In this post, I will collect all the useful resources to do Machine Learning, enter the Kaggle world and get good predictions !! Data Preprocessing  : # Importing the libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd # Importing the dataset dataset = pd.read_csv( 'Data.csv' ) X = dataset.iloc[:, :- 1 ].values y = dataset.iloc[:, 3 ].values # Splitting the dataset into the Training set and Test set from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.2 , random_state= 0 ) # Feature Scaling from sklearn.preprocessing import StandardScaler sc_X = StandardScaler() X_train = sc_X.fit_transform(X_train) X_test = sc_X.transform(X_test) # Encoding categorical data from sklearn.preprocessing import LabelEncoder, OneHotEncoder labelencoder_X = LabelEncoder() X[:, 0 ] = labelencoder_X.fit_transform(X[:, 0 ]) onehotencoder = OneHotEncoder(categori...

Machine learning is the future, the more I read about it the more I get fascinated by this field. There are 2 YouTube channels I am currently following and I really encourage people who are interested in machine learning to follow them too, the two channels are  Two Minute Papers and  Siraj Raval , they always bring new subjects and they will make you interested in ML more than ever, even if you hate math x). So for a while I've been trying to learn more about ML but I really couldn't find time, I did some slow progress but there are a lot more I need to learn, this year (2018) advancing in ML gonna be one of my goals so I hope from now on I am gonna do more projects in ML and write more posts about it. In this post I am going to talk about the project I made the first semester, I didn't want to do a classic project so I talked to the prof and asked him to let me do something in ML and he liked the idea, as long as I use python (the subject course) he is Ok with it. ...