A Beginner’s Guide to Building Your First Machine Learning Model.

Step by step

Embarking on the journey of building your first machine-learning model can be both exciting and overwhelming. Rest assured, everyone starts somewhere, and here’s a helpful guide to steer you through the initial steps:

🎯 Data Extraction

Your journey begins with the data. Where is your dataset coming from, and what environment are you working in? Whether it’s Kaggle, Colab, Deep Note, Jupyter Notebook, or VS Code, understanding your scripting environment is crucial. Start with reasonably sized datasets, avoiding overly large ones initially. #dataextraction

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🎯 Data Exploration & Analysis

Dig deep into your data during this phase. Identify missing values or cells that might impact your model’s performance. Learn about the structure and data types inherent in the dataset. This step is essential in preparing your dataset for the training process. #dataexploration #dataanalysis

🎯 Algorithm Selection

Now, it’s time to choose the right machine learning algorithm. Is your dataset labeled (supervised)? Does it involve numeric values (supervised-regression)? Define the business goal you intend to achieve and select an algorithm that aligns with it. #algorithmselection

🎯Apply the Chosen Algorithm

Apply the chosen algorithm to your dataset. Fit your model by training it with the selected features. For instance, if you want to predict product prices (y), identify the factors affecting prices (feature selection, X).

🎯Prediction & Evaluation

Run predictions using the test dataset on the trained model. This is the out-of-sample testing phase. Gauge the performance of your model using various evaluation techniques and metrics. Ensure that predicted outputs align closely with the actual values. #prediction #evaluation

🎯 Business Application of the Model

Finally, consider the real-world application of your model. How can it be utilized to solve a specific business problem or achieve a desired outcome? Implement the insights gained from your model into practical scenarios.

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What’s Next?

Congratulations, you’ve taken your first steps into the world of machine learning! This guide is openly sourced, meaning you can adapt and build upon it. Keep learning, exploring, and applying your newfound knowledge.

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Happy learning on your machine-learning journey!

#machinelearning