import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.metrics import accuracy_score, f1_score
from sklearn.metrics import recall_score, precision_score6 Steps to create a classification model
The steps below will outline each of the stages, in order, you will need to complete to create a model and then improve it.
6.1 Import the necessary packages
You can’t do anything without these! You will need:
6.2 Building a Baseline Classification Model
Define the Problem: Clearly understand and define the classification problem you’re aiming to solve. Determine whether it’s a binary classification or a multi-class classification.
Data Collection: Gather the data that will be used to train and test the model. This could involve collecting new data or using an existing dataset.
6.3 Data Cleaning and Preprocessing:
Handle missing values through imputation or removal.
Convert categorical variables into numeric representations using techniques like one-hot encoding.
Normalise or standardise numerical features to ensure they’re on a similar scale.
Exploratory Data Analysis (EDA): Visualise and analyse the data to find patterns, outliers, and understand the distribution of the data. Use visual tools like histograms, scatter plots, and box plots.
Feature Selection: Choose the most relevant features that contribute to the predictive power of the model. Remove irrelevant or redundant features.
Split the Data: Divide your data into training and test sets to evaluate the performance of your model. A common split is 80% training and 20% testing.
Choose a Model: Start with a simple model that is quick to implement, such as Logistic Regression for binary classification or Decision Trees for multi-class problems. This serves as your baseline.
Train the Model: Use the training data to fit the model.
Evaluate the Model: Test the model on the unseen test set to evaluate its performance using metrics such as accuracy, precision, recall, and the F1 score. Confusion matrices are also helpful to visualise the model’s performance.
Interpret Results: Analyse the outcomes to understand how well the model is performing and identify any areas for improvement.
6.4 Improving the Classification Model
Feature Engineering: Create new features or modify existing ones to improve the model.
Model Complexity: Experiment with more complex models or ensembles, such as Random Forests, Gradient Boosting Machines (GBMs), or Support Vector Machines (SVMs), to see if they offer better performance.
Hyperparameter Tuning: Use techniques like Grid Search or Random Search to find the optimal set of model parameters for the best performance.
Cross-Validation: Implement cross-validation to assess how your model will generalise to an independent dataset. This helps ensure the model’s robustness.
Class Imbalance Handling: If your dataset is imbalanced, explore techniques like SMOTE, undersampling, or oversampling to balance it. Alternatively, adjust the class weight parameter in your model if available.
Advanced Evaluation Metrics: Depending on your problem, consider using advanced metrics like the Area Under the Curve (AUC) for ROC curves, especially for imbalanced datasets.
Feature Importance: Analyse which features are most important in making predictions. This can inform feature selection and provide insights into the data.
Model Ensembles: Combining the predictions of several models can often improve performance. Techniques include Bagging, Boosting, and Stacking.
Iterate: Machine learning is an iterative process. Use the insights gained from each model iteration to refine your approach, retrain your model, and improve its performance.