Define the Problem: Clearly identify the objective and the outcome you want to predict, such as forecasting sales or customer churn
Collect and Clean Data: Gather relevant data, handling missing values and removing outliers to ensure your dataset is accurate and usable
Choose the Right Algorithm: Select a predictive modeling algorithm like linear regression, decision trees, or random forests based on the nature of your data and problem
Train and Test the Model: Split your data into training and test sets, using the training set to teach the model and the test set to evaluate its performance
Evaluate and Fine-Tune: Assess your model’s accuracy using performance metrics like accuracy, precision, or RMSE, and make adjustments to improve results