In this blog, we will guide you on how to build an end-to-end Machine Learning project step by step guide. So let's get started.
Machine Learning Model Training
Here, I will take a regression problem statement, so first, we import basic libraries of Python like Numpy, Pandas, Matplotlib, and Seaborn. Load the .csv file of the dataset on Jupyter Notebook. We describe the dataset with the help of .describe() function.
The next step comes to visualization. You can use both Seaborn and Matplotlib libraries. You can use plots like Catplot, bar graph, or histogram to compare one variable to the other variable.
After the description of the dataset, we remove the missing values. Then, we use count function to check the values of categorical variables. After that we have to apply one hot encoding or replace it with a dictionary keys.
The next process comes with feature selection. Here, we focussed on important independent features that can affect the dependent feature.
After that, we create X and y variables. Here X variable, we put all the independent variables and in the y variable we put our target variable.
The next step is to select important independent features, so here we can use the Extra Tree regressor that helps to identify which features are important.
After testing the algorithms, you will find the accuracy of the ML algorithm in comparison with other algorithms. Whichever has the highest accuracy, you choose that algorithm and fit the X_train and y_train datasets and predict it with the X_test dataset.
For model training, you can choose different Machine Learning Algorithms according to the use case. In our case we have a regression problem statement, then we choose different regression algorithms and make a function that takes all the regression algorithms and make a function that takes all the regression algorithms and make another function of accuracy to find accuracy metrics and gives final accuracy.
Whichever has the highest accuracy score, then we choose that algorithm and train it.
The next step is to check whether our dataset is imbalanced or not. To do that we use KFold, and cross_val_score with different parameters and compare them with different splits and we find the mean of all the split scores to get the final score.
We can use Hyperparameter tunning to improve the accuracy of our model, Here you can use Randomized Search CV by defining parameters and getting the best parameters and scores.
We have to save our final model with the help of a pickle module, So you have to import pickle, and finally, we have completed and saved our model.
So, these are some basic model training of regression problem statements.
Make Front-end Applications with the help of Flask
If you want to make a front-end application with integration with Machine Learning Model, Then we can use the Flask framework. It is a python Python-based web framework for building front-end applications.
To use that, we have to install it in our environment by simply typing pip install flask. According to the independent features in our dataset, we can make an HTML page with a predict button. To style the HTML page we can apply CSS to make styling our HTML page.
Note :- You have to install all the dependencies of your machine learning application into your flask app.
Deployment of Flask Application
For the deployment, you can use Google Cloud, Amazon AWS, and Microsoft Azure, are the most used cloud platforms.