Predictive Analytics
Forecasting future trends and outcomes using statistical models
Understanding Predictive Analytics
Predictive analytics uses historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes. It goes beyond knowing what happened to providing a best assessment of what will happen in the future.
Predictive Analysis Process
Data Collection
Gather historical data
Data Preparation
Clean and transform
Model Building
Train and validate
Deployment
Make predictions
Common Techniques
Time Series Analysis
- • ARIMA Models
- • Exponential Smoothing
- • Prophet
Machine Learning
- • Regression Models
- • Random Forests
- • Neural Networks
Implementation Example
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
import numpy as np
from sklearn.metrics import mean_squared_error, r2_score
# Load time series data
df = pd.read_csv('sales_data.csv')
df['date'] = pd.to_datetime(df['date'])
# Feature engineering
df['year'] = df['date'].dt.year
df['month'] = df['date'].dt.month
df['day'] = df['date'].dt.day
# Prepare features and target
X = df[['year', 'month', 'day', 'previous_sales']]
y = df['sales']
# Split data
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
# Train model
model = RandomForestRegressor(n_estimators=100)
model.fit(X_train, y_train)
# Make predictions
predictions = model.predict(X_test)
# Evaluate
mse = mean_squared_error(y_test, predictions)
r2 = r2_score(y_test, predictions)
print(f'MSE: {mse:.2f}')
print(f'R2 Score: {r2:.2f}')Real-World Applications
Sales Forecasting
Predicting future sales using historical data and seasonal patterns
Risk Assessment
Evaluating potential risks in financial and business operations