Cheat Sheet.md

Installing Packages

!pip install pandas

!pip install seaborn

!pip install numpy

!pip install matplotlib

!pip install sklearn

!pip install warnings

Importing Packages

import pandas as pd

import seaborn as sns

import numpy as np

import matplotlib.pyplot as plt

import sklearn

import warnings

Reading Dataset

df = pd.read_csv("Dataset_Name.csv")

Exploring and Visualizing Data

df.head()

By default it returns the first 5 rows of dataset.

df.shape

It is used to know the dimensions(No.of rows ,No.of columns) of Dataset.

df.info()

This method is used to know the summary of DataFrame .The Information like Columnname ,Column Datatypes, Non-null values and Memory usage.

df.describe()

This method is used to generate descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.

df.isna().sum()

This method is used to detect missing values for an array-like object and count them for every column.

df["Column_Name"].value_counts()

It returns a Series containing counts of unique values

sns.barplot(x = None, y = None, data = None)

A bar plot represents an estimate of central tendency for a numeric variable with the height of each rectangle and provides some indication of the uncertainty around that estimate using error bars.

sns.distplot(a = None, bins = None)

It is used basically for univariant set of observations and visualizes it through a histogram.

sns.lineplot(x = None, y = None, data = None)

It draws a line plot with the possibility of several semantic groupings. The relationship between x and y can be shown for different subsets of the data using the hue, size, and style parameters

sns.jointplot(x = None, y = None, data = None)

It draws a plot of two variables with bivariate and univariate graphs.

sns.heatmap(data = None)

This gives a graphical representation of data using colors to visualize the value of the matrix.

Cleaning and Pre-processing Data

sklearn.utils.resample(*arrays, replace=True, n_samples=None, random_state=None)

This function resamples arrays or sparse matrices in a consistent way. The default strategy implements one step of the bootstrapping procedure.

DataFrame.iloc()

Purely integer-location based indexing for selection by position.

sklearn.impute.SimpleImputer(missing_values = np.nan, strategy = "most_frequent")

This replaces missing values using the most frequent value along each column.

sklearn.preprocessing.LabelEncoder()

It is a utility class to help normalize labels such that they contain only values between 0 and n_classes-1

sklearn.preprocessing.StandardScaler()

This helps in standardizing features by removing the mean and scaling to unit variance.

Modelling

sklearn.model_selection.train_test_split(*arrays, test_size=None, train_size=None, random_state=None)

This split arrays or matrices into random train and test subsets

sklearn.linear_model.LogisticRegression()

In statistics, the logistic model (or logit model) is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick. This can be extended to model several classes of events such as determining whether an image contains a cat, dog, lion, etc. Each object being detected in the image would be assigned a probability between 0 and 1, with a sum of one. Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model.

sklearn.tree.DecisionTreeClassifier()

Decision tree uses the tree representation to solve the problem in which each leaf node corresponds to a class label and attributes are represented on the internal node of the tree. We can represent any boolean function on discrete attributes using the decision tree.

sklearn.ensemble.RandomForestClassifier()

A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.

sklearn.lightgbm.LGBMClassifier()

Light GBM is a gradient boosting framework that uses tree based learning algorithm. Light GBM grows tree vertically while other algorithm grows trees horizontally meaning that Light GBM grows tree leaf-wise while other algorithm grows level-wise. It will choose the leaf with max delta loss to grow. When growing the same leaf, Leaf-wise algorithm can reduce more loss than a level-wise algorithm.

sklearn.naive_bayes.GaussianNB()

The naive Bayes classifier assumes all the features are independent to each other. Even if the features depend on each other or upon the existence of the other features. A Gaussian Naive Bayes algorithm is a special type of NB algorithm. It’s specifically used when the features have continuous values. It’s also assumed that all the features are following a gaussian distribution i.e, normal distribution.

sklearn.metrics.confusion_matrix(y_true, y_pred)

A confusion matrix is a table that is often used to describe the performance of a classification model (or "classifier") on a set of test data for which the true values are known.

Algorithms

S.No	Name	Defination
1	Logistic Regression	Logistic regression is used to describe data and to explain the relationship between one dependent binary variable.
2	Random Forest	A Random forest is a machine learning algorithm that develops large numbers of random decision trees analyzing sets of variables.
3	Decision Trees	A decision tree is a map of the possible outcomes of a series of related choices. It allows to weigh possible actions against one another based on their probabilities. This can can be used to map out an decision that predicts the best choice mathematically.
4	LightGBM	Light GBM is a fast, distributed, high-performance gradient boosting framework based on decision tree algorithm, used for ranking and classification.The tree is grows vertically.
5	Naive Bayes	Naive Bayes is a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.