Building a machine learning (ML) model involves several steps. Here is a general outline of the process:
1. Define the problem: Clearly define the problem you want to solve with ML. Determine the objective, the available data, and the expected outcome of the model.
In this step we need to looked the data and determine which type of solution it's needed like regression, classification or clustering
2. Gather and preprocess the data: This process involves importing data and cleaning according the need.
Like removing null columns, filling empty fields according to the need
3. Split the data: Divide the dataset into training, validation, and test sets. The training set is used to train the model, the validation set helps in tuning the model's hyperparameters, and the test set is used to evaluate the final model's performance.
4. Select a model and algorithm: Choose an appropriate ML algorithm based on the problem type, data characteristics, and available resources. It could be a decision tree, support vector machine, neural network, or other algorithms suitable for regression, classification, clustering, etc.
5. Train the model: Feed the training data into the chosen algorithm and train the model. The model learns from the data and adjusts its internal parameters to minimize errors and improve its predictions.
6. Validate and tune the model: Use the validation set to assess the model's performance and fine-tune its hyperparameters. This involves adjusting parameters like learning rate, regularization, or network architecture to optimize the model's performance.
7. Evaluate the model: Once the model is trained and tuned, evaluate its performance using the test set. Metrics such as accuracy, precision, recall, or mean squared error can be used depending on the problem type.
8. Deploy the model: If the model performs well, deploy it in a real-world setting to make predictions on new, unseen data. Integrate the model into the desired application or system to generate predictions or decisions.
9. Monitor and update: Continuously monitor the model's performance in the production environment. If necessary, update the model periodically using new data to maintain its accuracy and relevance.
It's important to note that these steps can vary depending on the specific ML problem, algorithm, and domain. Experimentation, iteration, and continuous improvement are key elements of the ML model-building process.
Model Functions Classification:
def Classification_models(X_train,Y_train):
#Using DecisionTreeClassifier of tree class to use Decision Tree Algorithm
from sklearn.tree import DecisionTreeClassifier
tree = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)
tree.fit(X_train, Y_train)
#Using RandomForestClassifier method of ensemble class to use Random Forest Classification algorithm
from sklearn.ensemble import RandomForestClassifier
forest = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)
forest.fit(X_train, Y_train)
#Using KNeighborsClassifier Method of neighbors class to use Nearest Neighbor algorithm
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
knn.fit(X_train, Y_train)
#Using SVC method of svm class to use Kernel SVM Algorithm
from sklearn.svm import SVC
svc_rbf = SVC(kernel = 'rbf', random_state = 0)
svc_rbf.fit(X_train, Y_train)
forest.fit(X_train, Y_train)
#Using SVC method of svm class to use Support Vector Machine Algorithm
from sklearn.svm import SVC
svc_lin = SVC(kernel = 'linear', random_state = 0)
svc_lin.fit(X_train, Y_train)
#print model accuracy on the training data.
print('[1]K Nearest Neighbor Training Accuracy:', knn.score(X_train, Y_train))
print('[2]Support Vector Machine (Linear Classifier) Training Accuracy:', svc_lin.score(X_train, Y_train))
print('[3]Support Vector Machine (RBF Classifier) Training Accuracy:', svc_rbf.score(X_train, Y_train))
print('[5]Decision Tree Classifier Training Accuracy:', tree.score(X_train, Y_train))
print('[6]Random Forest Classifier Training Accuracy:', forest.score(X_train, Y_train))
return knn, svc_lin, svc_rbf, tree, forest
All Model Function
def All_models(X_train,Y_train):
#Using Logistic Regression Algorithm to the Training Set
from sklearn.linear_model import LogisticRegression
log = LogisticRegression(random_state = 0)
log.fit(X_train, Y_train)
#Using KNeighborsClassifier Method of neighbors class to use Nearest Neighbor algorithm
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
knn.fit(X_train, Y_train)
#Using SVC method of svm class to use Support Vector Machine Algorithm
from sklearn.svm import SVC
svc_lin = SVC(kernel = 'linear', random_state = 0)
svc_lin.fit(X_train, Y_train)
#Using SVC method of svm class to use Kernel SVM Algorithm
from sklearn.svm import SVC
svc_rbf = SVC(kernel = 'rbf', random_state = 0)
svc_rbf.fit(X_train, Y_train)
#Using GaussianNB method of naïve_bayes class to use Naïve Bayes Algorithm
from sklearn.naive_bayes import GaussianNB
gauss = GaussianNB()
gauss.fit(X_train, Y_train)
#Using DecisionTreeClassifier of tree class to use Decision Tree Algorithm
from sklearn.tree import DecisionTreeClassifier
tree = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)
tree.fit(X_train, Y_train)
#Using RandomForestClassifier method of ensemble class to use Random Forest Classification algorithm
from sklearn.ensemble import RandomForestClassifier
forest = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)
forest.fit(X_train, Y_train)
#print model accuracy on the training data.
print('[0]Logistic Regression Training Accuracy:', log.score(X_train, Y_train))
print('[1]K Nearest Neighbor Training Accuracy:', knn.score(X_train, Y_train))
print('[2]Support Vector Machine (Linear Classifier) Training Accuracy:', svc_lin.score(X_train, Y_train))
print('[3]Support Vector Machine (RBF Classifier) Training Accuracy:', svc_rbf.score(X_train, Y_train))
print('[4]Gaussian Naive Bayes Training Accuracy:', gauss.score(X_train, Y_train))
print('[5]Decision Tree Classifier Training Accuracy:', tree.score(X_train, Y_train))
print('[6]Random Forest Classifier Training Accuracy:', forest.score(X_train, Y_train))
return log, knn, svc_lin, svc_rbf, gauss, tree, forest
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