From Data Collection to Model Deployment: A Step-by-Step Guide in Machine Learning

1 Collect Data => To train we need data so we collect data from different sources like Excel, SQL, Web Scraping or many other sources... There are many sources like Kaggle or Stats, these are websites From where we can download the datasets, most of the datasets are available in CSV (Comma Separated Values), which is the Excel format that is most used in ML models.

2) Data Cleaning => Whenever we get data it is available in raw format which is not directly usable in ML purpose, it means some data is missing or there is null in data or some in wrong format. Is. Is. Are there any or some duplicates and much more. ,This type of data is called messy data. If we use it directly in ML models we may get wrong or binary predictions, confusing insights or low accuracy, so it is extremely important that we avoid using it in ML. First of all the data should be cleaned, for this we have many tools like pandas, sklearn. In Python, cloud options and many others but in initial ML journey pandas is the most popular library in which we have many functions which are used to remove duplicates, detect outliers, organize and stabilize the data so that we can get useful insights. Can get. Can get. Can get. This process is also called feature engineering.

3) Visualization => As a data scientist or data analyst we get insights from data, so after cleaning and organizing the data we can present in graph format, it gives information about data relationships, patterns and flow. Gives information. Gives information. Very easy way to understand, graphs will help to choose best ML algorithm according to data, using line charts, scatter plots, bar graphs, histograms and pie charts, these are very important to understand data quickly in Python. We have Matplotlib, Plotly and Dash, these are some libraries that provide us with many graphs.

4) Training and Segmentation => Now it is time to start training the data, now we can give our selected features along with the results to the machine in supervised learning, for this we use sklearn library which contains multiple algorithms according to the data pattern. Are. So that we can select the related algorithm and start training the data, in this process the machine finds the patterns and relationships between the features and the result, so from this training side it will predict the result in future but keep in mind that in the training We should not give complete data like we have a data set in which we have around 5000 rows, we should not give all the rows in training, we should give 70 to 80% data for training, and rest of the data we should keep with ourselves Testing. For this we use the split method from sklearn.

5) Prediction and Testing => After training, our model is ready to make predictions. In supervised learning, the new data we use for prediction needs to be similar to the data we trained the model with. This helps the machine make accurate predictions based on what it learned. We need to test our model to see if its predictions are correct or not. We use different methods and metrics like MAE, MSE, RMSE, R2, and confusion metrics to check how well our model is doing. If our predictions are not very accurate, we need to work on improving the model. It's important to test our model before using it in real-life situations. We should make sure it's in the right shape and format for deployment.

6) Deploy the Model => If we're happy with how accurate our model is, we can put it into action by deploying it to the cloud or an app. We typically do this using Flask or Django. Usually, we save our model in a format called Pickle for easy integration with APIs.

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