GitHub - lordkevinmo/machine-learning-with-python: Les fichiers hébergés dans cette repository contiennent des projets sur des applications de divers algorithme de Machine Learning.

This repository was archived by the owner on Jul 22, 2019. It is now read-only.

Name	Name	Last commit message	Last commit date
Latest commit lordkevinmo Update README.txt Jan 18, 2019 8f0fc2a · Jan 18, 2019 History 4 Commits
3D_distribution_based_on_Education_age_Income.png	3D_distribution_based_on_Education_age_Income.png	Update	Jan 18, 2019
About-recommander-dataset.txt	About-recommander-dataset.txt	Update 2	Jan 18, 2019
LICENSE	LICENSE	Initial commit	Jan 18, 2019
README.txt	README.txt	Update README.txt	Jan 18, 2019
Regression.txt	Regression.txt	Update	Jan 18, 2019
agglomerative_clusterin_with_scatter_plot.png	agglomerative_clusterin_with_scatter_plot.png	Update	Jan 18, 2019
based_content_filtering.py	based_content_filtering.py	Update	Jan 18, 2019
cancer_classification.py	cancer_classification.py	Update	Jan 18, 2019
centroid_visualisation.png	centroid_visualisation.png	Update	Jan 18, 2019
client_segmentation.py	client_segmentation.py	Update	Jan 18, 2019
client_segmentation_with_kmeans.png	client_segmentation_with_kmeans.png	Update	Jan 18, 2019
clustering_with_dbscan_on_random_dataset.png	clustering_with_dbscan_on_random_dataset.png	Update	Jan 18, 2019
clustering_with_kmeans_on_random_dataset.png	clustering_with_kmeans_on_random_dataset.png	Update	Jan 18, 2019
co2_emission_scatter.png	co2_emission_scatter.png	Update	Jan 18, 2019
collaborative_filtering.py	collaborative_filtering.py	Update	Jan 18, 2019
confusion_matrix.png	confusion_matrix.png	Update	Jan 18, 2019
data_fit.png	data_fit.png	Update	Jan 18, 2019
dataset_plot.png	dataset_plot.png	Update	Jan 18, 2019
dbscan_clustering.py	dbscan_clustering.py	Update	Jan 18, 2019
dendrogramme_obtenu_avec_le_jeu_de_donnee_aleatoire.png	dendrogramme_obtenu_avec_le_jeu_de_donnee_aleatoire.png	Update	Jan 18, 2019
distribution_of_customers_based_on_their_age_and_income.png	distribution_of_customers_based_on_their_age_and_income.png	Update	Jan 18, 2019
distribution_of_the_classes_based_on_clump thickness_and_uniformity(classification).png	distribution_of_the_classes_based_on_clump thickness_and_uniformity(classification).png	Update	Jan 18, 2019
drugtree.png	drugtree.png	Update	Jan 18, 2019
emission(cylinders)_scatter.png	emission(cylinders)_scatter.png	Update	Jan 18, 2019
emission(engine_size)_scatter.png	emission(engine_size)_scatter.png	Update	Jan 18, 2019
hierarchical_clustering.py	hierarchical_clustering.py	Update	Jan 18, 2019
model_accuracy_telecust_classification_accuracy(number_of_naighbors).png	model_accuracy_telecust_classification_accuracy(number_of_naighbors).png	Update	Jan 18, 2019
non-linear-regression.py	non-linear-regression.py	Update	Jan 18, 2019
nuage_de_points_des_donnees_aleatoires.png	nuage_de_points_des_donnees_aleatoires.png	Update	Jan 18, 2019
patient_decision_tree.py	patient_decision_tree.py	Update	Jan 18, 2019
regression.png	regression.png	Update	Jan 18, 2019
regression.py	regression.py	Update	Jan 18, 2019
scatter_plot_of_price_mpg(horspow).png	scatter_plot_of_price_mpg(horspow).png	Update	Jan 18, 2019
sigmoid_plot.png	sigmoid_plot.png	Update	Jan 18, 2019
station_clustering.png	station_clustering.png	Update	Jan 18, 2019
telecom_company_model.py	telecom_company_model.py	Update	Jan 18, 2019
telecom_company_model_confusion_matrix.png	telecom_company_model_confusion_matrix.png	Update	Jan 18, 2019
telecust_classification.py	telecust_classification.py	Update	Jan 18, 2019
underfiting.png	underfiting.png	Update	Jan 18, 2019
vehicle_clustering_dendrogram.png	vehicle_clustering_dendrogram.png	Update	Jan 18, 2019
vehicle_clustering_scatter_plot.png	vehicle_clustering_scatter_plot.png	Update	Jan 18, 2019
visualization_of_stations_on_the_map.png	visualization_of_stations_on_the_map.png	Update	Jan 18, 2019

Repository files navigation

Les fichiers hébergés dans cette repository contiennent des projets sur des applications de divers algorithme de Machine Learning.

Les datasets utilisées sont hébergées sur le lien suivant : 

https://drive.google.com/open?id=1nZS7fGIUSrks7OAYzXSZKNGa7fEXxccx

Les algos étudiés sont : 

1. La regression
- Régression Linéaire Simple
- Régression Linéaire Multiple
- Régression Non-Linéaire 

2. Classification
- K plus proche voisins
- Arbre de décision
- Régression logistique
- Machine à vecteur de support

3. Regroupement (Clustering)
- K Moyens
- Regroupement hiérarchique
- DBSCAN

4. Système de recommendation
- Recommendation basée sur le contenu
- Filtrage collaboratif