Machine Learning Mastery : Build Real World Auto ML projects

Build a heart attack risk predictor using auto machine learning with the Evil library, using the dataset and tables to automate model building and evaluation.

Import libraries such as pandas and numpy, load heart attack risk data in Google Colab, mount drive, and read a CSV to begin data analysis, feature engineering, and model building.

Analyze the heart disease dataset by exploring age, sex, chest pain, resting blood pressure, and cholesterol with seaborn heatmaps and plots. Learn data quality and correlations for univariate and bivariate analyses.

Explore data pre-processing with standard scaler, then compare logistic regression, decision tree, random forest, KNN, and SVM using a 70/30 train/test split and AutoML ensemble techniques.

Compare AdaBoost with logistic regression, KNN, and SVM, use grid search CV to tune hyperparameters, and reveal logistic regression as top performer with about 85% accuracy and a confusion matrix.

Explore auto machine learning with evil ml to auto select and evaluate pipelines for binary classification, then choose the best random forest imputer pipeline and save it for predictions.

Kick off a credit card fraud detection project with PyCaret's auto ML to identify anomalous transactions, compare outlier methods, and deploy a business-ready model.

Build a credit card fraud detection model using PyCaret, performing PCA-based dimensionality reduction, data analysis, feature engineering, and automated ML with Python libraries and outlier detection.

Analyze a large dataset to identify normal and fraud transactions, assess imbalanced classes, visualize distributions with bar plots and histograms, and prepare features for AutoML model building.

Split the data 70/30 to validate anomaly detection with isolation forest and one-class SVM, mapping predictions to 0/1 and achieving 99.7% accuracy.

Leverage automated machine learning with PyCaret to compare models, select the best performing classifier, tune hyperparameters, and generate predictions, including anomaly detection, from your data.

Predict flight fares using machine learning and AutoML with an airline dataset, then explore data analysis, feature engineering, preprocessing, and autosklearn for model selection.

Import libraries for data analysis and visualization, mount drive, and merge train and test datasets to examine features such as airline, date of journey, route, times, source, destination, and price.

Explore how airline features relate to price using seaborn box plots and cat plots, and prepare for feature engineering by converting categorical variables to numeric with label encoding.

Transform diverse data types into numeric features in feature engineering 1, split dates into date, month, year, extract arrival time, handle stops, and prepare data for model interpretation.

Split arrival and departure times into hour and minute features, split routes into route columns, handle nulls, drop unused columns, and apply encoding to prepare features for real-world Auto ML.

Explore feature selection using lasso and select from model to identify important predictors, split data into train and test sets, and visualize feature importance with an extra tree regressor.

Build a random forest regressor, fit and evaluate with training and test scores; tune hyperparameters via randomized search or grid search, preparing for auto machine learning with auto sklearn.

Explore model building with autosklearn in automated machine learning, selecting models and tuning hyperparameters for regression tasks, loading pre-processed data, fitting models, and evaluating with a leaderboard.

Forecast petrol prices using machine learning and automated machine learning with time series models like lstm and arima. Build predictive models with autokeras and perform data preprocessing.

Explore petrol price forecasting with TensorFlow and Autokeras, building LSTM and ARIMA models, importing data via Google Colab, and performing data analysis, pre-processing, and predictions.

Learn how to analyze time series data, handle null values, drop outliers, scale with minmax scaler, and split data by date into 65% training and 35% testing for forecasting models.

Learn to convert data into independent and dependent variables using a time step window, create x and y datasets, and reshape the data for Lstm modeling.

Build a stacked LSTM model for time series prediction, reshape data to three dimensions, train with 100 epochs using mean squared error and Adam, and compare with ARIMA.

Learn time series forecasting with Arima and Auto Keras AutoML, comparing Arima forecasting with the structured data regressor and evaluating results via mean squared error.

Predict bank customer churn using machine learning and auto machine learning, identify churn-driving factors, and estimate churn probability with neural networks and H2O AutoML.

Analyze bank customer data to identify factors driving churn and visualize them. Build a churn prediction model with H2O AutoML after data preprocessing and feature engineering.

Perform data analysis to understand customer churn, visualize with a pie chart, and explore factors via count plots and box plots for geography, gender, credit card, tenure, age, and salary.

Learn feature engineering to boost accuracy in real-world auto ml projects by creating features like salary balance ratio, applying one-hot and label encoding, and scaling data for train-test preparation.

Build and evaluate an artificial neural network with a sequential model in Keras, using ReLU and sigmoid activations to predict churn, trained for 100 epochs.

Discover H2O AutoML, an open source platform that automates model building, hyperparameter tuning, and evaluation from GBMs and deep learning to stacked ensembles, using train-test splits and a leaderboard.

Develop end-to-end air quality index predictor for Bangalore city using machine learning models like random forest and linear regression, then automate with teapot and deploy via flask api on heroku.

Import libraries in Google Colab, mount drive, and load the Bangalore air quality dataset; perform data analysis, feature engineering, and build with TPOT AutoML to predict AQI.

Analyze air quality index data by checking nulls, data types, and distributions, then visualize relationships with distribution plots, pair plots, and a correlation heatmap.

Check normality with q-q plots, scale and standardize features, then identify important features with an extra trees regressor, and plan a 70/30 train-test split.

Explore model building with linear regression, XGBoost regression, and random forest regression, applying hyper tuning, cross-validation, and evaluation metrics like mean squared error and R-squared to predict air quality index.

Compare random forest regression with XGBoost and linear regression, tune hyperparameters with random search CV, and deploy the winning model via pickle.

learn to build and evaluate models with auto ml using the tpot library, featuring genetic programming, cross-validation, and retrieving the best pipeline for deployment via a flask api.

Learn to deploy a machine learning model with a Flask API, using a simple app.py, pickle-loaded model, templates, and deploying to Heroku via GitHub.

Build an end-to-end rain prediction app using machine learning and auto ml with PyCaret, deploying via a Flask API on Heroku.

Build a rain tomorrow prediction app using auto machine learning, featuring data analysis, feature engineering, one-hot and label encoding, scaling, model comparison, and deployment with PyCaret, Flask API, and Heroku.

Analyze a large weather dataset to identify unique values and classify features as numerical, discrete, continuous, or categorical, then plan random sample and mean imputation for missing values.

Impute missing values using random sample imputation for the most affected variables, then apply mean imputation to numerical features. Analyze correlations with a heatmap and review distributions.

Apply feature engineering with one-hot encoding and label encoding for categorical variables, map location and date features, handle missing values, and prepare data with scaling and train-test split for modeling.

Compare random forest classifier, Gaussian Naive Bayes classifier, Naive Bayes k nearest classifier, and Xgb gradient boosting; evaluate accuracy and ROC AUC, then pickle the best model for pycaret ML.

Explore automated machine learning with PyCaret to go from data preparation to deploying a model in minutes, including low-code classification, model comparison, tuning, and Flask API deployment.

Deploy a machine learning model with a Flask API, loading a pickle file, rendering input templates, and predicting rain from user features before deploying on Heroku.

Deploy your machine learning model on Heroku by creating a GitHub repository, uploading the proc file, app, requirements, and templates, then connect to Heroku to deploy the Flask API.

Predict pizza prices using automated machine learning with data analysis, feature engineering (one hot encoding, label encoding, standardization), and model building with the Evil AutoML library.

Explore how to import libraries and datasets in a colab workflow, prep pizza price data including diameter and toppings, and apply feature engineering and AutoML for modeling.

analyze and visualize the dataset through data analysis, cleaning steps, type conversion, and feature engineering, using pie charts and histograms to reveal price, diameter, toppings, and extras.

Learn feature engineering by converting categorical data with one hot encoding and label encoding, creating dummy variables, and preparing train-test splits for model readiness.

Build real-world auto ML projects by constructing models with XGBoost regressor and support vector regression, tuning parameters with grid and randomized search, and comparing linear regression performance.

Leads learners through automating model building with auto ML using the Evil ML library; it demonstrates selecting, evaluating, and deploying best pipelines for regression, classification, and time series tasks.

Predict the final IPL match score using auto machine learning with the teapot library, leveraging data analysis, feature engineering, and hyperparameter tuning to build robust models.

Master IPL score prediction by using machine learning and automated machine learning with the TPU, covering data import, cleaning, analysis, preprocessing, model building, and comparing models in Google Colab.

Analyze and clean the IPL dataset by dropping nonessential columns, selecting consistent teams, and removing first five overs. Convert date to datetime and prepare features for AutoML modeling.

Apply one-hot encoding to categorical variables and consider label encoding as an alternative. Split the data by year, dropping the total column to form X and y.

Explore building and evaluating machine learning models using lasso and random forest regressors, with grid search and randomized search hyperparameter tuning, and assess predictions with mean squared error.

Master automated machine learning with teapot auto ml to optimize regression pipelines via genetic programming, including feature engineering, during ten splits of CV using negative mean absolute error.

Build a regression model using the TPOT AutoML library, identify the best extra tree regressor pipeline, and evaluate predictions with negative mean absolute error and R-squared.