espin086/autolearn

AutoLearn is a powerful tool for data scientists that automates the process of exploratory data analysis (EDA) and machine learning model training. By utilizing PyCaret and Pandas Data Profiler, AutoLearn streamlines the data analysis and model building process, providing several benefits for data scientists including speed and efficiency.

Hi 👋, I'm JJ

Data Scientist and Machine Learning Engineer

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My Portfolio of Projects

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💼 GPT-JobHunter: Text Analysis, APIs, SQL, User Input, Machine Learning, Generative AI

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Analyzes job postings and provides personalized recommendations to job seekers for improving their resumes.

Key Components:

• Generative AI API calls for embeddings
• Text similarityh algorithm
• Jobs API Call for job postings
• Class to perform data transformations
• SQLite functions for database operations
• Unit tests to ensure code quality
• Make file to install, test, and run application
• Code for Streamlit user interface

💰 NewsWaveMetrics: APIs, SQL, Python, Text Analysis, Time Series Analysis, etc.

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NewsWageMetrics is a powerful tool for analyzing news sentiment, allowing users to correlate these stories with stock market price data.

Key Components:

• Anomaly Detection Algorithm using Time Series Algorithms
• Data Transformation using Classes
• Extract Economic Data via API
• Local File Handler Class

🧠 AutoLearn: Automation, Machine Learning, Data Visualization, Model Training/Tuning/Inference

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AutoLearn is a powerful tool for data scientists that automates the process of exploratory data analysis (EDA) and machine learning model training.

Key Components:

• Streamlit Application

💥 EmoTrack: AWS, Computer Vision, Real-Time Processing, SQL

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A real-time emotion detection and tracking application using webcam input. Analyze and visualize your emotional trends over time with interactive charts.

Key Components:

• Call to AWS Facial Analysis API
• Webcam Access from Streamlit UI

Languages and Tools:

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My Github Activity:

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autolearn's Issues

Change name of Model Saved Either regression.pkl, clustering.pkl, or classification.pkl, when doing prediction allow use to select one of these 3 saved models.

Create a Streamlit Menu for Uploading CSV and Getting Predictions from Best Model

I would like to request the implementation of a new menu in our Streamlit application. This menu should allow users to upload a CSV file and obtain predictions from the best model saved in a .pkl file. The application is built using PyCaret.

Requirements:

1. The menu should have an option to upload a CSV file.
2. Once the file is uploaded, the application should load the best model saved in a .pkl file.
3. The loaded model should be used to generate predictions for the uploaded CSV file.
4. The predictions should be displayed to the user.

Add Classification to AutoML Model

Issue Description

Problem Statement

I want to enhance my Streamlit app that uses PyCaret for automatic regression analysis. Currently, the app only supports regression analysis, but I want to add the functionality to choose between regression and classification.

Steps to Reproduce

1. Open the Streamlit app.
2. Select the target variable.
3. Choose whether to run regression or classification analysis.
4. Verify that the selected analysis is executed correctly.

Expected Behavior

The app should allow the user to select the target variable and choose between regression and classification analysis. It should then execute the selected analysis accordingly.

Add Clustering Model to AutoLearn from PyCaret

Just like you did with classification, but add now a clustering algorithm, you can find info here: https://towardsdatascience.com/introduction-to-clustering-in-python-with-pycaret-5d869b9714a3

I am not sure if PyCaret we need it to run a bunch of clustering algorithms and display statistics as well

Dynamically Select Which Model to Save - Current Defaults to Best Model