Data visualization and one hot encoding of Kaggle dataset. Model trained with random forest classifier
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Updated
May 8, 2020 - Jupyter Notebook
Data visualization and one hot encoding of Kaggle dataset. Model trained with random forest classifier
Flight fare perdicting model
Repositório para o primeiro challenge de data science na Alura, mostrando o pipeline de tratamento, análise gráfica e construção de modelos de machine learning.
Predictive analysis, with feature engineering, and machine learning (ML) algorithms, such as linear regression, applied to predict the final sale price of homes in Ames, IA from 2006-2010.
Unofficial but extremely useful Label and One Hot encoders.
Using a dataset provided by Airbnb, analysis and predictions will be made to understand what effects the total price of an Airbnb
Implemented content-based and collaborative filtering recommendation systems using Python
Using Regression algorithms for predict houses prices for a dataset
Feature Engineering
The goal of this project is to use Natural Language Processing (NLP) techniques to analyze hotel reviews and gain insights into customer opinions and experiences to classify hotel reviews.
This Github repository contains cross selling of health insurance customers on vehicle insurance product. We have to predict whether a customer would be interested in Vehicle Insurance or not by building a ML model. Exploring Insights/Inferences by performing EDA on the given project data. Finding the high accuracy
This repository covers my code using regression models to predict if a customer would be exiting a bank or not. It also capture the use classification models to classify if a customer has left the bank or not (binary classification).
This project showcases an End-to-End ML Project on the Auto MPG dataset, where the main objective is to predict miles per gallon (MPG) of a car, based on its given attributes. This prediction would greatly help the designer engineers to understand what would be the expected MPG of their designed car and help them make suitable changes to deliver…
💚 A heart disease classifier using 4 SVM kernels and decision trees, with PCA, ROC, pruning, grid search cv, confusion matrix, and more
Use PySpark to predict the success of a terrorist attack using different machine learning approaches
This repository will help to address complex topics of machine learning such as clustering techniques, PCA and cross validation
University Project: building a random forest to predict loan defaults. This involves data processing, standardization, optimization, performance metrics, and model analysis.
Book price dataset analysis and modeling
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