where we use Standard Scaler, Logistic Regression Model, and Random Forest Classifier Model for supervised learning.
Showcasing Pandas, Python, and Sklearn, the challenge gathers data about spam. It separates the data in train and test and scales the data. Instances of logistic regression model and random forest classifier models are created and fitted with the data. The models and testing data are used to create predictions. Model and accuracy scores are calculated to determine the better performing model.
└───root
│ spam_detector.ipynb
└───README.md
Step 1:
Navigate to the "spam_detector.ipynb" file in GitHub repo. The output for each panel can be viewed in the "Preview" panel.
Alternatively,
Step 1:
Clone the repository.
Step 2:
In terminal, activate your conda environment and type conda scikit-learn.
Step 3:
If the library is installed in your conda environment, the terminal will output the package name and version number. If installed, skip to Step 5. If not installed, go to Step 4.
Step 4:
pip install -U scikit-learn
Step 5:
Open "spam_detector.ipynb"
Step 6:
Run all panels.
The data was supplied as starter code by ASU edX Boot Camps LLC.
The data is located at https://static.bc-edx.com/ai/ail-v-1-0/m13/challenge/spam-data.csv
Dataset Source: UCI Machine Learning Library