Predictive analysis is a statistical technique that uses historical data to predict future events or trends. It uses various machine learning models and statistical methods that can identify patterns and correlations in the data and, on that basis, predict future results.
In this article we want to present the reasons why predictive analytics is the future of success, and we have also prepared a detailed guide to the predictive analysis process with code samples.
Goals of Predictive Analysis:
- Increase efficiency: Predictive models can be used to optimise processes, prevent failures and shorten delivery times.
- Reduce risks: Predictive analysis allows companies to identify potential risks and take steps to minimise them.
- Increase profitability: Predictive models can be used to personalise products and services, target marketing campaigns and maximise sales.
- Gain a competitive advantage: Companies that can use predictive analysis effectively gain a considerable advantage over the competition.
Main Benefits of Predictive Analysis:
- Better decision-making: Predictive analysis gives companies valuable insights that help them make more informed business decisions.
- Increased efficiency: Predictive models can be used to optimise processes and shorten delivery times.
- Reduced risks: Predictive analysis allows companies to identify potential risks and take steps to minimise them.
- Increased profitability: Predictive models can be used to personalise products and services, target marketing campaigns and maximise sales.
- Improved customer satisfaction: Predictive analysis allows companies to understand customer needs better and to provide them with more personalised services.
Tools for Predictive Analysis:
There are many predictive analysis tools on the market that help companies implement and use this technique. The most popular tools include:
- SAS Enterprise Guide: Companies use SAS Enterprise Guide, a comprehensive platform for data analysis and machine learning, to easily create and implement predictive models.
- IBM SPSS Modeler: IBM SPSS Modeler is another popular platform for data analysis and machine learning, which offers a wide range of functions for predictive analysis.
- Microsoft Azure Machine Learning: Microsoft Azure Machine Learning is a cloud machine learning platform that lets companies easily create and deploy predictive models.
- Google Cloud AI Platform: Google Cloud AI Platform is another cloud machine learning platform that offers a wide range of functions for predictive analysis.
Predictive analysis and forecasting are becoming essential tools for companies that want to hold their own against the competition and achieve long-term success. By using predictive analysis, companies gain valuable insights that help them make more informed business decisions, optimise processes, reduce risks, increase profitability and improve customer satisfaction.
If you want to get into predictive analysis, do not hesitate to contact us. We will be happy to help you with it.
The Predictive Analysis Process: A Detailed Guide with Code Samples
We must follow several steps of the predictive analysis process to obtain reliable and useful predictions. Below you will find a detailed breakdown of the individual steps with code samples in Python:
1. Data collection and preparation:
- The first step is to collect the relevant data for the predictive model. The data can come from various sources, such as databases, CSV files, websites and APIs.
- The data must be cleaned and adjusted so that errors, duplicates and missing values are removed.
- We must normalise or transform the data to bring it into a format suitable for modelling.
Python code sample for loading data from a CSV file:
import pandas as pd
# Load the data from a CSV file into a DataFrame
data = pd.read_csv("data.csv")
# Explore the data
print(data.head())2. Data analysis:
- Before modelling it is important to analyse the data and understand its structure, properties and the relationships between variables.
- We can use data visualisation tools such as histograms, boxplots and correlation maps to explore the data and identify trends and anomalies.
- If the analysis turns up outliers that differ significantly from the rest of the data, we can consider removing them from the dataset.
Python code sample for data visualisation:
import matplotlib.pyplot as plt
# Create a histogram for the "price" column
plt.hist(data["price"])
plt.show()
# Create a boxplot for the "age" column
plt.boxplot(data["age"])
plt.show()
# Create a correlation map
plt.matshow(data.corr())
plt.show()3. Model selection and training:
- Based on the type of problem and the properties of the data, we choose a suitable machine learning algorithm for the prediction.
- There are many approaches to prediction, such as regression, classification or clustering. The choice of a suitable approach depends on the data we have available and the goal we want to achieve.
- For each approach we can choose from a whole range of algorithms, from classical linear regression through decision trees and the support vector method to deep neural networks.
- The choice of a specific algorithm again depends on the data, the problem being solved and its context.
- We must train the resulting model on part of the data, the so-called training set. Usually 80% of the available data is taken.
- The model learns from the data of the training set by trying to minimise its loss (that is, the "error rate" of the prediction).
Python code sample for training a regression model:
from sklearn.linear_model import LinearRegression
# Create the regression model
model = LinearRegression()
# Train the model on the training set
model.fit(X_train, y_train)4. Model evaluation and tuning:
- After training the model we must evaluate it on the part of the data that we did not use for training, the so-called test set.
- The evaluation helps us assess the accuracy and generalisability of the model for the given task.
- If the model does not reach the required accuracy, we can tune it further by adjusting the hyperparameters (= parameters that are set by a human manually, not those that are found by learning) of the algorithm or by choosing a different algorithm.
Python code sample for evaluating the model:
from sklearn.metrics import mean_squared_error
# Predict on the test set
y_pred = model.predict(X_test)
# Calculate the mean squared error (MSE)
mse = mean_squared_error(y_test, y_pred)
# Print the MSE value
print("MSE:", mse)5. Model deployment and monitoring:
- After successful evaluation and tuning of the model we can deploy it to the production environment, where it will generate predictions for new data.
- We must monitor the model and track its performance over time. If the performance of the model deteriorates, we must retrain or adjust it.
Python code sample for deploying the model:
# Load the saved model from a file
import pickle
model = pickle.load(open("model.pkl", "rb"))
# New data for prediction
new_data = [[...]] # Insert the new data here
# Generate predictions
y_pred = model.predict(new_data)
# Print the predictions
print("Predictions:", y_pred)