End-To-End Data Science And Machine Learning Project

Wine quality prediction

What you'll learn
End-to-end pipeline of a data science project
How to conduct data cleaning and exploratory data analysis
How to train and compare different ML models
How to boost and increase the performance of your models
Requirements
You need basic knowledge of Python and Machine Learning
Description
Welcome to the course wine quality prediction! In this course you will learn how to work with data from end-to-end and create a machine learning model that predicts the quality of wines.This data set contains records related to red and white variants of the Portuguese Vinho Verde wine. It contains information from 1599 red wine samples and 4898 white wine samples. Input variables in the data set consist of the type of wine (either red or white wine) and metrics from objective tests (e.g. acidity levels, PH values, ABV, etc.).It is super important to notice that you will need python knowledge to be able to understand this course. You are going to develop everything using Google Colab, so there is no need to download Python or Anaconda. You also need basic knowledge of Machine Learning and data science, but don't worry we will cover the theory and the practical needs to understand how each of the models that we are going to use work.In our case, we will work with a classification problem (a set from the supervised learning algorithms). That means that we will use the quality as the target variable and the other variables as the inputs. In this sense, we will some examples to train our model and predict the quality of other wines.You will learn to work with Decision Trees, Logistic Regression, how to use LazyPredict and how to tune the hyperparameters using Grid Search.

Overview

Section 1: Getting started

Lecture 1 Welcome

Lecture 2 Dataset information

Lecture 3 Dataset features

Lecture 4 Dataset download

Section 2: Data cleaning & Exploratory data analysis

Lecture 5 Data Cleaning

Lecture 6 Exploratory data analysis

Section 3: Modeling

Lecture 7 Outliers and IQR

Lecture 8 Dealing with outliers

Lecture 9 Theory behind the models

Lecture 10 Logistic Regression - Theory

Lecture 11 Logistic Regression

Lecture 12 Cross validation

Lecture 13 K-Nearest Neighbors - Theory

Lecture 14 Decision Tree - Theory

Lecture 15 Training other models

Lecture 16 Random Forest - Theory

Lecture 17 Random Forest

Lecture 18 Grid Search

Lecture 19 Result - How to create the barplot

Lecture 20 Final notebook

Beginner Python developers curious about data science and machine learning