# Autogon Engine (Studio)

- [Data Processing](/autogon-engine-studio/data-processing.md): This engine collects raw data and translates into usable information. It uses a single endpoint architecture, differentiated by function codes.
- [Data Input (DP\_1)](/autogon-engine-studio/data-processing/data-input-dp_1.md): Specify the data sources, this functionality can take database connection, CSV, JSON or ZIP files
- [Automated Data Processing (DP\_ADP)](/autogon-engine-studio/data-processing/automated-data-processing-dp_adp.md): This function automatically cleans and encodes supported data.
- [Missing Data (DP\_2)](/autogon-engine-studio/data-processing/missing-data-dp_2.md): This functionality handles missing data using various techniques. e.g mean, mode and more.
- [Data Encoding (DP\_3)](/autogon-engine-studio/data-processing/data-encoding-dp_3.md): This functionality converts data to a recognizable format through encoding. Supported techniques including, but are not limited to, one-hot, label and categorical encoding.
- [Data Split (DP\_4)](/autogon-engine-studio/data-processing/data-split-dp_4.md): This functionality splits data into two subsets: a training set and a test set. The training set is used to train a model, while the test set is used to evaluate its performance.
- [Feature Scaling (DP\_5)](/autogon-engine-studio/data-processing/feature-scaling-dp_5.md): This functionality normalizes the range of values for different features in the dataset
- [Drop Columns (DP\_6)](/autogon-engine-studio/data-processing/drop-columns-dp_6.md): This functionality drops specified multiple columns on the X and Y columns.
- [Time Stepper (DP\_7)](/autogon-engine-studio/data-processing/time-stepper-dp_7.md): This functionality enables you to transform your data into a time series format.
- [Parse Datetime (DP\_PDT)](/autogon-engine-studio/data-processing/parse-datetime-dp_pdt.md): In data analysis and various applications, datetime information is a crucial component. To leverage this information effectively, we rely on the process of datetime data parsing.
- [Reorder Columns (DP\_ROC)](/autogon-engine-studio/data-processing/reorder-columns-dp_roc.md): The process of reordering columns involves changing the sequence of columns to better suit the needs of analysis, visualization, or downstream processes.
- [Feature Sampling (DP\_FSP)](/autogon-engine-studio/data-processing/feature-sampling-dp_fsp.md): This functionality samples a dataset into X and Y features
- [Reshape Array (DP\_RSH)](/autogon-engine-studio/data-processing/reshape-array-dp_rsh.md): This function takes an input array of data and reshapes it into a time series format. The resulting time series data can be used for time-based analysis, modeling, and forecasting.
- [Column Astype (DP\_ASP)](/autogon-engine-studio/data-processing/column-astype-dp_asp.md): This function casts a column to a specified datatype
- [Show Duplicates (DP\_SDC)](/autogon-engine-studio/data-processing/show-duplicates-dp_sdc.md): This function shows duplicates
- [Drop Duplicates (DP\_DRD)](/autogon-engine-studio/data-processing/drop-duplicates-dp_drd.md): This function drops duplicated rows
- [Scalar to Ndarray (DP\_STN)](/autogon-engine-studio/data-processing/scalar-to-ndarray-dp_stn.md): This function "listifies" the scalar value
- [Image to Ndarray (DP\_ITN)](/autogon-engine-studio/data-processing/image-to-ndarray-dp_itn.md): This function creates an image array
- [Dataset Info (DP\_INF)](/autogon-engine-studio/data-processing/dataset-info-dp_inf.md): This function shows dataset info
- [Dataset Correlations (DP\_CRR)](/autogon-engine-studio/data-processing/dataset-correlations-dp_crr.md): This function shows dataset correlations
- [Dataset Description (DP\_DSC)](/autogon-engine-studio/data-processing/dataset-description-dp_dsc.md): This function shows dataset descriptions
- [Dataset Datatypes (DP\_DTY)](/autogon-engine-studio/data-processing/dataset-datatypes-dp_dty.md): This function shows dataset datatypes
- [Dataset Uniques (DP\_UNQ)](/autogon-engine-studio/data-processing/dataset-uniques-dp_unq.md): This function shows the number of unique data classes
- [Dataset Stats Counts (DP\_STC)](/autogon-engine-studio/data-processing/dataset-stats-counts-dp_stc.md): This function shows the number of unique data classes within a specific column
- [Principal Component Analysis (DP\_PCA)](/autogon-engine-studio/data-processing/principal-component-analysis-dp_pca.md): This function reduces the dimensionality using PCA
- [Text Vectorizer (DP\_VEC)](/autogon-engine-studio/data-processing/text-vectorizer-dp_vec.md): Transform textual data into numerical representations that are compatible with machine learning models, enabling efficient processing of text-based tasks.
- [Resampler (DP\_RES)](/autogon-engine-studio/data-processing/resampler-dp_res.md): Resample input datasets using specified resampling techniques.
- [Data Visualization](/autogon-engine-studio/data-visualization.md)
- [Scatter Plots (DP\_SCP)](/autogon-engine-studio/data-visualization/scatter-plots-dp_scp.md): This function creates scatter plots for pairs of columns in a given input dataset. Scatter plots are a useful visualization tool for examining the relationship between two variables.
- [Ordinary Plots (DP\_ORD)](/autogon-engine-studio/data-visualization/ordinary-plots-dp_ord.md): This function creates ordinary line plots for pairs of columns in a given input dataset.
- [Compare Scatter Plots (DP\_CSP)](/autogon-engine-studio/data-visualization/compare-scatter-plots-dp_csp.md): This function compares scatter plots for pairs of columns in a given input dataset
- [Pie Plots (DP\_PIE)](/autogon-engine-studio/data-visualization/pie-plots-dp_pie.md): This function creates pie plots for a given input dataset.
- [Heatmap Plots (DP\_HMP)](/autogon-engine-studio/data-visualization/heatmap-plots-dp_hmp.md): This function creates heatmap plots for a given input dataset.
- [Machine Learning](/autogon-engine-studio/machine-learning.md): This subset focuses on building systems that learn or improve performance based on the data they consume.
- [Simple Linear Regression (ML\_R\_1)](/autogon-engine-studio/machine-learning/simple-linear-regression-ml_r_1.md): This function models the relationship between two continuous variables. The objective is to predict the value of an output variable  based on the value of an input variable.
- [Multiple Linear Regression (ML\_R\_2)](/autogon-engine-studio/machine-learning/multiple-linear-regression-ml_r_2.md): This function models the relationship between more independent variables. The objective is to predict the value of an output variable  based on the value of input variables.
- [Polynomial Linear Regression (ML\_R\_3)](/autogon-engine-studio/machine-learning/polynomial-linear-regression-ml_r_3.md): This function uses the relationship between variables to find the best non-linear fit through the data points.
- [Support Vector Regression (ML\_R\_4)](/autogon-engine-studio/machine-learning/support-vector-regression-ml_r_4.md): This function can be used for solving both linear and non-linear problems.
- [Decision Tree Regression (ML\_R\_5)](/autogon-engine-studio/machine-learning/decision-tree-regression-ml_r_5.md): This function splits the data into smaller subsets while at the same time an associated decision rule is used to predict the target variable, built in the form of a tree structure.
- [Random Forest Regression (ML\_R\_6)](/autogon-engine-studio/machine-learning/random-forest-regression-ml_r_6.md): This function builds multiple decision trees and combines their outputs to make a final prediction.
- [Logistic Regression (ML\_CN\_1)](/autogon-engine-studio/machine-learning/logistic-regression-ml_cn_1.md): This function performs analysis on a dataset and returns the predicted binary outcome based on the input independent variables.
- [K-Nearest Neighbors - KNN (ML\_CN\_2)](/autogon-engine-studio/machine-learning/k-nearest-neighbors-knn-ml_cn_2.md): This function finds the K number of training examples closest (nearest neighbors) to the input data and then classifying the input data based on the majority class of its nearest neighbors.
- [Support Vector Machine (ML\_CN\_3)](/autogon-engine-studio/machine-learning/support-vector-machine-ml_cn_3.md): This functionality creates a decision boundary based on the support vectors, and classifies new input data based on which side of the boundary it falls on.
- [Kernel SVM (ML\_CN\_4)](/autogon-engine-studio/machine-learning/kernel-svm-ml_cn_4.md): This functionality uses a kernel function to map the input data to a higher-dimensional space, where a linear decision boundary is created based on the support vectors.
- [Naive Bayes (ML\_CN\_5)](/autogon-engine-studio/machine-learning/naive-bayes-ml_cn_5.md): This function uses the Bayes' theorem to calculate the probability of each class based on the frequency of the features in the training data, and classifies new input data based on highest probability
- [Decision Tree Classification (ML\_CN\_6)](/autogon-engine-studio/machine-learning/decision-tree-classification-ml_cn_6.md): This function finds the K number of training examples closest (nearest neighbors) to the input data and then classifying the input data based on the majority class of its nearest neighbors.
- [Random Forest Classification (ML\_CN\_7)](/autogon-engine-studio/machine-learning/random-forest-classification-ml_cn_7.md): This function combines multiple decision trees and aggregates their results to make predictions.
- [Hierarchical Clustering (ML\_CG\_1)](/autogon-engine-studio/machine-learning/hierarchical-clustering-ml_cg_1.md): Hierarchical Clustering groups similar data points into clusters by recursively merging the two closest clusters based on a distance metric.
- [K-Means Clustering (ML\_CG\_2)](/autogon-engine-studio/machine-learning/k-means-clustering-ml_cg_2.md): This function groups similar data points into K clusters by iteratively assigning each data point to the nearest center and updating the cluster centers based on the mean of the assigned data points.
- [XGBoost (MS\_XGBOOST)](/autogon-engine-studio/machine-learning/xgboost-ms_xgboost.md): This function is based on gradient boosting that iteratively trains weak models while optimizing a regularized objective function to reduce overfitting.
- [Grid Search (ML\_GRID)](/autogon-engine-studio/machine-learning/grid-search-ml_grid.md): This function exhaustively searches for the optimal combination of hyperparameter values for a machine learning model.
- [Shap Explain (ML\_SHAP)](/autogon-engine-studio/machine-learning/shap-explain-ml_shap.md): This function provides interpretable insights into machine learning model predictions by explaining the contribution of each feature to the output.
- [Isolation Forest (ML\_ISF)](/autogon-engine-studio/machine-learning/isolation-forest-ml_isf.md): This function isolates outliers by creating binary trees to efficiently separate normal data points from anomalies based on their low-dimensional representations.
- [(ML\_DBS)](/autogon-engine-studio/machine-learning/ml_dbs.md)
- [Automated Machine Learning](/autogon-engine-studio/automated-machine-learning.md)
- [AutoRegression (AUTO\_R\_1)](/autogon-engine-studio/automated-machine-learning/autoregression-auto_r_1.md): This function finds the K number of training examples closest (nearest neighbors) to the input data and then classifying the input data based on the majority class of its nearest neighbors.
- [AutoClassification (AUTO\_CN\_1)](/autogon-engine-studio/automated-machine-learning/autoclassification-auto_cn_1.md): This function finds the K number of training examples closest (nearest neighbors) to the input data and then classifying the input data based on the majority class of its nearest neighbors.
- [AutoRegression II (AUTO\_R\_2)](/autogon-engine-studio/automated-machine-learning/autoregression-ii-auto_r_2.md)
- [Deep Learning](/autogon-engine-studio/deep-learning.md)
- [Artificial Neural Network (DL\_ANN)](/autogon-engine-studio/deep-learning/artificial-neural-network-dl_ann.md): This function creates and uses a model consisting of layers of interconnected nodes (neurons) that process input data and produce output predictions.
- [Self Organizing Maps (DL\_SOM)](/autogon-engine-studio/deep-learning/self-organizing-maps-dl_som.md): This function creates self organizing maps used for data clustering
- [Restricted Boltzmann Machine (DL\_RBM)](/autogon-engine-studio/deep-learning/restricted-boltzmann-machine-dl_rbm.md): This function creates a Restricted Boltzmann Machine used for dimensionality reduction
- [Automated Deep Learning](/autogon-engine-studio/automated-deep-learning.md)
- [Auto Image Classification (A\_DL\_IMC)](/autogon-engine-studio/automated-deep-learning/auto-image-classification-a_dl_imc.md): This function creates an Automated Image classifying model
- [Auto Image Regression (A\_DL\_IMR)](/autogon-engine-studio/automated-deep-learning/auto-image-regression-a_dl_imr.md): This function creates an Automated Image regression model
- [Auto Text Classification (A\_DL\_TXC)](/autogon-engine-studio/automated-deep-learning/auto-text-classification-a_dl_txc.md): This function creates an Automated Text classifying model
- [Auto Text Regression (A\_DL\_TXR)](/autogon-engine-studio/automated-deep-learning/auto-text-regression-a_dl_txr.md): This function creates an Automated Text regression model
- [Auto Structured Data Classification (A\_DL\_SDC)](/autogon-engine-studio/automated-deep-learning/auto-structured-data-classification-a_dl_sdc.md): This function creates an Automated Structured Data classifying model
- [Auto Structured Data Regression (A\_DL\_SDR)](/autogon-engine-studio/automated-deep-learning/auto-structured-data-regression-a_dl_sdr.md): This function creates an Automated Structured Data regression model
- [General AutoDL Blocks (A\_DL\_ALL)](/autogon-engine-studio/automated-deep-learning/general-autodl-blocks-a_dl_all.md): This function loads and uses pre-trained AutoDL models to perform actions such as model evaluation and value prediction.