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1. This code is a compilation of utilit... This code is a compilation of utilities for machine learning model evaluation, data analysis reporting, and visualization in Python. It includes functions for inspecting pandas DataFrames, generating evaluation reports for classification and regression models in PDF format, and saving/loading models with joblib. Additionally, it offers tools for dataset normalization, performance metrics visualization, and model comparison through plots. The script leverages popular libraries such as pandas, sklearn, matplotlib, seaborn, and FPDF, covering aspects from data preprocessing to post-training model evaluation.

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   import pandas as pd

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   from fpdf import FPDF

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   from sklearn.metrics import (accuracy_score, classification_report, 

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                                roc_auc_score, f1_score, confusion_matrix, 

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                                precision_recall_curve, auc, roc_curve)

2. CartPolemodel CartPolemodel Public

   This project is a practical exercise to implement and test a reinforcement learning agent using the CartPole environment from OpenAI Gym. The goal is to develop an agent capable of maintaining a po…

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3. ClusteringBankClients ClusteringBankClients Public

   This repository contains a practical exercise focused on clustering techniques, designed to train and enhance skills in data analysis and machine learning.

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4. DenguePredictionModels DenguePredictionModels Public

   This repository hosts a collection of ML exercises focused on predicting dengue fever outbreaks. It serves as a practical application for practicing and refining skills in predictive modeling and h…

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5. EarthquakeDamageModels EarthquakeDamageModels Public

   Practical exercise using the "Richter's Predictor: Modeling Earthquake Damage" competition on DrivenData

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6. PyClips PyClips Public

   PyClips example

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