Complete Machine Learning Project Flowchart Explained!

If you are new to machine learning or confused about your project steps, this is a complete ML project life cycle flowchart with an in-depth explanation of each step.

Problem Formulation: This is the initial step for any machine learning project. You need to find a problem that you can solve using machine learning algorithms or if you have already then you need to be very clear about the problem you want to solve and the problem type.

I’m assuming that you already know about the types of machine learning. This guide mostly focuses on supervised learning. There are two types of problems that we want to predict commonly, they are Classification and Regression problems. In classification, we predict categorical values e.g. yes or no, 0 or 1, and in regression, we predict continuous values e.g. house rent, probability of rain today, etc.

Data Acquisition: In this part, we need to find datasets for our selected problem. We can get datasets in two ways. Access datasets from public data sources e.g. Kaggle, UCI, MDPI, and by collecting your dataset.

Data Cleaning and Labelling: If you are using a dataset from any public data repository you may need to clean it first. There might be some unnecessary features present in a public dataset that are not important for your project or problem. In case, you collect a dataset you may need to label, it depending on your problem and requirements.

Machine Learning Project Flowchart

Exploratory Data Analysis (EDA): Okay, now you have the dataset and a clear idea about the target feature we want to predict. You can simply understand the EDA part by thinking like, we have to extract a dataset summary and find a story from it. Every data can produce some story, we just need to tell the story to others by expressing it in numbers or graphs. So, we call it data analysis when we extract data summary or statistics and data visualization when it’s represented in graphs.

Data Pre-processing: Our data needs to be prepared for the learning phase. Before going to the learning phase you just need to check a few things:

1. Are there any duplicate records in your dataset? If any, remove them.
2. Are there any null values in your dataset? If any, handle them.
3. Find outliers and handle them.
4. Check the correlation between each feature. Remove features depending on high or low correlations. Add features if necessary. (It depends on the dataset)
5. In the case of classification problem if out is there any class imbalance issues? If any, then try to balance it by using any oversampling techniques.
6. Encode your categorical features by using an encoder. e.g. Normalization or Standardization.
7. Scale your features into a range by using a scale. e.g. MinMaxScaler or StandardScaler.

After performing all these, you have processed the dataset for the next step.

Split Dataset: Depending on the shape of the dataset you can split a dataset into three or two different sets. There is no fixed ratio, but here are some general guidelines:

Small Datasets (Less than 1,000 samples):

• Training Set: 70–80%
• Test Set: 20–30%

Medium Datasets (1,000 to 10,000 samples):

• Training Set: 60–80%
• Validation Set: 10–20%
• Test Set: 10–20%

Large Datasets (More than 10,000 samples):

• Training Set: 70–90%
• Validation Set: 5–15%
• Test Set: 5–15%

These are rough guidelines, and the actual ratios can vary depending on the specific characteristics of your data and problem.

Now you will use the training set to train your machine learning models. That means you will use the training set of data for learning. And you do not need the validation and test set now.

Model Performance Evaluation: After training a model you need to validate the model by evaluating it using various performance metrics. e.g. Accuracy, Precision, Recall, F1-score for classifier and MSE, RMSE, MAE for regressor model. You will use the validation set (If applicable) to validate the model in this step, and no use for the testing set.

Hyperparameter Tuning: Different model has its parameter settings. Initially, we train our data by using default model settings. Depending on the dataset and model type, performance may vary for different parameter settings. So, we need to test models on different parameter settings to get better results. You can use different parameter tuning techniques that automatically perform this task for you on the given set of parameters. e.g. RandomizedSearchCV, GridSearchCV, Optuna, etc. It is a time-consuming step.

Final Evaluation: After performing hyperparameter tuning you may notice some performance increase on your models (It may vary). Now finally using the testing set of data test your best model and check how well the model performs on the test set of data. If you get a satisfactory outcome then go to the next step otherwise analyze and study again from your data processing and try to reach your target result.

Model Deployment: Now you get your best and final model that gives a satisfactory prediction. If you want people to use your solution then you have to deploy your model. You can deploy your model via a Web app or Mobile app, whatever is convenient. The most common tools and technologies used for model deployment are Python Flask, Web tools and API, Android Studio, Flutter, etc.

Publication: If you want to go for a publication, you have to remember that your work should have some novelty. You have to state your unique contribution and your research outcome is better than other's work in every possible way.

These are the main steps for a machine learning project life cycle. It may vary on the problem type and dataset.

Hope this helps you to understand your project steps better. Thank you.