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| Topics Covered | Price (USD) | Time |
|---|---|---|
| Basic Concepts of SciKit Learn | $50 | 1 day |
| Data Preprocessing | $70 | 2 days |
| Supervised Learning | $80 | 2 days |
| Unsupervised Learning | $90 | 3 days |
| Model Evaluation | $60 | 1 day |
| Hyperparameter Tuning | $100 | 3 days |
| Project Assistance | Custom | Custom |
- SciKit Learn Assignment Help
- Implementing Logistic Regression with SciKit Learn
- Top Reasons to Trust Our SciKit Learn Assignment Help Service
- Steps to Get SciKit Learn Assignment Help from Our Service
- Delivering Plagiarism-Free Solutions in Our SciKit Learn Assignment Help Service
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Implementing Logistic Regression with SciKit Learn
# Importing necessary libraries
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
# Load the Iris dataset
iris = load_iris()
X = iris.data
y = iris.target
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Initialize the logistic regression model
model = LogisticRegression(max_iter=1000)
# Train the model on the training data
model.fit(X_train, y_train)
# Predict on the test data
y_pred = model.predict(X_test)
# Calculate accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy of the logistic regression model: {accuracy:.2f}")
In this code snippet:
- We load the Iris dataset using SciKit Learn's load_iris function.
- Split the dataset into training and testing sets using train_test_split.
- Initialize a logistic regression model and train it on the training data.
- Predict on the test data and calculate the accuracy of the model using accuracy_score.
This example demonstrates a basic usage of SciKit Learn for training a machine learning model and evaluating its performance on a test set.
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- Visit Our Website: Start by visiting ProgrammingHomeworkHelp.com to explore our SciKit assignment help services. Navigate to the SciKit Learn Assignment Help section to learn more about what we offer.
- Submit Your Assignment Details: Fill out our easy-to-use assignment submission form. Provide detailed instructions, including assignment requirements, deadlines, and any specific requests.
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