About • Reproductibility • Websites Exercises • Author • License
This repository was created in order to store and share all my experiences amoung Datascience exercises that we can found in the web.
All this exercises have been solved by myself. You can freely check the code or the HTML recap in each model in order to see how I've proceed.
- Download the latest version of the repository as ZIP file.
or
- In your terminal/command line
git clone https://github.com/camilleAmaury/MachineLearningExercises.git
- Anaconda : An environnement for datascience project and more...
- Python 3.(7) : The main language used.
- Jupyter Notebook : A really efficient and usable IDE.
| Website | Models |
|---|---|
| OPR - OpenClassRoom | Linear Regression, KNN, Ridge Regression, Lasso Regression, Linear SVM, Logistic Regression, Kernel Classification (SVM), Kernel Regression (Ridge) |
| Website | Other Concept |
|---|---|
| OPR - OpenClassRoom | PCA |
- Bar plot (OPR - KNN)
- Comparative Bar plot (OPR - Linear Regression)
- ScatterPlot (OPR - Linear Regression)
- LineChart (OPR - KNN)
- HeathMap (OPR - KNN)
- Data preprocessing
- Data exploration
- Supervised Learning
- Basic statistic indicators (mean, median, quantiles, max, min, count)
- R² score, MSE, RMSE, AUC, ROC
- Cross Validation Score
- Outliers related both on mean and median
- Classification
- Hyperparameter
- Overfitting
- Label Encoder (category)
- Normalization, Min-max standardization
- Covariance, Correlation
See the LinkedIn Shield.
Freely to use for your personnal purpose. Prohibited for commercial purpose.
You should still mention me if you want to use it in the public sphere.