Wine Quality Prediction with Machine Learning (part 1) — Data preprocessing and Feature Correlation

Hello Everyone! My name is Andrew Fung, in this video, I will be showing you how to create a Wine Quality Prediction Project as a part of the Machine Learning series. I will be walking you through different processes and steps to take to slowly train and enhance the performance of your models, such as data preprocessing, features selection, feature correlation , classification and hyper parameters tuning. Hope you enjoy this tutorial ;) #python​ #correlation #machinelearning​ #dataprocessing Installation and Setup! Installing Jupyter Notebook: https://jupyter.readthedocs.io/en/lat​... Sklearn linear regression doc: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html Check out my Github! https://github.com/Andrew-FungKinHo​ Timestamps 0:00​ | Introduction 0:48​ | Dataset 2:08​ | Data processing 4:57 | Correlation heat map 6:48​ | Out tro Full code: ——————————————————————————————— import pandas as pd import numpy as np import seaborn as sns # check whether the libraries are imported properly and show their versions print(pd.__version__) print(np.__version__) print(sns.__version__) # import the training dataset as a pandas dataframe df_train = pd.read_csv('wine_data/train.csv', delimiter=',') df_train.info() # replace NaN values with median df_train = df_train.apply(lambda x: x.fillna(x.median()),axis=0) # drop duplicated rows df_train = df_train.drop_duplicates() # draw a heatmap to visualize the correlation between every two of the 10 features df_corr = df_train.iloc[:,:10].corr() sns.heatmap(df_corr,cmap="RdBu") ——————————————————————————————— Feel free to drop a like and comment if you enjoy and video and let me know if you want me to do other types of programming videos ;) !!!