Easy 7 Step Process to Build Machine Learning Model from scratch in Python.

Here's a seven-step process to build a machine learning model from scratch in Python:

1. Identification of the business problem

• The first step of any ML-based project is to understand the requirements of the business.
• You need to develop an understanding of the problem before attempting to decode it.

Step 2: Collect and preprocess data

• Collect data from reliable sources, such as Kaggle or UCI Machine Learning Repository, or you can create your own data.
• Preprocess the data by cleaning and formatting it, handling missing values, and converting categorical data into numerical data.

Step 3: Split the data

• Split the data into training, validation, and testing sets.
• The training set is used to train the model, the validation set is used to fine-tune the hyperparameters of the model, and the testing set is used to evaluate the final performance of the model.

Step 4: Choose and train a model

• Choose a suitable machine learning model for your problem such as Logistic Regression, Linear Regression, Decision Tree, Random Forest etc.
• Train the model using the training set.
• Use appropriate techniques to avoid overfitting, such as regularization, early stopping, or dropout.

Step 5: Fine-tune hyperparameters

• Use the validation set to fine-tune the hyperparameters of the model.
• Try different values for hyperparameters such as learning rate, number of hidden layers, and number of neurons per layer.
• Use techniques such as grid search or random search to find the best set of hyperparameters.

Step 6: Evaluate the model

• Use the testing set to evaluate the performance of the model.
• Calculate various metrics such as accuracy, precision, recall, F1 score, and AUC-ROC.

Step 7: Present your results

• Create a summary of your project and highlight the key insights.
• Use visualizations such as graphs, charts, and tables to make your results more appealing.
• Share your code on GitHub, LinkedIn or any other platform.