Evaluate Machine Learning Models with Yellowbrick
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Updated
Jun 28, 2020 - Jupyter Notebook
Evaluate Machine Learning Models with Yellowbrick
Objective: The department wants to build a model that will help them identify the potential customers who have a higher probability of purchasing the loan. This will increase the success ratio while at the same time reducing the cost of the campaign.
Student performance analysis and prediction using datasets has become an essential component of modern education systems. With the increasing availability of data on student, schools and universities are using advanced analytics and machine learning algorithms to gain insights into student performance and predict future outcomes.
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Building a model to classify student grade based on various factors and conducting exploratory data analysis
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Module 12 - Using the imblearn , I'll use a logistic regression model to compare 2 versions of a dataset. First, I’ll use the original data. Next, I’ll resample the data by using RandomOverSampler. In both cases, I’ll get the count of the target classes, train a logistic regression classifier, calculate the balanced accuracy score, generate a con
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Iris Data : Classification / Pattern Recognition, Predict the Class of Flower based on Available Attributes.
Given a set of text movie reviews that have been labeled positive or negative, a machine learning classification model should be trained and tested for predicting the nature of future movie reviews.
Create a Logistic Regression model to predict the credit risk of customers to a lending company. Use historical data and train the model before making predictions of test data. Lastly write an analysis report on the models created.
This repository features code for a fraud detection model achieving 100% accuracy in identifying fraudulent credit card transactions. Utilizing transaction data from Jan 2019 to Dec 2020, the model employs RandomForestClassifier, assessing features like credit card numbers, transaction amounts, and merchant information.
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NLP and Classification techniques to analyze the sentiment in tweets
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