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Predicting the health of power transformers using machine learning is crucial for ensuring the reliable operation of electrical grids. Will be analyzing historical data on transformer performance found here on Kaggle, machine learning algorithms can identify patterns and anomalies that indicate potential failures or deteriorating conditions. This early detection enables proactive maintenance and replacement, minimizing downtime, reducing costs, and preventing catastrophic fa

Thank you to Ula Yousef on linkedin who designed the entire workbook herself and prepared this post, basically everything but the follow-along video. Thank you so much, the beautiful plotting and wonderful clustering for a beginner Python project. In this python guided project, you will learn how to build your first k-means clustering project step by step using these libraries: 1_ Pandas : which has multiple functions to perform analysis tasks. 2_ Numpy : which is use

In this free-to-use follow-along Python Sklearn guided project in Python. In this beginner simple ML project, we will use ensemble methods to predict the price of smartwatches based on their characteristics. Will will focus this project on ensemble methods in a head-to-head competition. We will use Gradient Boosting Regressor , Random Forest Regressor , Bagging Regressor , and Voting Regressor to find out Sklearn's best ensemble method. In a normal project, it is always a

In this Python guided project, you can follow along and build your first Simple Random Forest machine-learning model. In this Python project, we will use RandomForestClassifier from Sklearn. In is a good idea when doing an ML workflow to have a simple base model that your more robust model will try and beat. In this situation, Logistic Regression acts like our base model and Random Forest acts like our robust complex model. Special Thanks to John David who designed the e

In the second part of the Python Guided Machine Learning Project, the data scientist picks up where the data analyst left off. We use the data analyst's sights to guide the data scientist preprocessing strategy for machine learning.

In this Python Machine Learning Guided Project, we will explore how to manage a common problem for house price predictions. Too many features.

In this Python ML project, we will explore an individual DecisionTree and learn to use pre-pruning and post-pruning techniques. Prepruning techniques are generally easier to use and involve setting hyperparameters that limit the growth of our decision trees. Post-pruning techniques are a little harder to work with but very important to understand how to use the when applying them to a random forest of decision trees. Let's practice with pre and post-pruning techniques in

Join me on the Python Machine Learning Project where we predict the price of cars in Poland. This supervised learning problem in Python is a regression problem. In this project with will focus on linear regression techniques including Lasso , Ridge , and ARDRegression models. This is a great project for those looking to explore linear regression techniques.

Join us on this Python Machine Learning Guided project. In this Python Regression project, we will be predicting the MPG of classic cars. This is a slight variation on a common predictive workflow. Use ensemble methods like RandomForestRegressor , AdaBoostRegressor , and GradientBoostingRegressor in the supervised machine learning project. This is a great beginner Python project to practice machine learning with ensemble methods.

In this supervised learning Python ML guided project we will be predicting whether or not someone was approved for a credit card. In this Python project, we will only be using the LogisticRegession() in Sklearn for this supervised classification problem. Although this project would work with RandomForestClassifier() or GradientBoostingClassifier() we will be focusing on error analysis in this classification problem. Understanding precision versus recall and why we would

In the Python Project, we will use Pandas and Seaborn to perform our exploratory data analysis in en effort to understand how our features impact our target. A big aspect of this is paying close attention to the distributions of our features use in our supervised machine learning project.

Follow along with this Python ML-guided project. In this beginner Python project, we build a classification model in Sklearn to predict if or not a person has early-stage diabetes in this medical prediction with machine learning.

This intro project is great for those new to Sklearn . Learn how to set up an ML workflow. From exploratory data analysis to predictions. Use pandas and seaborn in Python to perform your data analysis. Then use Sklearn to do the train test split and make your predictions. Here we also look at the R-squared score to evaluate our model.

Data Analysis Guided Project in Python. Use your data analysis skills and analyze the spaceship titanic data from Kaggle Competition as...