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The libraries and example codes for each of the machine learning models using Python https://medium.com/@osama.ghandour/the-libraries-and-example-codes-for-each-of-the-machine-learning-models-using-python-ed3f1ee83b14

the libraries and example codes for each of the machine learning models using Python:

1. Linear Regression: — Library: scikit-learn — Code Example: python from sklearn.linear_model import LinearRegression model = LinearRegression()

2. Logistic Regression: — Library: scikit-learn — Code Example: python from sklearn.linear_model import LogisticRegression model = LogisticRegression()

3. Support Vector Machines (SVM): — Library: scikit-learn — Code Example: python from sklearn.svm import SVC model = SVC()

4. K-Nearest Neighbors (KNN): — Library: scikit-learn — Code Example: python from sklearn.neighbors import KNeighborsClassifier model = KNeighborsClassifier()

5. Random Forest: — Library: scikit-learn — Code Example: python from sklearn.ensemble import RandomForestClassifier model = RandomForestClassifier()

6. Gradient Boosting Machines (e.g., XGBoost, LightGBM): — Libraries: xgboost, lightgbm — Code Examples: ```python import xgboost as xgb model = xgb.XGBClassifier()

Or

import lightgbm as lgb model = lgb.LGBMClassifier() ```

1. Neural Networks (Deep Learning): — Library: tensorflow or pytorch — Code Example (using tensorflow): python import tensorflow as tf model = tf.keras.Sequential([ tf.keras.layers.Dense(128, activation=’relu’), tf.keras.layers.Dense(1, activation=’sigmoid’) ])

2. Naive Bayes: — Library: scikit-learn — Code Example: python from sklearn.naive_bayes import GaussianNB model = GaussianNB()

3. Clustering Algorithms (e.g., K-Means, DBSCAN): — Library: scikit-learn — Code Example (for K-Means): python from sklearn.cluster import KMeans model = KMeans(n_clusters=3)

4. Principal Component Analysis (PCA): — Library: scikit-learn — Code Example: python from sklearn.decomposition import PCA model = PCA(n_components=2)

5. Reinforcement Learning Algorithms (e.g., Q-Learning, Deep Q Networks): — Library: gym (for environments), tensorflow or pytorch (for models) — Code Example: ```python import gym env = gym.make(‘CartPole-v1’)

Define and train Q-learning or Deep Q Network

(Complex and requires reinforcement learning libraries)

```

Note: Before running the above code examples, you need to install the required libraries using pip install scikit-learn xgboost lightgbm tensorflow gym. Additionally, more complex models like neural networks for deep learning or specific reinforcement learning algorithms may require more extensive setup and configuration.

Why do we are depend on Engineering , Technology and programming in STEM Education as In Assiut STEM School مدرسة اسيوط للمتفوقين فى العلوم و التكنولوجيا