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Bhavesh Badani

Bhavesh Badani

19-May-2024

Machine learning model involves several key steps. Let’s break it down:

Problem Definition:

  • Clearly define the problem you want the model to solve. Understand what you’re trying to predict or classify.

Data Preparation:

  • Collect relevant data for your problem. Clean and preprocess the data by handling missing values, outliers, and formatting issues.

Model Selection:

  • Choose an appropriate machine learning model based on your problem type (e.g., regression, classification, clustering). Consider factors like interpretability, accuracy, and scalability.

Training Your Model:

  • Use a portion of your data to train the model. The model learns from the input features and their corresponding target values.

Evaluating Model Performance:

  • Assess how well your model performs on unseen data. Metrics like accuracy, precision, recall, and F1-score help evaluate performance.

Tuning and Optimizing Your Model:

  • Fine-tune hyperparameters (settings) to improve model performance. This involves adjusting learning rates, regularization, and other parameters.

Deploying the Model:

  • Once satisfied with the model’s performance, deploy it for making predictions on new data.

A machine learning model is an iterative process, and continuous improvement is essential.

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