group Project

bikesharing-using-dask

exploratory-data-analysis-with-ggplot2

For this project, credit card transactions are analyzed to extract insights on tourists' spending patterns during a specific period of time. The goal is to discover different insights from the data through data visualization (with GGPlot2) and then transform them into potential business outcomes. Some of the questions that will be answered are: 1. Is there a particular time in which tourists are buying? 2. Which business category do tourist spend most on? 3. Which nationality has the highest average ticket and why? 4. Which nationality has the highest expenditure volumes? 5. Do high end fashion retailers need to focus more on attracting visitors of a specific set of nationalities? Why?

fake-news-classifier

ie-mbd-advanced-python

"Advanced Python" subject from the Master in Big Data @ IE

ie_pandas

This project contains the group assignment for Advanced Python, on which we have to create our own Python installable package for own created simplified "Pandas library".

kc-house-prices

predicting-house-prices

In this following practice, we will attempt to predict house prices in Iowa based on the House Prices Dataset on Kaggle: <https://www.kaggle.com/c/house-prices-advanced-regression-techniques>. The dataset presents different features of the houses that variably change the predictions. The data will require some work for cleaning and preprocessing to be ready for modeling. First, we will need to load the packages required for preprocessing and modeling phases. The steps of this practice will involve: 1 - Cleaning the data from null values using domain knowledge or practical judgements. 2 - Removing outliers from train data so they don't affect our predictions. 3 - Feature Engineering: this will involve transforming variables, creating new variables, and eliminating variables in favor of our predictions. 4 - Adjusting skewness for our numeric variables 5 - Using our transformed data with different practices and apply different evaluation models to predict the final price of each home

predicting-washington-bike-sharing

Predict the total number of bicycle users on an hourly basis in the city of Washington.