In this project we try to predict income and gender using US Census Data. We will use classification models to try to predict both variables. Before we try out the different classification models there was some data exploration and processing that had to occur first: 1 - Since income data was a numerical variable, we changed it to a categorical variable where the income was either <= 50k or >50k. 2 - We deal with missing and duplicate values 3 - Removal of outliers 4 - Correlation between features 5 - One-hot-encoding categorical variables 6 - Splitting data into training and test Data

Kobe Bryant was a prolific American professional basketball player, and is considered one of the greatest players in the history of the game. We are provided with the location and circumstances of every shot attempted by Bryant during his 20-year career. The goal of the project is twofold: 1st - Model the data to predict whether the shot was made successfully or not on the test dataset, given the model data set.