amazon-recomendation-system

Build a recommendation system to recommend products to customers based on their previous ratings for other products.

armandogago

bullet-train-hackathon

The goal of the Hackathon is to predict whether a passenger was satisfied or not considering his/her overall experience of traveling on the Shinkansen Bullet Train.

decision-trees-and-random-forest

Decision Trees and Random Forest Project: Predicting Potential Customers. Analyze and build a ML model to help identify which leads are more likely to convert to paid customers. Find the factors driving the lead conversion process. Create a profile of the leads which are likely to convert.

deep-learning-artificial-neural-network

The main objective is to build a feed-forward neural network model that can recognize the digits in the images.

deep-learning-convolutional-neural-network

Build a CNN model that can recognize the digits in the images using the Street View Housing Number Digit Recognition.

eda-and-apriori-algorythm

Code used to do an exploratory data analysis and an Apriori algorithm with ticket sales data in the year 2020-2021 of a company which sells organic food.

kmeans-gaussianmm-and-kmedoids

Using the credit card customer base dataset, identify different segments in the existing customer base, taking into account their spending patterns as well as past interactions with the bank.

linear-regression-and-cross-validation

Predict the housing prices of a town or a suburb based on the features of the locality provided. Identify the most important features affecting the price of the house. Employ techniques of data preprocessing and build a linear regression model that predicts the prices for the unseen data.

music-recommendation-system

Build a recommendation system to propose the top 10 songs for a user based on the likelihood of listening to those songs.

pca-and-tsne-in-auto-mpg-dataset

The objective of this problem is to explore the data, extract meaningful insights, and find different groups of vehicles in the data by using dimensionality reduction techniques like PCA and t-SNE.