✏️ Implementing Machine Learning Algorithms
Numpy (short for Numerical Python) is a library used for computing scientific/mathematical data.
It gives the ability to create multidimensional array objects and perform faster mathematical operations.
import numpy as np
Matplotlib is a library of python.
Using Matplotlib you can plot graphs, histogram, bar plot and other sort of things.
It is the Python's equivalent of MATLAB
matplotlib.pyplot is a collection of command style functions that make matplotlib work like MATLAB.
import matplotlib.pyplot as plt
Pandas (Python Data Analysis Library) is a library for data manipulation and analysis.
The Pandas module is used for working with tabular data
import pandas as pd
SimpleImputer class provides basic strategies for imputing missing values.
import numpy as np
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values=np.nan,strategy='mean')
imputer = imputer.fit(X[:, :])
X[:, :] = imputer.transform(X[:, :])
LabelEncoder is a way to encode class levels.
Encode target labels with value between 0 and n_classes-1.
It is used to convert categorical data, or text data, into numbers,
from sklearn.preprocessing import LabelEncoder
labelencoder_X = LabelEncoder()
X[:, 0] = labelencoder_X.fit_transform(X[:, 0]) # first column
After implementing the label encoding, our model might think that a column has data with some kind of order or hierarchy, even when we don't have it. To overcome this ‘OneHotEncode’ is used.
OneHotEncode represents the categorical variables as binary vectors.
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
ct = ColumnTransformer(
[('one_hot_encoder', OneHotEncoder(), [0])], # first column
remainder='passthrough'
)
X = np.array(ct.fit_transform(X), dtype=np.float)
Or you can use the OneHotEncoder directly
from sklearn.preprocessing import OneHotEncoder
transformer = ColumnTransformer(
transformers=[
("OneHotEncoder", # Just a name
OneHotEncoder(), # The transformer class
[3] # The column(s) to be applied on.
)
],
remainder='passthrough' # donot apply anything to the remaining columns
)
X = transformer.fit_transform(X.tolist())
Used for splitting the dataset into the training set and the testing set.
train_test_split divide the datasets into two parts for testing and training .
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)
Feature Scaling is a technique used at the time of Data Pre Processing to normalise the independent variables or features of data in a particular range.
Standardisation:
x_stand = ( x - mean(x) ) / ( standard devation(x) )
Normalisation:
x_norm = ( x - min(x) ) / ( max(x) - min(x) )
StandardScaler normalises data features in such a way that it has mean as 0 and standard deviation as 1.
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
A scatter plot of y vs x
Plot y versus x as lines or markers
plt.scatter(X, Y, color = 'red', label = 'Scatter Plot')
plt.plot(X, Y, color = 'blue', label = 'Line Plot')
plt.title('Title of the Plot')
plt.xlabel('Xlabel plot')
plt.ylabel('Ylabel plot')
plt.legend()
plt.show()
1️⃣ Select a significance level to stay in the model (ie: SL = 0.05)
2️⃣ Fit the full model with all possible predictors
3️⃣ Consider the predictor with the highest P-value. If P > SL, go to step 4 otherwise go to FINISH
4️⃣. Remove the predictor
5️⃣ Fit model without the predictor
iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position.
# Single selections using iloc and DataFrame
# Rows:
data.iloc[0] # first row of data frame
data.iloc[1] # second row of data frame
data.iloc[-1] # last row of data frame
# Columns:
data.iloc[:,0] # first column of data frame
data.iloc[:,1] # second column of data frame
data.iloc[:,-1] # last column of data frame
# Multiple row and column selections using iloc and DataFrame
data.iloc[0:5] # first five rows of dataframe
data.iloc[:, 0:2] # first two columns of data frame with all rows
data.iloc[[0,3,6,24], [0,5,6]] # 1st, 4th, 7th, 25th row + 1st 6th 7th columns
data.iloc[0:5, 5:8] # first 5 rows and 5th, 6th, 7th columns of data frame