N Days_With_Machine_Learning (Part1 )

What is Machine learning ?

let’s Start With Data-Preprocessing

Numpy
scikit learn
sudo apt-get update
sudo apt-get -y install python-pip
sudo apt-get install python3-matplotlib
sudo pip3 install numpy
sudo pip3 install pandas
sudo pip3 install scipy
sudo pip3 install -U scikit-learn

Now let’s start coding

# Imporitng the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('Data.csv');
data.csv
X = dataset.iloc[:,:-1].values
Y = dataset.iloc[:,3].values
print(X)
print(Y)
from sklearn.preprocessing import Imputer 
imputer = Imputer(missing_values='NaN',strategy='mean',axis=0)
imputer = imputer.fit(X[:,1:3])
X[:,1:3]= imputer.transform(X[:,1:3])
print(X)
from sklearn import preprocessing
from sklearn.preprocessing import OneHotEncoder
le = preprocessing.LabelEncoder()
enc = OneHotEncoder(categorical_features=[0])
X[:,0]= le.fit_transform(X[:,0])
X = enc.fit_transform(X).toarray();
Y = le.fit_transform(Y)
print(X)
print(Y)
from sklearn.model_selection import train_test_split
X_Train ,X_Test , Y_Train,Y_Test= train_test_split(X,Y, test_size=0.2,random_state=0)
print(X_Train)
print(Y_Train)
print('**************testing data**********')
print(X_Test)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_Train)
X_Test =scaler.transform(X_Test)'''

Develop a passion for learning. If you do, you will never cease to grow.

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