Datacamp projects

I have compiled all the datacamp projects I have been solving until this day. These projects are listed below:

1) predictive_modeling_agriculture

Supervised machine learning and feature selection with logistic regression to select the best soil cultivation indicator that predicts the highest crop growth. The best soil indicator is potassium (K) for all types of crops.

• Keywords: Python; Logistic regression; Classification; Machine learning; Agriculture.

2) taxi_fare_random_forest

Analyzed 2013 NYC taxi journey data to predict fares and tips using regression trees and random forests. The taxi fare prediction is based on the location on Manhattan's island and the time of day. The most expensive location from the model is downtown.

• Keywords: R; Random forest; Decision tree; Regression; Machine learning; Taxi fares prediction.

3) kmeans_clustering_penguin_species

Application of K-means clustering to label penguin species based on their physical characteristics. This analysis showed that the best solution is 3 clusters.

• Keywords: Python; K-means; Unsupervised learning; Clustering; Machine learning; Animal species; Biology.

4) cnn_keras_clothes_image_recognition

This code demonstrates how to build, train, and evaluate a Convolutional Neural Network (CNN) for image recognition (and classification) using TensorFlow and Keras on the Fashion MNIST dataset. The current model shows an an accuracy of 85% in the testing dataset.

• Keywords: Python; Keras; Deep learning; Convolutional neural network; Image recognition; Clothes classification.